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![On Equivalence and Rewriting of XPath Queries
Using Views under DTD Constraints
Pantelis Aravogliadis and Vasilis Vassalos
Department of Informatics, Athens University of Economics and Business,
Athens, Greece
Abstract. It has long been recognized that query rewriting techniques
are important tools for query optimization and semantic caching and are
at the heart of data integration systems. In particular, the problem of
rewriting queries using view definitions has received a lot of attention in
these contexts. At the same time, the XPath language has become very
popular for processing XML data, and there is much recent progress
in semantic XPath optimization problems, such as XPath containment,
and, more recently, XPath rewriting using views. In this paper we address
the open problems of finding equivalent query rewritings using views for
XPath queries and views that include the child, predicate and wildcard
features (i.e., they are in XP(/, [], ∗)) under DTD constraints. In the
process, we also develop novel containment tests for queries in XP(/, [], ∗)
under DTD constraints.
Keywords: XML data, XPath processing, query rewriting using views.
1 Introduction
XML has become a widely used standard for data representation and exchange
over the Internet. To address the increasing need to query and manipulate XML
data, the W3C has proposed the XML query language XQuery [7]. XPath [6] is
the XQuery subset for navigating XML documents, and is designed to be embed-
ded in a host language such as XQuery. XPath expressions often define compli-
cated navigation, resulting in expensive query processing. Numerous techniques
to speed up evaluation of XPath queries have been developed (e.g. [8]). Rewriting
queries using materialized views is known to provide a powerful tool for query
optimization, semantic caching, and data integration, for both relational and
semistructured data (e.g, see [12,19]), and consequently the rewriting challenge
for XPath queries and views has started receiving more attention [1,3,14,23,26].
The rewriting query using views problem is traditionally formulated in two
different ways. In the equivalent rewriting formulation, the problem is, given a
query q and a view v, to find a rewriting of q using v that is equivalent to q. This
formulation is motivated by classical query optimization and solutions to the
problem address the problem of speeding up XPath query evaluation [1,3,26].
The maximally-contained rewriting problem is the problem of finding, given a
query q and a view v, a rewriting of q using v that is contained in q and is
A. Hameurlain et al. (Eds.): DEXA 2011, Part II, LNCS 6861, pp. 1–16, 2011.
c
Springer-Verlag Berlin Heidelberg 2011](https://image.slidesharecdn.com/1226049-250610022655-20cb42fd/75/Database-And-Expert-Systems-Applications-22nd-International-Conference-Dexa-2011-Toulouse-France-August-29-September-2-2011-Proceedings-Part-Ii-1st-Edition-Pantelis-Aravogliadis-32-2048.jpg)
![2 P. Aravogliadis and V. Vassalos
maximal. This problem arises mostly in the context of information integration,
where we cannot always find an equivalent rewriting due to limited coverage of
data sources [14,23].
Even though there is important progress in the equivalent rewriting problem
for various fragments of XPath (see [1,26] and also Sections 3 and 5 for more
discussion), existing work assumes the absence of schema constraints. Given the
many business, scientific and other uses of XML where schema information is
widely available, the problem of equivalent query rewriting in the presence of a
schema is at least equally important. Using Document Type Definitions (DTDs)
as our schema language, we present the following results:
– Novel sound and complete algorithms for the containment and the query
rewriting problem for XPath queries and views in XP(/,[],*) under a duplicate-
free DTD or an acyclic and choice-free DTD. The algorithms are based on a
novel containment test relying on the extraction of appropriate constraints
from the DTD.
– While the above mentioned algorithm is in PTIME for choice-free DTD,
it is in EXPTIME for duplicate-free DTD. Hence we also develop sound
polynomial algorithms for the containment and the XPath query rewriting
using views problems for XPath queries and views in XP(/,[],*) under a
duplicate-free DTD. The algorithms are shown to be complete when the
query meets a specific condition, namely its C-satisfying set (section 4.1) is
not empty.
The paper is organized as follows: Section 2 reviews the necessary material con-
cerning XPath, tree pattern queries and techniques for XPath containment. The
problem of XPath query rewriting is formally defined in Section 3, where known
results (in the absence of schema information) are also discussed. Section 4 devel-
ops the containment tests for XP(/, [], ∗) as well as the two rewriting algorithms
mentioned above. Section 5 presents the related work, and we conclude with
Section 6 where we discuss ongoing and future work.
2 XPath, DTD and XPath Containment
We first give some definitions for the XPath language and DTDs. Then we
discuss the XPath query containment problem. The definitions follow closely [15]
and [26].
2.1 XPath and Tree Pattern Queries
XML documents are modeled as rooted, ordered, unranked node-labeled trees,
called XML trees, over an infinite alphabet S. We denote all XML trees over S
as TS. Given an XML tree t, following [26], for any node n in t we denote the
subtree of t rooted at n as (t)n
sub.](https://image.slidesharecdn.com/1226049-250610022655-20cb42fd/75/Database-And-Expert-Systems-Applications-22nd-International-Conference-Dexa-2011-Toulouse-France-August-29-September-2-2011-Proceedings-Part-Ii-1st-Edition-Pantelis-Aravogliadis-33-2048.jpg)
![On Equivalence and Rewriting of XPath Queries 3
Every XPath query in XP(/, //, [], ∗) can be represented as a labeled tree,
called a tree pattern, which we define in Definition 1.
Definition 1. A tree pattern p is a tree Vp, Ep, rp, op over S ∪ {∗}, where Vp
is the node set and Ep is the edge set, and: (i) Each node n ∈ Vp has a label from
S ∪ {∗}, denoted as n.label, (ii) Each edge e ∈ Ep has a label from {
/
,
//
},
denoted as e.label, and (iii) rp, op ∈ Vp are the root and the selection node of p
respectively.
An edge with label / is called a child edge, otherwise a descendant edge. Given
a pattern pVp, Ep, rt, op we say that p
Vp , Ep , rp , op is a subpattern of p
if the following conditions hold: (1) Vp ⊆ Vp, (2) Ep = (Vp × Vp ) ∩ Ep, and (3)
if op ∈ Vp , then op is also the selection node of p
. We denote with (p)n
sub the
subpattern of p rooted at node n ∈ Vp and containing all descendants of n. We
next provide the definition of the notion of embedding from a tree pattern to an
XML tree.
Definition 2. [15] Given an XML tree tVt, Et, rt and a pattern pVp, Ep, rp, op,
an embedding from p to t is a function e : Vp → Vt, with the following properties:
(i) root preserving: e(rp) = rt, (ii) label preserving: ∀n ∈ Vp, if n.label =
∗
,
then n.label = e(n).label, and (iii) structure preserving: ∀e
= (n1, n2) ∈ Ep, if
e
.label =
/
then e(n2) is a child of e(n1) in t ; otherwise e(n2) is a descendant
of e(n1) in t.
Each embedding maps the selection node op of p to a node n in t. We say that
the subtree (t)n
sub of t is the result of the embedding. We can now define the
result of evaluating the tree pattern query p over an XML tree t.
Definition 3. Given a tree pattern query p and an XML tree t, then the result
of evaluating p over t, denoted as p(t), is defined as: p(t) = ∪e∈EB{(t)
e(op)
sub },
where EB is the set of all embeddings from p to t.
2.2 Schema and DTDs
DTDs provide a means for typing, and therefore constraining, the structure of
XML documents. We provide a formal definition of a DTD.
Definition 4. A DTD D over a finite alphabet S consists of a start symbol,
denoted as root(D), and a mapping for each symbol a ∈ S to a regular expres-
sion over S. Every such mapping is called a rule of the DTD and the regular
expression corresponding to a is denoted as Ra
.
A tree t ∈ TS satisfies a DTD D over S if root(t).label = root(D).label and, for
each node x in t with a sequence of children y1, . . . , yn, the string y1.label, . . .,
yn.label is in L(Rx.label
), i.e., in the regular language described by the regular
expression on the right-hand side of a rule with left-hand side x.label. The set
of trees satisfying DTD D is denoted as SAT (D).](https://image.slidesharecdn.com/1226049-250610022655-20cb42fd/75/Database-And-Expert-Systems-Applications-22nd-International-Conference-Dexa-2011-Toulouse-France-August-29-September-2-2011-Proceedings-Part-Ii-1st-Edition-Pantelis-Aravogliadis-34-2048.jpg)
![4 P. Aravogliadis and V. Vassalos
In the following, we will consider two categories of DTDs, duplicate-free DTDs
and acyclic and choice-free DTDs. An acyclic and choice-free DTD does not
contain alternation in any of its rules and also does not support recursion. A DTD
is duplicate-free if in each right-hand side of any DTD rule each element name
appears at most once. According to [17] most real-wold DTDs are duplicate-free
DTDs.
2.3 XPath Query Containment
XPath query containment is central to most query rewriting algorithms. We
briefly discuss techniques applicable to this problem for subsets of the expressive
XPath fragment XP(/, //, [].∗). We also present an overview of relevant results
for XPath containment under DTD constraints, focusing especially on the use
of the chase [14,25]. Table 1 in appendix shows known complexity results for the
query containment problem with or without DTD constraints for several XPath
fragments.
Query Containment in the absence of schema. For an XML tree t and
an XPath query q we have that t |= q if q(t) = ∅. We define the boolean XPath
query containment problem.
Definition 5. XPath query p is contained in q, denoted as p ⊆0 q, if and only
if t |= p implies t |= q , for every XML tree t.
According to [13] a more general setting of the containment problem, which is
XPath query p is contained in q, denoted as p ⊆ q, if and only if p(t) ⊆ q(t) for
every XML tree t, can be polynomially on the number of query nodes reduced
to the boolean containment problem. Also, XPath query p is equivalent to q,
denoted as p ≡ q, if and only if p ⊆ q and q ⊆ p.
We can reason about query containment using the canonical models technique
[15,20]. Although this technique provides a sound and complete algorithm for
the containment problem in XP(/, //, [], ∗), it is exponential to the number of
descendant edges of the query. On the contrary, the homomorphism technique
[15,20] provides a sound and complete polynomial algorithm only for the con-
tainment problem for the subfragments of XP(/, //, [], ∗), i.e. for XP(/, //, []),
XP(/, //, ∗) and XP(/, [], ∗).
We briefly describe the homomorphism technique as we will adapt and make
use of it in what follows.
Definition 6. A homomorphism from tree pattern q to tree pattern p is a func-
tion h : nodes(q) → nodes(p) such that: (1) The root of q must be mapped to the
root of p, (2) If (u, v) is a child-edge of q then (h(u), h(v)) is a child-edge in p,
(3) If (u, v) is a descendant-edge of q then h(v) has to be below h(u) in p, and
(4) if u is labeled with e = ∗ then h(u) also has to carry label e.
The existence of a homomorphism is a necessary and sufficient condition for con-
tainment decision in the subfragments of XP(/, //, [], ∗), though is only sufficient
for fragment XP(/, //, [], ∗) [15].](https://image.slidesharecdn.com/1226049-250610022655-20cb42fd/75/Database-And-Expert-Systems-Applications-22nd-International-Conference-Dexa-2011-Toulouse-France-August-29-September-2-2011-Proceedings-Part-Ii-1st-Edition-Pantelis-Aravogliadis-35-2048.jpg)
![On Equivalence and Rewriting of XPath Queries 5
Query Containment with DTDs. In the presence of a DTD, the definition
of containment between two XPath queries is as follows.
Definition 7. Given two XPath queries p, q, then q D-contains p, written
p ⊆SAT (D) q, if for every tree t ∈ SAT (D) it holds p(t) ⊆ q(t).
The containment problem for XP(/, []) in the presence of a duplicate-free DTD
is in PTIME [25] and for XP(/, //, []) under an acyclic and choice-free DTD is
also in PTIME [14]. In order to decide efficiently the containment problem the
chase technique [14,25] has been employed. The basic idea is to chase one of the
two queries with a set of constraints extracted from the DTD and then decide
containment between the queries with the homomorphism technique. Two of
these constraints, which we use in section 4.1 are the Sibling (SC) and Functional
(FC) Constraints [25]. An XML tree t satisfies the SC a: B ⇓ c if whenever a
node labeled a in t has children labeled from set B then it has a child node
labeled c. Also, t satisfies the FC a ↓ b if no node labeled a in t has two distinct
children labeled with b.
A chasing sequence of a query p by a set of Constraints C extracted from a
DTD is a sequence p = p0, . . . , pk such that for each 0 ≤ i ≤ k − 1, pi+1 is the
result of applying some constraint in C to pi , and no constraint can be applied
to pk. We define the pk expression as the chase of p by C, denoted as chaseC(p).
3 XPath Query Rewriting
We first provide the definition of the concatenation operator between two tree
patterns, as in [26].
Definition 8. Given two tree pattern queries p, q then the concatenation op-
erator, denoted as ⊕, when applied on them as p ⊕ q results in a tree pattern
constructed by merging the root of p, denoted as rp, and the selection node of
q, denoted as oq, into one node, provided that their labels are compatible that is
(1) they carry the same label or (2) at least one of them is wildcard labeled. The
merged node is labeled with their common label (case 1) or the non-wildcard label
(case 2) and rq,op are the root and the selection node of p ⊕ q respectively.
Given a tree pattern query q and a DTD D, then q is D-satisfiable if there is an
XML tree t that satisfies D such that q(t) = ∅. We now give the definition of
equivalent XPath query rewriting in the presence of a DTD.
Definition 9. Given a DTD D, an XPath query q and a view v, which are
D-satisfiable, the query rewriting problem is formulated as an: (1) Existence
Problem: Decide if q is rewritable using view v, which is decide if a compensation
query e exists s.t. e ⊕ v ≡SAT (D) q, or a (2) Construction Problem: Find this
compensation query e and thus the equivalent rewriting is the XPath expression
e ⊕ v 1
.
1
In the absence of intersection, we cannot use more than one view in a rewriting in
this setting.](https://image.slidesharecdn.com/1226049-250610022655-20cb42fd/75/Database-And-Expert-Systems-Applications-22nd-International-Conference-Dexa-2011-Toulouse-France-August-29-September-2-2011-Proceedings-Part-Ii-1st-Edition-Pantelis-Aravogliadis-36-2048.jpg)
![6 P. Aravogliadis and V. Vassalos
3.1 Rewriting in the Absence of Schema
To the best of our knowledge there is no existing work addressing the problem in
Definition 9. However, the equivalent rewriting problem in the absence of schema
is addressed by Xu and Özsoyoglu [26]. The following theorem from [26] provides
a basis for deciding the equivalent rewriting existence problem.
Theorem 1. Let u, p be two tree patterns in the subfragments of XP(/, //, [], ∗),
and let the selection node of u be the i-th node in its selection path (i.e. the path
from root of u to its selection node) while np be the i-th node in the selection
path of p, then: if a compensation pattern p
of p using u exist, the subpattern
(p)
np
sub of p is a compensation pattern of p using u.
Theorem 1 reduces the equivalent rewriting existence problem to the decision
problem of whether (p)
np
sub ⊕ u ≡ p. Since for the subfragments of XP(/, //, [], ∗)
the equivalence problem is in PTIME, the rewriting existence problem in these
fragments is in PTIME.
4 Query Rewriting and Containment in the Presence of
DTD
In this section we address the query containment and the equivalent query rewrit-
ing problem in the presence of a DTD for the XPath fragment XP(/, [], ∗).
4.1 The XPath Fragment XP (/, [], ∗) with Duplicate-Free DTD
We discuss the containment and the equivalent rewriting problem for XPath
expressions in XP(/, [], ∗) under the assumption that the considered DTDs are
duplicate-free. In order to provide an efficient solution we will attempt to reduce
the DTD into an appropriate set of constraints, thus reducing the necessary
containment test under a DTD to a containment test under said constraints.
Then we will develop the rewriting algorithm by again appropriately utilizing
Theorem 1. Following this approach we will develop a sound algorithm. Getting
a sound and complete algorithm will require a non-trivial extension, as shown
in the remaining of section 4.1.
Query Containment for XP (/, [], ∗). Containment of queries in XP(/, [], ∗)
in the presence of a duplicate free DTD is CoNP-hard [17]. In this section we
provide a PTIME algorithm for the containment problem that is always sound,
and is complete when the query meets a particular condition (described in Defi-
nition 10 and Theorem 2). Then we proceed to give an unconditional sound and
complete EXPTIME algorithm.
Towards the efficient solution we describe first the constraints that need to
be inferred by a duplicate-free DTD D. Since the XPath fragment we consider
includes only the child axis we will see that we need to infer only Sibling (SC),
Functional (FC) [25] and the newly defined Unique child (UC) constraints. A](https://image.slidesharecdn.com/1226049-250610022655-20cb42fd/75/Database-And-Expert-Systems-Applications-22nd-International-Conference-Dexa-2011-Toulouse-France-August-29-September-2-2011-Proceedings-Part-Ii-1st-Edition-Pantelis-Aravogliadis-37-2048.jpg)
![On Equivalence and Rewriting of XPath Queries 7
Unique child constraint, denoted as UC: a → b, declares that if a node labeled
a in an XML tree t has children, then all of them are labeled with b. Extracting
a UC from a duplicate-free DTD is in PTIME since every rule a → b or a → b∗
from a DTD D yields the a → b UC.
To handle the wildcard labeled nodes we define the corresponding wildcard
versions of the previously mentioned constraints. Let S be the finite alphabet
over which the DTD D is defined. From the set of SCs S
⊆ C, we extract the
wildcard applicable SCs (WSC) as follows. If in S
there are SCs of the
form x: B ⇓ c for every x ∈ S such that x has children with labels in B, a WSC
with the form ∗: B ⇓ c is derived. Also, from the set of FCs F ⊆ C, we extract
the wildcard applicable FCs (WFC) as follows. If there are FCs in F of the
form x ↓ b for every x ∈ S, a WFC of the form ∗ ↓ b is inferred . Finally, from
the set of UCs UI ⊆ C, we extract the wildcard applicable UCs (WUC). If
there are UCs in UI of the form x → b for every x ∈ S, a WUC the form ∗ → b
is inferred.
We observe that the alphabet S from which the labels of any tree t satisfying
D come from is finite. Therefore, we note that the “options” for the wildcards in
XPath expressions in XP(DT D, /, [], ∗) are finite (we will use this observation
in the algorithms of Section 4.1). Furthermore, all the above constraints can
be extracted in PTIME, just following the definitions and utilizing the PTIME
extraction of SCs and FCs from duplicate-free DTDs [25]. In the following, we
use terms FC, SC and UC meaning also the WFC, WSC and WUC constraints.
Let p be a query in XP(/, [], ∗) and C be the set of SCs, FCs and UCs implied
by a DTD D. We describe how to chase p applying these constraints inspired by
[25]:
Sibling Constraints: Let s ∈ C be a SC of the form a: B ⇓ c or a WSC of
the form ∗: B ⇓ c, where B = {b1, . . . , bn}. Let u be a node of p with children
v1, . . . , vn, such that u.label = a for the SC case or u.label = ∗ for the WSC case
and vi.label = bi, 1 ≤ i ≤ n, and u does not have a child labeled c, then the
chase result on p will be the adding of a child-edge to p between u and c.
Functional Constraints: Let f ∈ C be a FC of the form a ↓ b or a WFC of
the form ∗ ↓ b. Let u be a node of p with distinct children v and w, such that
u.label = a for the FC case or u.label = ∗ for the WFC case and v.label =
w.label = b, then the chase result on p is the merging of v, w of p.
Unique Child Constraints: Let ui ∈ C be a UC of the form a → b or a WUC
of the form ∗ → b. Let u be a node of p with distinct wildcard or b labeled
children v1, . . . , vn (and for at least one vi, vi.label = b in the WUC case), such
that u.label = a for the UC case or u.label = ∗ for the WUC case, then the chase
result on p is turning the labels of v1, . . . , vn to b.
Lemma 1. Let D be a duplicate-free DTD. Then q ≡SAT (D) chaseC(q).
Moreover, containment of query q in p under the set of constraints C is reducible
to containment of chaseC(q) in p.](https://image.slidesharecdn.com/1226049-250610022655-20cb42fd/75/Database-And-Expert-Systems-Applications-22nd-International-Conference-Dexa-2011-Toulouse-France-August-29-September-2-2011-Proceedings-Part-Ii-1st-Edition-Pantelis-Aravogliadis-38-2048.jpg)
![8 P. Aravogliadis and V. Vassalos
Lemma 2. Let D be a duplicate-free DTD and C be the set of FCs, SCs and
UCs implied by D. For XP(/, [], ∗) queries p and q it holds p ⊇SAT (C) q if and
only if p ⊇ chaseC(q).
Proof. See proof in [2].
Given the above, we would like to show that p ⊇SAT (D) q if and only if p ⊇SAT (C)
q. As it turns out, this is not always the case for queries in XP(/, [], ∗). The
following definition adopted from [25] helps in proving the required condition
under which it holds that p ⊇SAT (D) q if and only if p ⊇SAT (C) q.
Definition 10. [25] Let q be a D-satisfiable query in XP(/, [], ∗) in the presence
of DTD D and R ⊆ SAT (D) be the set of trees with a subtree 1-1 homomorphic
to chaseC(q). We call R the C-satisfying set for q. Each tree in R has a
core subtree which is 1-1 homomorphic to chaseC(q) and each node in that core
subtree is called core node. Each node which is not a core node is called a
non-core node.
Specifically, we have proven theorem 2 using lemma 3.
Lemma 3. Let D be a duplicate-free DTD and p, q be queries in XP(/, [], ∗).
Let q be D-satisfiable, C be the set of SCs, FCs and UCs implied by D, and R
the C-satisfying set for q, such that R = ∅. If p ⊇SAT (D) q, then for each node
w in p, either w can be mapped to a core node in every tree in R or w can be
mapped to a non-core node in every tree in R.
Theorem 2. Let D be a duplicate-free DTD and C be the set of FCs, SCs, UCs
implied by D. Let p, q be queries in XP(/, [], ∗), such that the C-satisfying set
for q R = ∅. Then p ⊇SAT (D) q if and only if p ⊇SAT (C) q.
Proof. (if) Assume p ⊇SAT (C) q then p ⊇SAT (D) q, because SAT (C) ⊇ SAT (D).
(only if) Assume p ⊇SAT (D) q but p ⊇SAT (C) q. We will derive a contradiction.
Since there is an embedding from q to every t ∈ R and p ⊇SAT (D) q, then there
is also an embedding from p to every tree t ∈ R.
If p ⊇SAT (C) q then by lemma 2 there is no homomorphism from p to
chaseC(q). We have the following two cases:
Case A: a single path in p fails to map to any path in chaseC(q) : There is
a node x in p with parent y, such that y is mapped to a node in chaseC(q) but
no mapping from x to any node in chaseC(q) exists. We originally consider that
node y does not map to a leaf in chaseC(q), which means that x.label can not
be wildcard. So in any t ∈ R, x can never be mapped to a core node while y can
always be mapped to a core node u. Lemma 3 and p ⊇SAT (D) q imply that x
can always be mapped to a non-core node v in t ∈ R. Since v is a non-core node
and x can not be mapped to a core node, D can not imply the SC u: B ⇓ v,
where u and v are the nodes that y and x mapped in a t ∈ R and B is the set
of core node children of u. The same holds, if node y is mapped to a wildcard
labeled node in chaseC(q). This is because D can not imply the SC ∗: B ⇓ v,
which means there is a node labeled u that D can not imply the SC u: B ⇓ v.](https://image.slidesharecdn.com/1226049-250610022655-20cb42fd/75/Database-And-Expert-Systems-Applications-22nd-International-Conference-Dexa-2011-Toulouse-France-August-29-September-2-2011-Proceedings-Part-Ii-1st-Edition-Pantelis-Aravogliadis-39-2048.jpg)
![On Equivalence and Rewriting of XPath Queries 9
So, there must be a tree U ∈ SAT (D) which has a node w labeled the same as
u and children labeled from B, but with no child labeled as v node. Then, we
can replace the non-core child subtrees of u by the non-core child subtrees of w
and still have a tree t
∈ R. Node x can not be mapped to any non-core node in
t
. - A contradiction.
If we consider that the path in p, which fails to map to any path in chaseC(q),
is shorter from every path in chaseC(q) (i.e. y is mapped to a leaf node in
chaseC(q)) then we have two cases: (i) For the non wildcard-labeled leaves in
chaseC(q), D can not imply the SC u: ∅ ⇓ v, which means that either p is
unsatisfiable or the arguments in the proof still hold, (ii) For the wildcard-
labeled leafs in chaseC(q), D can not imply the SC ∗: ∅ ⇓ v, which means there
is at least one node labeled say u that can not have as child a node labeled v. If
all nodes can not have as child a node v, then p is unsatisfiable. If at least one
node can have as child node v, then the proof’s arguments still hold.
Case B: each path in p can map but for some node w which is common ancestor
of nodes u and v, the two mapped paths for u and v in chaseC(q) can not be
joint on w : Let wn be the common ancestor of u, v that is closest to the root
of p, such that the path from root to u via wn (i.e. root → wn → u) maps to
a path in chaseC(q) using homomorphism h1 and the path from root to v via
wn (i.e. root → wn → v) maps to a path in chaseC(q) using homomorphism h2,
but h1(wn) = h2(wn). Since chaseC(q) has a single root and h1(wn) = h2(wn),
then none of these nodes labeled the same as wn is chaseC(q) root. Therefore,
wo, the root of chaseC(q), is a parent of two children labeled the same as wn
and for the root of p, say wpo, it holds h1(wpo) = h2(wpo) = wo.
Case B1 wn.label = ∗ : Note that D can not imply the FC wo ↓ wn (since
the wn-labeled nodes in chaseC(q) would have been merged) and that means
that wo can have an unbounded number of children labeled the same as wn. Let
t be the smallest tree in R, such that the node in t that corresponds to wo in
chaseC(q) has two children nodes with labels the label of wn. Then there is no
embedding from p to t. - A contradiction.
Case B2 wn.label = ∗ : Note that D can not imply the UC wo → b and
the FC wo ↓ wn, for some label b ∈ S, where S is the alphabet from D (since
the wn-labeled nodes in chaseC(q) would have been merged) and that means
that wo can have at least two children labeled from S. Let t be the smallest
tree in R, such that the node in t that corresponds to wo in chaseC(q) has two
children nodes with labels from S. Then there is no embedding from p to t. - A
contradiction.
Note that in theorem 2 the direction “if p ⊇SAT (D) q then p ⊇SAT (C) q” is trivial
and always holds. The opposite direction requires R = ∅. Example 1 describes a
case in which R = ∅ and the effects that this has in the decision of p ⊇SAT (D) q.
Example 1. Let D be a duplicate-free DTD: a → (b, c), b → (i, j), c → (k), and
let p ≡ /a[b[i][j]] and q ≡ /a[∗[i]][∗[j]] be two XPath expression in XP(/, [], ∗).
It is obvious that chaseC(q) ≡ q and also that p ⊇ chaseC(q) since there is no
homomorphism from p to chaseC(q). It is the case in the example that chaseC(q)](https://image.slidesharecdn.com/1226049-250610022655-20cb42fd/75/Database-And-Expert-Systems-Applications-22nd-International-Conference-Dexa-2011-Toulouse-France-August-29-September-2-2011-Proceedings-Part-Ii-1st-Edition-Pantelis-Aravogliadis-40-2048.jpg)
![10 P. Aravogliadis and V. Vassalos
is not 1-1 homomorphic to any tree in SAT (D), or in other words R = ∅. Hence,
theorem 2 does not hold. Indeed, while p ⊇SAT (D) q, according to the theorem
it should not.
Deciding the emptiness of R for a query q, which is a critical condition in theorem
2, is possible as proved in theorem 3.
Theorem 3. Given an XPath query q in XP(/, [], ∗) and a duplicate-free DTD
D the problem of determining whether the C-satisfying set for q is empty, i.e.
R = ∅, is decidable.
Proof. See proof in [2].
A Sound and Complete Algorithm for Query Containment. Given q, p
XPath queries in XP(/, [], ∗) and a duplicate-free DTD D we provide a sound
and complete algorithm for deciding p ⊇0 q. The basic idea is to eliminate
wildcards from q and then by utilizing theorem 2 and lemma 2 we are able to
decide the containment problem.
First we describe how to reduce a query in XP(/, [], ∗) to a disjunction of
queries in XP(/, []). We will do so by providing a useful definition and then
describing a full procedure for eliminating wildcards from q.
Definition 11. Let q be an XPath query in XP(/, [], ∗) and D be a duplicate-
free DTD over S. Let also n be the number of nodes x ∈ Vq, such that x.label = ∗.
We define as L(x1, . . . , xn) the n-ary tuple for which the following hold: (1) each
xi ∈ S, with 1 ≤ i ≤ n, (2) each xi corresponds to a wildcard-labeled node from
q and (3) the query q
, that comes from q by replacing every wildcard node with
its corresponding xi, is D-satisfiable.
The wildcard-set of q, denoted L(q), is the maximum set that contains tuples
of the form L(x1, . . . , xn).
Example 2. Let q ≡ /a/∗/g be an XPath query in XP(/, [], ∗) and the following
duplicate free DTD D: a → (b, c, d, e∗
), b → (f, g, a∗
), e → (f, g), c → (i),
d → (i), f → (i), g → (i).
The alphabet is S = {a, b, c, d, e, f, g, i} and the query q has only one wildcard
label. It is obvious to see that L(q) = {(b), (e)}, since /a/b/g and /a/e/g are
D-satisfiable queries.
Algorithm 1. Wildcard Elimination
Input: An XPath query q in XP(/, [], ∗) and a DTD D.
Compute wildcard-set L(q).
i ← 0
FOR EACH n-ary relation L(x1, . . . , xn) ∈ L(q) DO
i ← i + 1
qi ← replace every wildcard in q with each corresponding xi
q
← q
| qi
END FOR
Output: XPath query q
≡ q1| . . . |qm, s.t. qi is in XP(/, [])](https://image.slidesharecdn.com/1226049-250610022655-20cb42fd/75/Database-And-Expert-Systems-Applications-22nd-International-Conference-Dexa-2011-Toulouse-France-August-29-September-2-2011-Proceedings-Part-Ii-1st-Edition-Pantelis-Aravogliadis-41-2048.jpg)
![On Equivalence and Rewriting of XPath Queries 11
The reduction of an XPath query from XP(/, [], ∗) to XP(/, []) under a
duplicate-free DTD produces an XPath query that is formulated as the dis-
junction of a set of queries in XP(/, []). Wildcard elimination algorithm is not
polynomial due to the complexity of computing the wildcard-set. If we consider
all possible values for each wildcard labeled node in computing the wildcard set
this yields an exponential algorithm. Note that the D-satisfiability does not in-
fluence this complexity, since its complexity for XPath queries in XP(/, []) under
a duplicate-free DTD is PTIME [17]. It is obvious that the number of queries
in the disjunction is equal to the cardinality of the wildcard-set, which in worst
case is exponential to the number of the symbols in the alphabet of the DTD.
Algorithm 2 below is a sound and complete decision procedure for the query
containment problem for XPath expressions in XP(/, [], ∗). It reduces q into a
set of XPath queries in XP(/, []) using algorithm 1. Then, using the fact that an
XPath expression contains a disjunction of XPath expressions if it contains every
single expression that constitutes the disjunction, theorem 2, and lemma 2 we
can decide p ⊇0 q, since for each query in XP(/, []) R = ∅ [25] / The complexity
is dominated by the wildcard-Set computation. Also note that chaseC(qi) is
computed considering only FCs and SCs because each qi is in XP(/, []).
Algorithm 2. Containment Check
Input:q, p in XP(/, [], ∗), a set C of SCs, FCs inferred by a duplicate-free DTD.
q
← Wildcard − Elimination(q)
FOR EACH qi ∈ q
DO
qi ← chaseC(qi)
IF there is no homomorphism from p to qi
THEN RETURN “no”
END FOR
RETURN “yes”;
Output: “yes” if p ⊇0 q ; “no” otherwise.
Example 3. (continuing Example 1) Algorithm 2 first will call the Wildcard-
elimination procedure on q that results in an expression q
≡ /a[b[i]][b[j]]. Then
the FOR loop will be executed only once and in this execution first the chase of
q
will be computed that is chaseC(q
) ≡ /a[b[i][j]] applying the FC a ↓ b and
then will be checked the homomorphism existence from p to chaseC(q
). Since
there exists such a homomorphism then the algorithm will output “yes” meaning
that p ⊇SAT (D) q.
A PTIME Algorithm for Query Containment. Utilizing the equivalence
p ⊇SAT (D) q if and only if p ⊇ chaseC(q), we can derive from theorem 2 and
lemma 2 a PTIME procedure for deciding containment of queries in XP(/, [], ∗)
under a duplicate-free DTD. Since theorem 2 requires the C−satisfying set R for
query q to be non-empty, the algorithm is complete only for queries with this prop-
erty. It is trivially sound since, if p ⊇ chaseC(q) then p ⊇SAT (D) q. It uses a](https://image.slidesharecdn.com/1226049-250610022655-20cb42fd/75/Database-And-Expert-Systems-Applications-22nd-International-Conference-Dexa-2011-Toulouse-France-August-29-September-2-2011-Proceedings-Part-Ii-1st-Edition-Pantelis-Aravogliadis-42-2048.jpg)
![12 P. Aravogliadis and V. Vassalos
PTIME chase procedure inspired by [25], that in contrast to applying every rule
to the query until no other rule application changes the query, applies the rules
on q along its selection path in a way that takes into account the query p.
Algorithm 3. PTIME Containment Check
Input: q, p in XP(/, [], ∗), a duplicate free DTD D.
Step 1: Compute the chaseC(p) – C set of FCs, SCs, UCs inferred by D.
p ← chaseC(p, q, D)
Step 2: Check Containment
IF there exist a homomorphism from q to p THEN RETURN “yes”
ELSE RETURN “no”
Output: “yes” if p ⊆0 q ; “no” otherwise.
Algorithm 4. Chase(p,q,D)
Input: p, q ∈ XP(/, [], ∗), D a duplicate-free DTD.
Apply UCs to p inferred by D
Apply FCs to p inferred by D
Traverse in top-down manner expressions p, q
Find nodes x ∈ q and u ∈ p s.t.
(x.label = u.label OR u.label = ∗ OR x.label = ∗) AND (x has a child y)
AND (u has no child with y.label = ∗ OR y.label = ∗)
IF (y.label = ∗)
Apply the inferred SC u.label: B ⇓ y.label from D to p,
where B is the set of labels of children of u ∈ p.
ELSE −(y.label = ∗)
Apply every inferred SC u.label: B ⇓ z.label from D to p,
where B is the set of labels of children of u ∈ p.
Output: chaseC(p).
Query Rewriting for XP (/, [], ∗). For a view v and a query q in XP(/, [], ∗),
it is CoNP-hard to decide if there is a rewriting of q using v in the presence of
a duplicate free DTD D. This follows immediately from theorem 2 of [17] and
lemma 4.1 of [26]. In what follows we provide two algorithms for the query
rewriting problem described in definition 9.
A Sound and Complete Query Rewriting Algorithm. Algorithm 5 below,
when we use as containment test Algorithm 2, is a sound and complete algorithm
for the rewriting problem for queries in XP(/, [], ∗) under a duplicate-free DTD.
Algorithm 5. Query Rewriting using views
Input: q, v in XP(/, [], ∗), D a duplicate-free DTD D.
Step 1: Check rewriting existence
let ov be the output node of v
let nq be a node in q having the same position to ov
IF ((q)
nq
sub ⊕ v ≡SAT (D) q) THEN RETURN “no”](https://image.slidesharecdn.com/1226049-250610022655-20cb42fd/75/Database-And-Expert-Systems-Applications-22nd-International-Conference-Dexa-2011-Toulouse-France-August-29-September-2-2011-Proceedings-Part-Ii-1st-Edition-Pantelis-Aravogliadis-43-2048.jpg)
![On Equivalence and Rewriting of XPath Queries 13
ELSE RETURN “yes”
Step 2: Find rewriting
IF (answer is “yes”)
THEN (q)
nq
sub is a compensation pattern of q using v.
Output: “yes” if q is rewritable using v and the compensation pattern (q)
nq
sub.
Since the containment test is EXPTIME then algorithm 5, whose correctness
follows from theorem 1, is in EXPTIME.
Query Rewriting in PTIME. If in algorithm 5 we use instead the PTIME
containment test of section 4.1 then we obtain a PTIME query rewriting
algorithm.
Example 4 shows a simple rewriting for a query in XP(/, [], ∗).
Example 4. (continuing Example 2) Let v ≡ /a/ ∗ [f] be an XPath view in
XP(/, [], ∗) under the duplicate free DTD D defined in example 2. Algorithm 5
will generate as compensation query c ≡ / ∗ /g. Notice that chasing q with the
WSC ∗: {g} ⇓ f we get q
≡ /a/ ∗ [f]/g.
4.2 The XPath Fragment XP (/, [], ∗) with Acyclic and Choice-Free
DTD
We can adapt algorithm 5 to solve the query rewriting problem under acyclic
and choice-free DTD. For the wildcard-labeled children of any node of a query in
XP(/, [], ∗) under an acyclic and choice-free DTD, is easy to determine to which
label from the alphabet correspond. Therefore following the wildcard elimination
technique we obtain a PTIME algorithm for the containment problem for queries
in XP(/, [], ∗) under acyclic and choice-free DTD. This problem is shown in [4] to
be in PTIME. Therefore, we can obtain a PTIME sound and complete algorithm
for the rewriting problem.
5 Related Work
A framework for the problem of finding equivalent XPath query rewritings using
views is provided in [3]. The problem is studied extensively in [1,26] for XPath
queries in XP(/, //, [], ∗) providing either incomplete algorithms [26] or suffi-
cient conditions under which the problems can be shown to be CoNP-Complete
[1]. There is also recent work [9,21] on the problem of answering XPath queries
using multiple views via the use of node intersection. Such work expands the
scope of the XPath subset considered for the views and the rewritings, adding
intersection. Finally, in [10] Cautis et al. have looked into the problem of rewrit-
ing XPath queries using views generated by expressive declarative specifications
resulting potentially in an infinite number of views. The specifications impose
certain constraints on the structure of generated views, but these constraints are
orthogonal to DTD constraints, which are imposed on the database.](https://image.slidesharecdn.com/1226049-250610022655-20cb42fd/75/Database-And-Expert-Systems-Applications-22nd-International-Conference-Dexa-2011-Toulouse-France-August-29-September-2-2011-Proceedings-Part-Ii-1st-Edition-Pantelis-Aravogliadis-44-2048.jpg)
![14 P. Aravogliadis and V. Vassalos
The XPath query containment problem, which is very closely related to the
XPath query rewriting problem, has been extensively studied for various XPath
fragments in [5,11,14,15,18,24,25,27], including certain constraints such as DTDs.
Relevant work have been discussed in section 2. We use and extend these results
developing algorithms for the problem under DTD constraints for an XPath
fragment with wildcard. These results are the basis for the rewriting algorithms
we propose.
6 Conclusions and Future Work
We develop sound and complete algorithms for XPath rewriting using views
in the presence of DTD constraints for the XPath fragment XP(/, [], ∗). We
also provide a novel reduction of a DTD into chase-able constraints and use it to
develop two algorithms for checking containment for the XP(/,[],*) fragment; one
is polynomial but is complete only when the query satisfies a certain condition
(not empty C-Satisfying set), the other is complete but exponential, as it relies
on the elimination of wildcards from one of the queries.
We plan to continue our investigation into efficient techniques for rewrit-
ing using views for more expressive XPath fragments including backward axes.
Moreover, following recent results for the containment of XPath fragments that
include union and intersection [22], we are working on rewriting for queries and
views for those XPath fragments.
References
1. Afrati, F., Chirkova, R., Gergatsoulis, M., Pavlaki, V., Kimelfeld, B., Sagiv, Y.:
On rewriting xpath queries using views. In: EDBT, pp. 168–179 (2009)
2. Aravogliadis, P., Vassalos, V.: Rewriting xpath queries using views under dtd con-
straints. Technical report, AUEB (2009),
http://wim.aueb.gr/papers/xpathrewrite-ext.pdf
3. Balmin, A., Ozcan, F., Beyer, K., Cochrane, R., Pirahesh, H.: A framework for
using materialized XPath views in XML query processing. In: Proc. of the 30th
VLDB Conference, pp. 60–71 (2004)
4. Benedikt, M., Fan, W., Geerts, F.: XPath satisfiability in the presence of DTDs.
In: Proc. PODS 2005, pp. 25–36 (2005)
5. Benedikt, M., Koch, C.: Xpath leashed. ACM Comptuting Survey 41(1) (2008)
6. Berglund, A., Boag, S., Chamberlin, D., et al.: XML Path Language (XPath) 2.0.
W3C, http://www.w3.org/TR/XPath20
7. Boag, S., Chamberlin, D., Fernandez, M.F., et al.: XQuery 1.0: An XML Query
Language. W3C, http://www.w3.org/TR/XQuery
8. Bruno, N., Koudas, N., Srivastava, D.: Holistic twig joins: optimal XML pattern
matching. In: Proc. SIGMOD Conference, pp. 310–321 (2002)
9. Cautis, B., Deutsch, A., Onose, N.: Xpath rewriting using multiple views: Achieving
completeness and efficiency. In: Proc. WebDB 2008 (2008)
10. Cautis, B., Deutsch, A., Onose, N., Vassalos, V.: Efficient rewriting of xpath queries
using query set specifications. In: Proceedings of the VLDB Endowment, vol. 2,
pp. 301–312 (2009)](https://image.slidesharecdn.com/1226049-250610022655-20cb42fd/75/Database-And-Expert-Systems-Applications-22nd-International-Conference-Dexa-2011-Toulouse-France-August-29-September-2-2011-Proceedings-Part-Ii-1st-Edition-Pantelis-Aravogliadis-45-2048.jpg)
![16 P. Aravogliadis and V. Vassalos
Appendix
Constraints from DTD. In section 4.1 we define the SC, FC and UC constraints
and their corresponding wildcard versions. The definition of WSC was such that
no new wildcard nodes will be added to the query after chasing it. Also, rules of
the form a ↓ ∗ are not extracted from DTD since such rules are a meaningless
abbreviation of a set of functional constraints and also according to the semantics
of chase such rules will not maintain the equivalence given in lemma 1. Finally,
note that the UC constraint declares that a node will have children all labeled the
same and obviously can not derive from SC or FC constraints.
Table 1. XPath query containment complexity with-without DTD
DTD / // [] * Complexity
without + + + PTIME [15]
without + + + PTIME [15,16]
without + + + PTIME [15]
without + + + + CoNP-Complete [15]
general + + PTIME [18]
general + + CoNP-complete [18]
duplicate-free PTIME [25]
general CoNP-hard [18]
acyclic choice-free + + + PTIME [14]
duplicate-free CoNP-hard [17]
acyclic choice-free + + + PTIME [4]
general + + + + EXPTIME-complete [25]](https://image.slidesharecdn.com/1226049-250610022655-20cb42fd/75/Database-And-Expert-Systems-Applications-22nd-International-Conference-Dexa-2011-Toulouse-France-August-29-September-2-2011-Proceedings-Part-Ii-1st-Edition-Pantelis-Aravogliadis-47-2048.jpg)
![Incremental Maintenance of Materialized XML Views
Leonidas Fegaras
University of Texas at Arlington
fegaras@cse.uta.edu
Abstract. We investigate the problem of incremental maintenance of material-
ized XML views. We are considering the case where the underlying database is
a relational database and the view exposed to querying is a materialized XML
view. Then, updates to the underlying database should be reflected to the stored
XML view, to keep it consistent with the source data, without recreating the en-
tire view from the database after each source update. Unlike related work that
uses algebraic methods, we use source-to-source compositional transformations,
guided by the database and view schemata. We first translate SQL updates to pure
(update-free) XQuery expressions that reconstruct the entire database state, re-
flecting the updated values in the new state. Then, we synthesize the right-inverse
of the XQuery view function, guided by the view schema. This inverse function is
applied to the old view to derive the old database state, which in turn is mapped to
the new database state through the update function, and then is mapped to the new
view through the view function. The resulting view-to-view function is normal-
ized and translated to XQuery updates that destructively modify the materialized
view efficiently to reflect the new view values.
1 Introduction
We address the problem of incremental maintenance of materialized XML views. That
is, given a materialized XML view over a database and an update against the database,
our goal is to generate view updates so that the new view after the updates is exactly
the same as the view we would have gotten if we had applied the view to the updated
source data. We are considering the case where the underlying database is a relational
database and the view exposed to querying is a materialized XML view, stored in a
native XML database. A native XML database is a specialized database that supports
storage, indexing methods, and query processing techniques specially tailored to XML
data. There are already many native XML database management systems available, in-
cluding Oracle Berkeley-DB XML, Qizx, Natix, etc. Our framework is targeting those
native XML databases that support the XQuery Update Facility (XUF) [16] for updat-
ing XML data. Exporting data through materialized XML views is very important to
data integration, not only because it speeds up query processing, but also because it
provides a light-weight interface to data services, without exposing the main database.
The incremental maintenance of materialized views is important for applications that
require freshness of view data, but it is too expensive to recompute the view data every
time the source data change.
In our framework, SQL updates are translated to plain XQueries applied to a canon-
ical view of the relational database. More specifically, we derive a pure update function
A. Hameurlain et al. (Eds.): DEXA 2011, Part II, LNCS 6861, pp. 17–32, 2011.
c
Springer-Verlag Berlin Heidelberg 2011](https://image.slidesharecdn.com/1226049-250610022655-20cb42fd/75/Database-And-Expert-Systems-Applications-22nd-International-Conference-Dexa-2011-Toulouse-France-August-29-September-2-2011-Proceedings-Part-Ii-1st-Edition-Pantelis-Aravogliadis-48-2048.jpg)
![18 L. Fegaras
that maps the old database to the new database. Then, we synthesize the right-inverse of
the XQuery view function, guided by the view schema. This inverse function is applied
to the old view to derive the old database state, which in turn is mapped to the new
database state through the update function, and then mapped to the new view through
the view function. Although correct, the resulting view-to-view function can be very
inefficient. We provide methods to optimize this program and translate it into efficient
XQuery updates, so that only the relevant parts of the materialized view are updated.
More specifically, our framework performs the following tasks:
1. We map the underlying relational database DB to the XML data db using a canoni-
cal view of relational data. This wrapper, which is a virtual XML view, allows us to
refer to relational data within the XQuery data model without any model extension.
2. We express the source SQL updates in pure (update-free) XQuery code over the
virtual XML view, as a function u from the old to the new database state that re-
constructs the entire canonical XML view of the relational database, reflecting the
updates.
3. Given a view, view, expressed in XQuery, we synthesize the XQuery expression
view
that is a right-inverse of view, such that view(view
(V )) = V for any
valid view instance V (that is, view must be a surjective function). Note that,
view
(view(DB)) is not necessarily equal to DB; it is equal to DB for isomorphic
views only, which are uncommon.
4. The composition F(V ) = view(u(view
(V ))) is a function from the old view V to
the new view (after the updates), where view
(V ) creates an entire source database
from the view V , then u reconstructs this database to reflect the updates, and finally
view maps the new database to the new view. We use normalization techniques to
optimize F(V ) so that the resulting program avoids the reconstruction of most parts
of the intermediate database data.
5. Finally, we rewrite the normalized XQuery expression F(V ) into efficient XQuery
updates that destructively modify the original view V using the XQuery Update
Facility (XUF). That is, the resulting program is a direct view update expressed in
XUF that, in most cases, does not access the underlying database.
The key contribution of our work is in the development of a novel framework for the
incremental maintenance of materialized XML views that uses source-to-source, com-
positional transformations only. Unlike related approaches that use framework-specific
algebras to achieve a similar goal [10,12], our work can be incorporated into any ex-
isting XQuery engine. Since it breaks the task of view maintenance into a number of
more manageable tasks that are easier to implement and verify their correctness, it has
the potential of becoming a general methodology for incremental view maintenance.
The most important limitation of our approach is that both the inverse synthesis
(Step 3) and the XUF generation (Step 5) algorithms are schema-based. That is, they re-
quire that both the underlying relational and the XML view schemata be known at query
translation time. The second limitation is that our algorithms are heuristics that work
only in certain cases, such as they can handle one-to-one and one-to-many but they can-
not handle many-to-many joins in the view definition. Finally, due to the lack of space,
we have not covered some parts the XQuery/XUF syntax, such as descendant-or-self](https://image.slidesharecdn.com/1226049-250610022655-20cb42fd/75/Database-And-Expert-Systems-Applications-22nd-International-Conference-Dexa-2011-Toulouse-France-August-29-September-2-2011-Proceedings-Part-Ii-1st-Edition-Pantelis-Aravogliadis-49-2048.jpg)
![Incremental Maintenance of Materialized XML Views 19
steps, but we believe that there is no fundamental reason why our framework cannot be
extended to cover most of the XQuery syntax.
2 Related Work
Although the problem of incremental view maintenance has been extensively studied
for relational views (see [11] for a literature survey), there is still little work on XML
view maintenance [3,1]. One of the earliest works on incremental maintenance of ma-
terialized views over semi-structured data was for the Lorel query language [2], which
was based on the graph-based data model OEM. Their method produces a set of queries
that compute the updates to the view based upon an update of the source. Although there
are already a number of proposals for XQuery update languages, there is now a W3C
candidate recommendation, called XQuery Update Facility (XUF) [16]. El-Sayed et
al [12] use an algebraic approach for incremental XQuery view maintenance, where an
update to the XML source is transformed into a set of update primitives that are prop-
agated through the XML algebra tree and become incremental update primitives to be
applied to the result view. The work by Sawires et al [14] considers the view mainte-
nance problem for simple XPath views (without value-based predicates). It reduces the
frequency of view recomputation by detecting cases where a source update is irrele-
vant to a view and cases where a view is self-maintainable given a base update. BEA’s
AquaLogic Data Services Platform allows developers to specify inverse functions that
enable its XQuery optimizer to push predicates and perform updates on data services
that involve the use of such functions [13]. Finally, although not directly applicable
to incremental view maintenance, the inverse problem of updating XML views over
relational data by translating XML view updates to embedded SQL updates has been
studied by earlier work (see for example [4,6]).
3 Our Framework
In this section, we use an XML view example to describe our approach to the incre-
mental maintenance of XML views. Consider a bibliography database described by the
following relational schema:
Article ( aid, title, year )
Author ( aid, pid )
Person ( pid, name, email )
where the attributes Author.aid and Author.pid are foreign keys that reference the keys
Article.aid and Person.pid, respectively. An XML view over a relational database can be
defined over a canonical XML view of relational data, as is done in SilkRoute [9] and
many other systems. The canonical XML view of the bibliography relational schema
has the following XML type:
element DB {
element Article {
element row { element aid { xs: int }, element title { xs: string },
element year { xs: string } }∗ },](https://image.slidesharecdn.com/1226049-250610022655-20cb42fd/75/Database-And-Expert-Systems-Applications-22nd-International-Conference-Dexa-2011-Toulouse-France-August-29-September-2-2011-Proceedings-Part-Ii-1st-Edition-Pantelis-Aravogliadis-50-2048.jpg)
![20 L. Fegaras
element Author {
element row { element aid { xs: int }, element pid { xs: int } }∗ },
element Person {
element row { element pid { xs: int }, element name { xs: string },
element email { xs: string } }∗ } }
that is, a relational table is mapped to an XML element whose tag name is the table
name and its children are row elements that correspond to the table tuples. In addition,
this type satisfies the following key constraints:
key { /DB/Article/row, aid / data () }
key { /DB/Author/row, (aid ,pid )/ data () }
key { /DB/Person/row, pid/ data () }
As in XML-Schema [15], for a key { p1, p2 }, the selector path p1 defines the applicable
elements and the field path p2 defines the data that are unique across the applicable
elements. For example, key { /DB/Article/row, aid/data() } indicates that the aid elements
are unique for the Article rows.
Given this canonical view $DB of the relational database, an XML view, view($DB),
can be defined using plain XQuery code:
view($DB) = bib{
for $i in $DB/Article/row
return article aid=’{$i/ aid/ data()}’{
$i / title , $i /year,
for $a in $DB/Author/row[aid=$i/aid]
return author pid=’{$a/pid/ data()}’{
$DB/Person/row[pid=$a/pid]/name/data()
}/author
}/ article }/bib
This code converts the entire canonical view of the relational database into an XML
tree that has the following type:
element bib {
element article {
attribute aid { xs: int },
element title { xs: string }, element year { xs: string },
element author { attribute pid { xs: int }, xs: string }∗ }∗ }
We assume that this view is materialized on a persistent storage, which is typically a
native XML database. Our goal is to translate relational updates over the base data to
XQuery updates (expressed in XUF) that incrementally modify the stored XML view to
make it consistent with the base data. Our framework synthesizes a right-inversefuncton
view’ of the view function, view, such that, for all valid view instances V , view(view’(V ))
is equal to V . Since an inverted view reconstructs the relational database from the view,
it must preserve the key constraints of the relational schema. This is accomplished with
an extension of the FLWOR XQuery syntax that enforces these constraints. In its sim-
plest form, our FLWOR syntax may contain an optional ‘unique’ part:
for $v in e where p($v) unique k($v) return f($v)](https://image.slidesharecdn.com/1226049-250610022655-20cb42fd/75/Database-And-Expert-Systems-Applications-22nd-International-Conference-Dexa-2011-Toulouse-France-August-29-September-2-2011-Proceedings-Part-Ii-1st-Edition-Pantelis-Aravogliadis-51-2048.jpg)
![22 L. Fegaras
return article aid=’{$i/aid / data()}’{
$i / title , $i /year,
for $a in view’($V)/Author/row[aid=$i/ aid]
return author pid=’{$a/pid/ data()}’{
view’($V)/Person/row[pid=$a/pid ]/name/data() }/author
}/ article }/bib
XQuery normalization reduces general XQueries to normal forms, such as to for-loops
whose domains are simple XPaths. In addition to fusing layers of XQuery compositions
and eliminating their intermediate results, normal forms are simpler to analyze than
their original forms. Normalization can be accomplished with the help of rules, such as:
for $v in (for $w in e1 returne2) return e3 = for $w in e1, $v in e2 return e3
to normalize the for-loop domain. If we expand the view’($V) definition and normalize
the resulting code, we get the following code:
bib{
for $ii in $V/ article
unique $ii /@aid/data()
return article aid=’{$ii /@aid/data()}’{
$ii / title , $ii /year,
for $ia in $V/ article , $aa in $ia / author
where $ia/@aid/data()= $ii /@aid/data()
unique ($ia/@aid/data (), $aa/@pid/data())
return author pid=’{$aa/@pid/data()}’{
for $ip in $V/ article , $ap in $ip/ author
where $ap/@pid/data()=$aa/@pid/data()
unique $ap/@pid/data()
return $ap/data ()
}/author
}/ article }/bib
To simplify this code further, we would need to take into account the key constraints
expressed in the unique parts of the for-loops. A general rule that eliminates nested
for-loops is:
for $v1 in f1($v), . . . , $vn in fn($v)
return g( for $w1 in f1($w), . . . , $wn in fn($w)
where key($w) = key($v) unique key($w) return h($w) )
= for $v1 in f1($v), . . . , $vn in fn($v) return g( h($v) )
where key, g, h, f1, . . . , fn are XQuery expressions that depend on the XQuery vari-
ables, $v and $w, which are the variables $v1, . . . , $vn and $w1, . . . , $wn, respectively.
This rule, indicates that if we have a nested for-loop, where the inner for-loop variables
have the same domains as those of the outer variables, modulo variable renaming (that
is, fi($v) and fi($w), for all i), and they have the same key values, for some key func-
tion, then the values of $w must be identical to those of $v, and therefore the inner
for-loop can be eliminated. We can apply this rule to our previous normalized code, by
noticing that the outer for-loop is over the variables $ia and $aa and the inner for-loop
is over the variables $ip and $ap, where the key is $ap/@pid/data() and the predicate is
$ap/@pid/data()=$aa/@pid/data(). This means that the code can be simplified to:](https://image.slidesharecdn.com/1226049-250610022655-20cb42fd/75/Database-And-Expert-Systems-Applications-22nd-International-Conference-Dexa-2011-Toulouse-France-August-29-September-2-2011-Proceedings-Part-Ii-1st-Edition-Pantelis-Aravogliadis-53-2048.jpg)
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- 6. Lecture Notes inComputer Science 6861 Commenced Publication in 1973 Founding and Former Series Editors: Gerhard Goos, Juris Hartmanis, and Jan van Leeuwen Editorial Board David Hutchison Lancaster University, UK Takeo Kanade Carnegie Mellon University, Pittsburgh, PA, USA Josef Kittler University of Surrey, Guildford, UK Jon M. Kleinberg Cornell University, Ithaca, NY, USA Alfred Kobsa University of California, Irvine, CA, USA Friedemann Mattern ETH Zurich, Switzerland John C. Mitchell Stanford University, CA, USA Moni Naor Weizmann Institute of Science, Rehovot, Israel Oscar Nierstrasz University of Bern, Switzerland C. Pandu Rangan Indian Institute of Technology, Madras, India Bernhard Steffen TU Dortmund University, Germany Madhu Sudan Microsoft Research, Cambridge, MA, USA Demetri Terzopoulos University of California, Los Angeles, CA, USA Doug Tygar University of California, Berkeley, CA, USA Gerhard Weikum Max Planck Institute for Informatics, Saarbruecken, Germany
- 8. Abdelkader Hameurlain StephenW. Liddle Klaus-Dieter Schewe Xiaofang Zhou (Eds.) Database and Expert Systems Applications 22nd International Conference, DEXA 2011 Toulouse, France,August 29 - September 2, 2011 Proceedings, Part II 1 3
- 9. Volume Editors Abdelkader Hameurlain PaulSabatier University, IRIT Institut de Recherche en Informatique de Toulouse 118, route de Narbonne, 31062 Toulouse Cedex, France E-mail: hameur@irit.fr Stephen W. Liddle Brigham Young University, 784 TNRB Provo, UT 84602, USA E-mail: liddle@byu.edu Klaus-Dieter Schewe Software Competence Centre Hagenberg Softwarepark 21, 4232 Hagenberg, Austria E-mail: kd.schewe@scch.at Xiaofang Zhou University of Queensland, School of Information Technology and Electrical Engineering Brisbane QLD 4072, Australia E-mail: zxf@uq.edu.au ISSN 0302-9743 e-ISSN 1611-3349 ISBN 978-3-642-23090-5 e-ISBN 978-3-642-23091-2 DOI 10.1007/978-3-642-23091-2 Springer Heidelberg Dordrecht London New York Library of Congress Control Number: 2011934284 CR Subject Classification (1998): H.4, I.2, H.3, C.2, H.5, J.1 LNCS Sublibrary: SL 3 – Information Systems and Application, incl. Internet/Web and HCI © Springer-Verlag Berlin Heidelberg 2011 This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, re-use of illustrations, recitation, broadcasting, reproduction on microfilms or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable to prosecution under the German Copyright Law. The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Typesetting: Camera-ready by author, data conversion by Scientific Publishing Services, Chennai, India Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
- 10. Preface This volume includesinvited papers, research papers, and short papers presented at DEXA 2011, the 22nd International Conference on Database and Expert Sys- tems Applications, held in Toulouse, France. DEXA 2011 continued the long and successful DEXA tradition begun in 1990, bringing together a large collection of bright researchers, scientists, and practitioners from around the world to share new results in the areas of database, intelligent systems, and related advanced applications. The call for papers resulted in the submission of 207 papers, of which 52 were accepted as regular research papers, and 40 were accepted as short papers. The authors of these papers come from 47 different countries. These papers discuss a range of topics including: – Query processing and Skyline queries – Search (Web, Semantic Web, database) – Data integration, transactions, optimization, and design – Physical aspects of databases, storage – Database semantics – Security and privacy – Spatial and temporal data – Semantic Web – Web applications – Data mining – Ontologies – Distribution – Information retrieval – XML querying and views – Business applications – User support Three internationally recognized scholars submitted papers and delivered keynote speeches: Patrick Valduriez: Principles of Distributed Data Management 2020? Bernhard Thalheim: The Science of Conceptual Modelling Gabriele Kern-Isberner: Probabilistic Logics in Expert Systems: Approaches, Implementations, and Applications In addition to the main conference track, DEXA 2011 also included 12 work- shops that explored the conference theme within the context of life sciences, specific application areas, and theoretical underpinnings.
- 11. VI Preface We aregrateful to the hundreds of authors who submitted papers to DEXA 2011 and to our large Program Committee for the many hours they spent care- fully reading and reviewing these papers. The Program Committee was also assisted by a number of external referees, and we appreciate their contributions and detailed comments. We are thankful to the Institut de Recherche en Informatique de Toulouse at the Université Paul Sabatier for organizing DEXA 2011, and for the excellent working atmosphere provided. In particular, we recognize the efforts of the con- ference Organizing Committee, including Makoto Takizawa (Seikei University, Japan; Honorary Chairperson), Abdelkader Hameurlain (IRIT, Paul Sabatier University, France; General Chair), Riad Mokadem (IRIT, Paul Sabatier Uni- versity; Local Organization), Vladimir Marik (Czech Technical University, Czech Republic; Publication Chair), Franck Morvan (IRIT, Paul Sabatier University, Toulouse, France; Workshops Co-chair), A Min Tjoa (Technical University of Vienna, Austria; Workshops Co-chair), and Roland R. Wagner (FAW, Univer- sity of Linz, Austria; Workshops Co-chair). Without the diligent efforts of these people, DEXA 2011 would not have been possible. Finally, we are especially grateful to Gabriela Wagner, whose professional attention to detail and skillful handling of all aspects of the Program Committee management and proceedings preparation was most helpful. August 2011 Stephen W. Liddle Klaus-Dieter Schewe Xiaofang Zhou
- 12. Program Committee Honorary Chairperson MakotoTakizawa Seikei University, Japan General Chair Abdelkader Hameurlain IRIT, Paul Sabatier University, Toulouse, France Conference Program Chair Stephen Liddle Brigham Young University, USA Klaus-Dieter Schewe Software Competence Center Hagenberg and Johannes Kepler University Linz, Austria Xiaofang Zhou University of Queensland, Australia Program Committee Witold Abramowicz The Poznan University of Economics, Poland Osman Abul TOBB University, Turkey Rafael Accorsi University of Freiburg, Germany Hamideh Afsarmanesh University of Amsterdam, The Netherlands Riccardo Albertoni CNR-IMATI-GE, Italy Toshiyuki Amagasa University of Tsukuba, Japan Rachid Anane Coventry University, UK Annalisa Appice Università degli Studi di Bari, Italy Mustafa Atay Winston-Salem State University, USA James Bailey University of Melbourne, Australia Spiridon Bakiras City University of New York, USA Ladjel Bellatreche ENSMA-Poitiers University, France Morad Benyoucef University of Ottawa, Canada Catherine Berrut Grenoble University, France Bishwaranjan Bhattacharjee IBM Thomas J. Watson Research Center, USA Debmalya Biswas SAP Research, Germany Agustinus Borgy Waluyo Institute for Infocomm Research, Singapore Patrick Bosc IRISA/ENSSAT, France Athman Bouguettaya CSIRO, Australia Danielle Boulanger University of Lyon, France Omar Boussaid University of Lyon, France
- 13. VIII Program Committee StephaneBressan National University of Singapore, Singapore Patrick Brezillon University Paris VI, France Yingyi Bu Microsoft, China Luis M. Camarinha-Matos Universidade Nova de Lisboa + Uninova, Portugal Yiwei Cao RWTH Aachen University, Germany Barbara Carminati Università degli Studi dell’Insubria, Italy Silvana Castano Università degli Studi di Milano, Italy Barbara Catania Università di Genova, Italy Michelangelo Ceci University of Bari, Italy Wojciech Cellary University of Economics at Poznan, Poland Cindy Chen University of Massachusetts Lowell, USA Phoebe Chen La Trobe University, Australia Shu-Ching Chen Florida International University, USA Hao Cheng University of Central Florida, USA Reynold Cheng The University of Hong Kong, China Max Chevalier IRIT - SIG, Université de Toulouse, France Byron Choi Hong Kong Baptist University, Hong Kong Henning Christiansen Roskilde University, Denmark Soon Ae Chun City University of New York, USA Eliseo Clementini University of L’Aquila, Italy Gao Cong Microsoft Research Asia, China Oscar Corcho Universidad Politécnica de Madrid, Spain Bin Cui Peking University, China Emiran Curtmola University of California, San Diego, USA Alfredo Cuzzocrea University of Calabria, Italy Deborah Dahl Conversational Technologies, worldwide Jérôme Darmont Université Lumière Lyon 2, France Valeria De Antonellis Università di Brescia, Italy Andre de Carvalho University of Sao Paulo, Brazil Guy De Tré Ghent University, Belgium Olga De Troyer Vrije Universiteit Brussel, Belgium Roberto De Virgilio Università Roma Tre, Italy John Debenham University of Technology, Sydney, Australia Hendrik Decker Universidad Politécnica de Valencia, Spain Zhi-Hong Deng Peking University, China Vincenzo Deufemia Università degli Studi di Salerno, Italy Claudia Diamantini Università Politecnica delle Marche, Italy Juliette Dibie-Barthélemy AgroParisTech, France Ying Ding Indiana University, USA Zhiming Ding Chinese Academy of Sciences, China Gillian Dobbie University of Auckland, New Zealand Peter Dolog Aalborg University, Denmark Dejing Dou University of Oregon, USA Dirk Draheim Universität Innsbruck, Austria
- 14. Program Committee IX Cedricdu Mouza CNAM, France Johann Eder University of Vienna, Austria David Embley Brigham Young University, USA Suzanne M. Embury The University of Manchester, UK Christian Engelmann Oak Ridge National Laboratory, USA Bettina Fazzinga University of Calabria, Italy Leonidas Fegaras The University of Texas at Arlington, USA Flavio Ferrararotti Victoria University of Wellington, New Zealand Stefano Ferilli University of Bari, Italy Eduardo Fernandez Florida Atlantic University, USA Filomena Ferrucci Università di Salerno, Italy Flavius Frasincar Erasmus University Rotterdam, The Netherlands Bernhard Freudenthaler Software Competence Center Hagenberg, Austria Hiroaki Fukuda Shibaura Institute of Technology, Japan Steven Furnell University of Plymouth, UK Aryya Gangopadhyay University of Maryland Baltimore County, USA Yunjun Gao Zhejiang University, China Manolis Gergatsoulis Ionian University, Greece Bernard Grabot LGP-ENIT, France Fabio Grandi University of Bologna, Italy Carmine Gravino University of Salerno, Italy Nathan Griffiths University of Warwick, UK Sven Groppe Lübeck University, Germany William Grosky University of Michigan, USA Volker Gruhn Leipzig University, Germany Jerzy Grzymala-Busse University of Kansas, USA Francesco Guerra Università degli Studi di Modena e Reggio Emilia, Italy Giovanna Guerrini University of Genova, Italy Antonella Guzzo University of Calabria, Italy Abdelkader Hameurlain Paul Sabatier University, Toulouse, France Ibrahim Hamidah Universiti Putra Malaysia, Malaysia Wook-Shin Han Kyungpook National University, Korea Takahiro Hara Osaka University, Japan Theo Härder TU Kaiserslautern, Germany Francisco Herrera University of Granada, Spain Steven Hoi Nanyang Technological University, Singapore Estevam Rafael Hruschka Jr. Carnegie Mellon University, USA Wynne Hsu National University of Singapore, Singapore Yu Hua Huazhong University of Science and Technology, China Jimmy Huang York University, Canada Xiaoyu Huang South China University of Technology, China
- 15. X Program Committee San-YihHwang National Sun Yat-Sen University, Taiwan Ionut Emil Iacob Georgia Southern University, USA Renato Iannella Semantic Identity, Australia Sergio Ilarri University of Zaragoza, Spain Abdessamad Imine University of Nancy, France Yoshiharu Ishikawa Nagoya University, Japan Mizuho Iwaihara Waseda University, Japan Adam Jatowt Kyoto University, Japan Peiquan Jin University of Science and Technology, China Ejub Kajan State University of Novi Pazar, Serbia Anne Kao Boeing Phantom Works, USA Stefan Katzenbeisser Technical University of Darmstadt, Germany Yiping Ke Chinese University of Hong Kong, Hong Kong Sang-Wook Kim Hanyang University, Korea Markus Kirchberg Hewlett-Packard Laboratories, Singapore Hiroyuki Kitagawa University of Tsukuba, Japan Carsten Kleiner University of Applied Sciences and Arts Hannover, Germany Ibrahim Korpeoglu Bilkent University, Turkey Harald Kosch University of Passau, Germany Michal Krátký VSB-Technical University of Ostrava, Czech Republic Arun Kumar IBM India Research Lab., India Ashish Kundu Purdue University, USA Josef Küng University of Linz, Austria Kwok-Wa Lam University of Hong Kong, Hong Kong Nadira Lammari CNAM, France Gianfranco Lamperti University of Brescia, Italy Anne Laurent LIRMM, Université Montpellier 2, France Mong Li Lee National University of Singapore, Singapore Alain Toinon Leger Orange - France Telecom R&D, France Daniel Lemire Université du Québec à Montréal, Canada Pierre Lévy Public Health Department, France Lenka Lhotska Czech Technical University, Czech Republic Wenxin Liang Dalian University of Technology, China Stephen W. Liddle Brigham Young University, USA Lipyeow Lim IBM T.J. Watson Research Center, USA Tok Wang Ling National University of Singapore, Singapore Sebastian Link Victoria University of Wellington, New Zealand Volker Linnemann University of Lübeck, Germany Chengfei Liu Swinburne University of Technology, Australia Chuan-Ming Liu National Taipei University of Technology, Taiwan Fuyu Liu University of Central Florida, USA
- 16. Program Committee XI Hong-CheuLiu Diwan University, Taiwan Hua Liu Xerox Research Center at Webster, USA Jorge Lloret Gazo University of Zaragoza, Spain Miguel Ángel López Carmona University of Alcalá de Henares, Spain Peri Loucopoulos Loughborough University, UK Chang-Tien Lu Virginia Tech, USA Jianguo Lu University of Windsor, Canada Alessandra Lumini University of Bologna, Italy Cheng Luo Coppin State University, USA Hui Ma Victoria University of Wellington, New Zealand Qiang Ma Kyoto University, Japan Stéphane Maag TELECOM & Management SudParis, France Nikos Mamoulis University of Hong Kong, Hong Kong Vladimir Marik Czech Technical University, Czech Republic Pierre-Francois Marteau Université de Bretagne Sud, France Elio Masciari ICAR-CNR, Italy Norman May SAP Research Center, Germany Jose-Norberto Mazon University of Alicante, Spain Dennis McLeod University of Southern California, USA Brahim Medjahed University of Michigan - Dearborn, USA Harekrishna Misra Institute of Rural Management Anand, India Jose Mocito INESC-ID/FCUL, Portugal Lars Mönch FernUniversität in Hagen, Germany Riad Mokadem IRIT, Paul Sabatier University, France Yang-Sae Moon Kangwon National University, Korea Reagan Moore San Diego Supercomputer Center, USA Franck Morvan IRIT, Paul Sabatier University, Toulouse, France Mirco Musolesi University of Cambridge, UK Ismael Navas-Delgado University of Málaga, Spain Wilfred Ng University of Science and Technology, Hong Kong Javier Nieves Acedo Deusto University, Spain Selim Nurcan University Paris 1 Pantheon Sorbonne, France Mehmet Orgun Macquarie University, Australia Mourad Oussalah University of Nantes, France Gultekin Ozsoyoglu Case Western Reserve University, USA George Pallis University of Cyprus, Cyprus Christos Papatheodorou Ionian University, Corfu, Greece Marcin Paprzycki Polish Academy of Sciences, Warsaw Management Academy, Poland Oscar Pastor Universidad Politecnica de Valencia, Spain Jovan Pehcevski MIT University, Skopje, Macedonia Reinhard Pichler Technische Universität Wien, Austria Clara Pizzuti CNR, ICAR, Italy
- 17. XII Program Committee JaroslavPokorny Charles University in Prague, Czech Republic Giuseppe Polese University of Salerno, Italy Pascal Poncelet LIRMM, France Elaheh Pourabbas National Research Council, Italy Xiaojun Qi Utah State University, USA Gerald Quirchmayr University of Vienna, Austria and University of South Australia, Australia Fausto Rabitti ISTI, CNR Pisa, Italy Claudia Raibulet Università degli Studi di Milano-Bicocca, Italy Isidro Ramos Technical University of Valencia, Spain Praveen Rao University of Missouri-Kansas City, USA Rodolfo F. Resende Federal University of Minas Gerais, Brazil Claudia Roncancio Grenoble University / LIG, France Edna Ruckhaus Universidad Simon Bolivar, Venezuela Massimo Ruffolo University of Calabria, Italy Igor Ruiz Agúndez Deusto University, Spain Giovanni Maria Sacco University of Turin, Italy Shazia Sadiq University of Queensland, Australia Simonas Saltenis Aalborg University, Denmark Demetrios G Sampson University of Piraeus, Greece Carlo Sansone Università di Napoli “Federico II”, Italy Igor Santos Grueiro Deusto University, Spain Ismael Sanz Universitat Jaume I, Spain N.L. Sarda I.I.T. Bombay, India Marinette Savonnet University of Burgundy, France Raimondo Schettini Università degli Studi di Milano-Bicocca, Italy Klaus-Dieter Schewe Software Competence Centre Hagenberg, Austria Erich Schweighofer University of Vienna, Austria Florence Sedes IRIT Toulouse, France Nazha Selmaoui University of New Caledonia, France Patrick Siarry Université Paris 12 (LiSSi), France Gheorghe Cosmin Silaghi Babes-Bolyai University of Cluj-Napoca, Romania Leonid Sokolinsky South Ural State University, Russia Bala Srinivasan Monash University, Australia Umberto Straccia Italian National Research Council, Italy Darijus Strasunskas Norwegian University of Science and Technology (NTNU), Norway Lena Stromback Linköpings Universitet, Sweden Aixin Sun Nanyang Technological University, Singapore Raj Sunderraman Georgia State University, USA Ashish Sureka Infosys Technologies Limited, India David Taniar Monash University, Australia Cui Tao Brigham Young University, USA
- 18. Program Committee XIII MaguelonneTeisseire LIRMM, Université Montpellier 2, France Sergio Tessaris Free University of Bozen-Bolzano, Italy Olivier Teste IRIT, University of Toulouse, France Stephanie Teufel University of Fribourg, Switzerland Jukka Teuhola University of Turku, Finland Taro Tezuka Ritsumeikan University, Japan Bernhard Thalheim Christian-Albrechts-Universität Kiel, Germany J.M. Thevenin University of Toulouse, France Philippe Thiran University of Namur, Belgium Helmut Thoma University of Basel, Switzerland A. Min Tjoa Technical University of Vienna, Austria Vicenc Torra Universitat Autonoma de Barcelona , Spain Farouk Toumani Université Blaise Pascal, France Traian Truta Northern Kentucky University, USA Vassileios Tsetsos National and Kapodistrian University of Athens, Greece Theodoros Tzouramanis University of the Aegean, Greece Roberto Uribeetxebarria Mondragon University, Spain Genoveva Vargas-Solar LSR-IMAG, France Maria Vargas-Vera The Open University, UK Krishnamurthy Vidyasankar Memorial University of Newfoundland, Canada Marco Vieira University of Coimbra, Portugal Johanna Völker University of Mannheim, Germany Jianyong Wang Tsinghua University, China Junhu Wang Griffith University, Brisbane, Australia Kewen Wang Griffith University, Brisbane, Australia Qing Wang University of Otago, Dunedin, New Zealand Wei Wang University of New South Wales, Sydney, Australia Wendy Hui Wang Stevens Institute of Technology, USA Andreas Wombacher University of Twente, The Netherlands Lai Xu SAP Research, Switzerland Hsin-Chang Yang National University of Kaohsiung, Taiwan Ming Hour Yang Chung Yuan Christian University, Taiwan Xiaochun Yang Northeastern University, China Haruo Yokota Tokyo Institute of Technology, Japan Zhiwen Yu Northwestern Polytechnical University, China Xiao-Jun Zeng University of Manchester, UK Zhigang Zeng Huazhong University of Science and Technology, China Xiuzhen (Jenny) Zhang RMIT University Australia, Australia Yanchang Zhao University of Technology, Sydney, Australia Yu Zheng Microsoft Research Asia, China Xiaofang Zhou University of Queensland, Australia Qiang Zhu The University of Michigan, USA
- 19. XIV Program Committee YanZhu Southwest Jiaotong University, Chengdu, China Urko Zurutuza Mondragon University, Spain External Reviewers Mohamad Othman Abdallah University of Grenoble, LIG, France Sandra de Amo University of Uberlandia, Brazil Laurence Rodrigues do Amaral Federal University of Goias, Brazil Sandra de Amo Federal University of Uberlandia, Brazil Diego Arroyuelo Yahoo! Research Latin America, Chile Tigran Avanesov INRIA Nancy Grand Est, France Ali Bahrami Boeing, USA Zhifeng Bao National University of Singapore, Singapore Devis Bianchini University of Brescia, Italy Souad Boukhadouma University of USTHB, Algeria Paolo Bucciol UDLAP, LAFMIA, Mexico Diego Ceccarelli ISTI-CNR Pisa, Italy Camelia Constantin Université Pierre et Marie Curie-Paris VI, Paris, France Yan Da University of Science and Technology, Hong Kong Matthew Damigos NTUA, Greece Franca Debole ISTI-CNR Pisa, Italy Paul de Vreize Bournemouth University, UK Raimundo F. DosSantos Virginia Tech, USA Gilles Dubois MODEME, University of Lyon-Jean Moulin Lyon 3, France Qiong Fang University of Science and Technology, Hong Kong Fabio Fassetti DEIS, University of Calabria, Italy Ingo Feinerer Vienna University of Technology, Austria Daniel Fleischhacker University of Mannheim, Germany Fausto Fleites Florida International University, USA Filippo Furfaro DEIS, University of Calabria, Italy Claudio Gennaro ISTI-CNR Pisa, Italy Panagiotis Gouvas Ubitech EU Carmine Gravino University of Salerno, Italy Hsin-Yu Ha Florida International University, USA Allel Hadj-Ali IRISA-University of Rennes 1, France Duong Hoang Vienna University of Technology, Austria Enrique de la Hoz Universidad de Alcala, Spain Hai Huang Swinburne University of Technology, Australia Gilles Hubert IRIT, Université Paul Sabatier, Université de Toulouse, France Mohammad Rezwanul Huq University of Twente, The Netherlands
- 20. Program Committee XV SaifulIslam Swinburne University of Technology, Australia Min-Hee Jang Hanyang University, Korea Hélène Jaudoin IRISA-University of Rennes 1, France Lili Jiang Lanzhou University, China Selma Khouri ESI, Algeria Emin Yigit Koksal Bilkent University, Turkey Theodorich Kopetzky SCCH - Software Competence Center Hagenberg, Austria Olivier le-Goaer University of Pau, France Paea LePendu Stanford University, USA Kenneth Leung University of Science and Technology, Hong Kong Wenxin Liang Dalian University of Technology, China Haishan Liu University of Oregon, USA Rosanne Liu The University of Michigan - Dearborn, USA Xuezheng Liu Google Inc., USA Xutong Liu Virginia Tech, USA Yannick Loiseau LIMOS, Blaise Pascal University, France Carlos Manuel López Enrı́quez Grenoble INP - UDLAP, LIG-LAFMIA, France Min Luo Tokyo Institute of Technology, Japan Laura Maag Alcatel-Lucent Bell-Labs, France Lucrezia Macchia University of Bari, Italy Ivan Marsa-Maestre Universidad de Alcala, Spain Michele Melchiori University of Brescia, Italy Ruslan Miniakhmetov South Ural State University, Chelyabinsk, Russia Ronald Ocampo Florida International University, USA Anna Oganyan Georgia Southern University, USA Ermelinda Oro ICAR-CNR, Italy Francesco Pagliarecci Università Politecnica delle Marche, Italy Constantin Pan South Ural State University, Chelyabinsk, Russia Vassia Pavlaki NTUA, Greece Olivier Pivert IRISA-University of Rennes 1, France Xu Pu Tsinghua University, China Gang Qian University of Central Oklahoma, USA Michele Risi University of Salerno, Italy Flavio Rizzolo Business Intelligence Network (BIN), Canada Edimilson Batista dos Santos Federal University of Rio de Janeiro, Brazil Federica Sarro University of Salerno, Italy Vadim Savenkov Vienna University of Technology, Austria Wei Shen Tsinghua University, China Grégory Smits IRISA-University of Rennes 1, France Sofia Stamou Ionian University, Corfu, Greece
- 21. XVI Program Committee LubomirStanchev Indiana University - Purdue University Fort Wayne, USA Chad M.S. Steel Virginia Tech, USA Thomas Stocker University of Freiburg, Germany Nick Amirreza Tahamtan Vienna University of Technology, Austria Guilaine Talens MODEME, University of Lyon-Jean Moulin Lyon 3, France Lu-An Tang University of Illinois at Urbana Champaign, USA Caglar Terzi Bilkent University, Turkey Gabriel Tolosa National University of Lujan, Argentina Claudio Vairo ISTI-CNR Pisa, Italy Changzhou Wang Boeing, USA Yousuke Watanabe Tokyo Institute of Technology, Japan Andreas Weiner University of Kaiserslautern, Germany Yimin Yang Florida International University, USA Soek-Ho Yoon Hanyang University, Korea Sira Yongchareon Swinburne University of Technology, Australia Tomoki Yoshihisa Osaka University, Japan Jing Yuan University of Science and Technology of China, China Chao Zhu The University of Michigan - Dearborn, USA Mikhail Zymbler South Ural State University, Chelyabinsk, Russia
- 22. Table of Contents– Part II XML Querying and Views On Equivalence and Rewriting of XPath Queries Using Views under DTD Constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Pantelis Aravogliadis and Vasilis Vassalos Incremental Maintenance of Materialized XML Views. . . . . . . . . . . . . . . . . 17 Leonidas Fegaras Ingredients for Accurate, Fast, and Robust XML Similarity Joins . . . . . . 33 Leonardo Andrade Ribeiro and Theo Härder Twig Pattern Matching: A Revisit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 Jiang Li, Junhu Wang, and Maolin Huang Boosting Twig Joins in Probabilistic XML . . . . . . . . . . . . . . . . . . . . . . . . . . 51 Siqi Liu and Guoren Wang Data Mining Prediction of Cerebral Aneurysm Rupture Using Hemodynamic, Morphologic and Clinical Features: A Data Mining Approach . . . . . . . . . . 59 Jesus Bisbal, Gerhard Engelbrecht, Mari-Cruz Villa-Uriol, and Alejandro F. Frangi Semantic Translation for Rule-Based Knowledge in Data Mining . . . . . . . 74 Dejing Dou, Han Qin, and Haishan Liu Mining Frequent Disjunctive Selection Queries . . . . . . . . . . . . . . . . . . . . . . . 90 Inès Hilali-Jaghdam, Tao-Yuan Jen, Dominique Laurent, and Sadok Ben Yahia A Temporal Data Mining Framework for Analyzing Longitudinal Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 Corrado Loglisci, Michelangelo Ceci, and Donato Malerba How to Use “Classical” Tree Mining Algorithms to Find Complex Spatio-Temporal Patterns? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 Nazha Selmaoui-Folcher and Frédéric Flouvat
- 23. XVIII Table ofContents – Part II Queries and Search Inferring Fine-Grained Data Provenance in Stream Data Processing: Reduced Storage Cost, High Accuracy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118 Mohammad Rezwanul Huq, Andreas Wombacher, and Peter M.G. Apers Approximate Query on Historical Stream Data . . . . . . . . . . . . . . . . . . . . . . 128 Qiyang Duan, Peng Wang, MingXi Wu, Wei Wang, and Sheng Huang An Incremental Approach to Closest Pair Queries in Spatial Networks Using Best-First Search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136 Chunan Chen, Weiwei Sun, Baihua Zheng, Dingding Mao, and Weimo Liu Fast Top-K Query Answering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144 Claus Dabringer and Johann Eder Towards an On-Line Analysis of Tweets Processing . . . . . . . . . . . . . . . . . . . 154 Sandra Bringay, Nicolas Béchet, Flavien Bouillot, Pascal Poncelet, Mathieu Roche, and Maguelonne Teisseire The Fix-Point Method for Discrete Events Simulation Using SQL and UDF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162 Qiming Chen, Meichun Hsu, and Bin Zhang Semantic Web Approximate and Incremental Processing of Complex Queries against the Web of Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171 Thanh Tran, Günter Ladwig, and Andreas Wagner Conjunctive Query Optimization in OWL2-DL. . . . . . . . . . . . . . . . . . . . . . . 188 Petr Křemen and Zdeněk Kouba RoSeS: A Continuous Content-Based Query Engine for RSS Feeds . . . . . 203 Jordi Creus Tomàs, Bernd Amann, Nicolas Travers, and Dan Vodislav Information Retrieval The Linear Combination Data Fusion Method in Information Retrieval . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219 Shengli Wu, Yaxin Bi, and Xiaoqin Zeng Approaches and Standards for Metadata Interoperability in Distributed Image Search and Retrieval . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 234 Ruben Tous, Jordi Nin, Jaime Delgado, and Pere Toran
- 24. Table of Contents– Part II XIX A Distributed Architecture for Flexible Multimedia Management and Retrieval . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 249 Mihaela Brut, Dana Codreanu, Stefan Dumitrescu, Ana-Maria Manzat, and Florence Sedes Business Applications Deontic BPMN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 264 Christine Natschläger Improving Stock Market Prediction by Integrating Both Market News and Stock Prices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 279 Xiaodong Li, Chao Wang, Jiawei Dong, Feng Wang, Xiaotie Deng, and Shanfeng Zhu Querying Semantically Enriched Business Processes . . . . . . . . . . . . . . . . . . 294 Michele Missikoff, Maurizio Proietti, and Fabrizio Smith Introducing Affective Agents in Recommendation Systems Based on Relational Data Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 303 João C. Xavier-Junior, Alberto Signoretti, Anne M.P. Canuto, Andre M. Campos, Luiz M.G. Gonçalves, and Sergio V. Fialho Converting Conversation Protocols Using an XML Based Differential Behavioral Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 311 Claas Busemann and Daniela Nicklas User Support Facilitating Casual Users in Interacting with Linked Data through Domain Expertise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319 Cormac Hampson and Owen Conlan Using Expert-Derived Aesthetic Attributes to Help Users in Exploring Image Databases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 334 Cormac Hampson, Meltem Gürel, and Owen Conlan Indexing SkyMap: A Trie-Based Index Structure for High-Performance Skyline Query Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 350 Joachim Selke and Wolf-Tilo Balke A Path-Oriented RDF Index for Keyword Search Query Processing. . . . . 366 Paolo Cappellari, Roberto De Virgilio, Antonio Maccioni, and Mark Roantree
- 25. XX Table ofContents – Part II Variable Length Compression for Bitmap Indices . . . . . . . . . . . . . . . . . . . . . 381 Fabian Corrales, David Chiu, and Jason Sawin Queries, Views and Data Warehouses Modeling View Selection as a Constraint Satisfaction Problem . . . . . . . . . 396 Imene Mami, Remi Coletta, and Zohra Bellahsene Enabling Knowledge Extraction from Low Level Sensor Data . . . . . . . . . . 411 Paolo Cappellari, Jie Shi, Mark Roantree, Crionna Tobin, and Niall Moyna Join Selectivity Re-estimation for Repetitive Queries in Databases. . . . . . 420 Feng Yu, Wen-Chi Hou, Cheng Luo, Qiang Zhu, and Dunren Che Matching Star Schemas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 428 Dariush Riazati and James A. Thom Ontologies Automated Construction of Domain Ontology Taxonomies from Wikipedia 439 Damir Jurić, Marko Banek, and Zoran Skočir Storing Fuzzy Ontology in Fuzzy Relational Database . . . . . . . . . . . . . . . . 447 Fu Zhang, Z.M. Ma, Li Yan, and Jingwei Cheng Using an Ontology to Automatically Generate Questions for the Determination of Situations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 456 Marten Teitsma, Jacobijn Sandberg, Marinus Maris, and Bob Wielinga Physical Aspects of Databases Indexing Frequently Updated Trajectories of Network-Constrained Moving Objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 464 Zhiming Ding Online Index Selection in RDBMS by Evolutionary Approach. . . . . . . . . . 475 Piotr Ko laczkowski and Henryk Rybiński Towards Balanced Allocations for DHTs . . . . . . . . . . . . . . . . . . . . . . . . . . . . 485 George Tsatsanifos and Vasilis Samoladas Caching Stars in the Sky: A Semantic Caching Approach to Accelerate Skyline Queries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 493 Arnab Bhattacharya, B. Palvali Teja, and Sourav Dutta
- 26. Table of Contents– Part II XXI Design Generating Synthetic Database Schemas for Simulation Purposes . . . . . . 502 Carlos Eduardo Pires, Priscilla Vieira, Márcio Saraiva, and Denilson Barbosa A New Approach for Fuzzy Classification in Relational Databases . . . . . . 511 Ricardo Hideyuki Tajiri, Eduardo Zanoni Marques, Bruno Bogaz Zarpelão, and Leonardo de Souza Mendes Anomaly Detection for the Prediction of Ultimate Tensile Strength in Iron Casting Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 519 Igor Santos, Javier Nieves, Xabier Ugarte-Pedrero, and Pablo G. Bringas Distribution LinkedPeers: A Distributed System for Interlinking Multidimensional Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 527 Athanasia Asiki, Dimitrios Tsoumakos, and Nectarios Koziris A Vertical Partitioning Algorithm for Distributed Multimedia Databases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 544 Lisbeth Rodriguez and Xiaoou Li Diffusion in Dynamic Social Networks: Application in Epidemiology . . . . 559 Erick Stattner, Martine Collard, and Nicolas Vidot Miscellaneous Topics Probabilistic Quality Assessment Based on Article’s Revision History . . . 574 Jingyu Han, Chuandong Wang, and Dawei Jiang Propagation of Multi-granularity Annotations . . . . . . . . . . . . . . . . . . . . . . . 589 Ryo Aoto, Toshiyuki Shimizu, and Masatoshi Yoshikawa Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 605
- 28. Table of Contents– Part I Keynote Talks Principles of Distributed Data Management in 2020? . . . . . . . . . . . . . . . . . 1 Patrick Valduriez The Science of Conceptual Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 Bernhard Thalheim Probabilistic Logics in Expert Systems: Approaches, Implementations, and Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 Gabriele Kern-Isberner, Christoph Beierle, Marc Finthammer, and Matthias Thimm Query Processing Multi-objective Optimal Combination Queries . . . . . . . . . . . . . . . . . . . . . . . 47 Xi Guo and Yoshiharu Ishikawa NEFOS: Rapid Cache-Aware Range Query Processing with Probabilistic Guarantees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 Spyros Sioutas, Kostas Tsichlas, Ioannis Karydis, Yannis Manolopoulos, and Yannis Theodoridis Reuse-Oriented Mapping Discovery for Meta-querier Customization . . . . 78 Xiao Li and Randy Chow Database Semantics Attribute Grammar for XML Integrity Constraint Validation . . . . . . . . . . 94 Béatrice Bouchou, Mirian Halfeld Ferrari, and Maria Adriana Vidigal Lima Extracting Temporal Equivalence Relationships among Keywords from Time-Stamped Documents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110 Parvathi Chundi, Mahadevan Subramaniam, and R.M. Aruna Weerakoon Codd Table Representations under Weak Possible World Semantics. . . . . 125 Flavio Ferrarotti, Sven Hartmann, Van Bao Tran Le, and Sebastian Link
- 29. XXIV Table ofContents – Part I Skyline Queries Efficient Early Top-k Query Processing in Overloaded P2P Systems . . . . 140 William Kokou Dédzoé, Philippe Lamarre, Reza Akbarinia, and Patrick Valduriez Top-k Query Evaluation in Sensor Networks with the Guaranteed Accuracy of Query Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156 Baichen Chen, Weifa Liang, and Geyong Min Learning Top-k Transformation Rules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172 Sunanda Patro and Wei Wang Security and Privacy Privacy beyond Single Sensitive Attribute . . . . . . . . . . . . . . . . . . . . . . . . . . . 187 Yuan Fang, Mafruz Zaman Ashrafi, and See Kiong Ng Privacy-Aware DaaS Services Composition . . . . . . . . . . . . . . . . . . . . . . . . . . 202 Salah-Eddine Tbahriti, Michael Mrissa, Brahim Medjahed, Chirine Ghedira, Mahmoud Barhamgi, and Jocelyne Fayn An Empirical Study on Using the National Vulnerability Database to Predict Software Vulnerabilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217 Su Zhang, Doina Caragea, and Xinming Ou Spatial and Temporal Data Similar Subsequence Search in Time Series Databases. . . . . . . . . . . . . . . . . 232 Shrikant Kashyap, Mong Li Lee, and Wynne Hsu Optimizing Predictive Queries on Moving Objects under Road-Network Constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247 Lasanthi Heendaliya, Dan Lin, and Ali Hurson Real-Time Capable Data Management Architecture for Database-Driven 3D Simulation Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . 262 Jürgen Roßmann, Michael Schluse, Ralf Waspe, and Martin Hoppen Collecting and Managing Network-Matched Trajectories of Moving Objects in Databases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 270 Zhiming Ding and Ke Deng On Guaranteeing k-Anonymity in Location Databases . . . . . . . . . . . . . . . . 280 Anh Tuan Truong, Tran Khanh Dang, and Josef Küng
- 30. Table of Contents– Part I XXV Semantic Web Search Smeagol: A “Specific-to-General” Semantic Web Query Interface Paradigm for Novices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 288 Aaron Clemmer and Stephen Davies Browsing-Oriented Semantic Faceted Search . . . . . . . . . . . . . . . . . . . . . . . . . 303 Andreas Wagner, Günter Ladwig, and Thanh Tran An Efficient Algorithm for Topic Ranking and Modeling Topic Evolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 320 Kumar Shubhankar, Aditya Pratap Singh, and Vikram Pudi Sampling the National Deep Web . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 331 Denis Shestakov A Bipartite Graph Model and Mutually Reinforcing Analysis for Review Sites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 341 Kazuki Tawaramoto, Junpei Kawamoto, Yasuhito Asano, and Masatoshi Yoshikawa Storage and Search Genetic Algorithm for Finding Cluster Hierarchies . . . . . . . . . . . . . . . . . . . 349 Christian Böhm, Annahita Oswald, Christian Richter, Bianca Wackersreuther, and Peter Wackersreuther A File Search Method Based on Intertask Relationships Derived from Access Frequency and RMC Operations on Files . . . . . . . . . . . . . . . . . . . . . 364 Yi Wu, Kenichi Otagiri, Yousuke Watanabe, and Haruo Yokota A General Top-k Algorithm for Web Data Sources . . . . . . . . . . . . . . . . . . . 379 Mehdi Badr and Dan Vodislav Improving the Quality of Web Archives through the Importance of Changes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 394 Myriam Ben Saad and Stéphane Gançarski Web Search Alternative Query Generation for XML Keyword Search and Its Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 410 Tetsutaro Motomura, Toshiyuki Shimizu, and Masatoshi Yoshikawa K-Graphs: Selecting Top-k Data Sources for XML Keyword Queries . . . . 425 Khanh Nguyen and Jinli Cao
- 31. XXVI Table ofContents – Part I Detecting Economic Events Using a Semantics-Based Pipeline . . . . . . . . . 440 Alexander Hogenboom, Frederik Hogenboom, Flavius Frasincar, Uzay Kaymak, Otto van der Meer, and Kim Schouten Edit Distance between XML and Probabilistic XML Documents . . . . . . . 448 Ruiming Tang, Huayu Wu, Sadegh Nobari, and Stéphane Bressan Towards an Automatic Characterization of Criteria. . . . . . . . . . . . . . . . . . . 457 Benjamin Duthil, François Trousset, Mathieu Roche, Gérard Dray, Michel Plantié, Jacky Montmain, and Pascal Poncelet Data Integration, Transactions and Optimization A Theoretical and Experimental Comparison of Algorithms for the Containment of Conjunctive Queries with Negation . . . . . . . . . . . . . . . . . . 466 Khalil Ben Mohamed, Michel Leclère, and Marie-Laure Mugnier Data Integration over NoSQL Stores Using Access Path Based Mappings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 481 Olivier Curé, Robin Hecht, Chan Le Duc, and Myriam Lamolle An Energy-Efficient Concurrency Control Algorithm for Mobile Ad-Hoc Network Databases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 496 Zhaowen Xing and Le Gruenwald Web Applications An Ontology-Based Method for Duplicate Detection in Web Data Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 511 Patrice Buche, Juliette Dibie-Barthélemy, Rania Khefifi, and Fatiha Saı̈s Approaches for Semantically Annotating and Discovering Scientific Observational Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 526 Huiping Cao, Shawn Bowers, and Mark P. Schildhauer A Scalable Tag-Based Recommender System for New Users of the Social Web . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 542 Valentina Zanardi and Licia Capra Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 559
- 32. On Equivalence andRewriting of XPath Queries Using Views under DTD Constraints Pantelis Aravogliadis and Vasilis Vassalos Department of Informatics, Athens University of Economics and Business, Athens, Greece Abstract. It has long been recognized that query rewriting techniques are important tools for query optimization and semantic caching and are at the heart of data integration systems. In particular, the problem of rewriting queries using view definitions has received a lot of attention in these contexts. At the same time, the XPath language has become very popular for processing XML data, and there is much recent progress in semantic XPath optimization problems, such as XPath containment, and, more recently, XPath rewriting using views. In this paper we address the open problems of finding equivalent query rewritings using views for XPath queries and views that include the child, predicate and wildcard features (i.e., they are in XP(/, [], ∗)) under DTD constraints. In the process, we also develop novel containment tests for queries in XP(/, [], ∗) under DTD constraints. Keywords: XML data, XPath processing, query rewriting using views. 1 Introduction XML has become a widely used standard for data representation and exchange over the Internet. To address the increasing need to query and manipulate XML data, the W3C has proposed the XML query language XQuery [7]. XPath [6] is the XQuery subset for navigating XML documents, and is designed to be embed- ded in a host language such as XQuery. XPath expressions often define compli- cated navigation, resulting in expensive query processing. Numerous techniques to speed up evaluation of XPath queries have been developed (e.g. [8]). Rewriting queries using materialized views is known to provide a powerful tool for query optimization, semantic caching, and data integration, for both relational and semistructured data (e.g, see [12,19]), and consequently the rewriting challenge for XPath queries and views has started receiving more attention [1,3,14,23,26]. The rewriting query using views problem is traditionally formulated in two different ways. In the equivalent rewriting formulation, the problem is, given a query q and a view v, to find a rewriting of q using v that is equivalent to q. This formulation is motivated by classical query optimization and solutions to the problem address the problem of speeding up XPath query evaluation [1,3,26]. The maximally-contained rewriting problem is the problem of finding, given a query q and a view v, a rewriting of q using v that is contained in q and is A. Hameurlain et al. (Eds.): DEXA 2011, Part II, LNCS 6861, pp. 1–16, 2011. c Springer-Verlag Berlin Heidelberg 2011
- 33. 2 P. Aravogliadisand V. Vassalos maximal. This problem arises mostly in the context of information integration, where we cannot always find an equivalent rewriting due to limited coverage of data sources [14,23]. Even though there is important progress in the equivalent rewriting problem for various fragments of XPath (see [1,26] and also Sections 3 and 5 for more discussion), existing work assumes the absence of schema constraints. Given the many business, scientific and other uses of XML where schema information is widely available, the problem of equivalent query rewriting in the presence of a schema is at least equally important. Using Document Type Definitions (DTDs) as our schema language, we present the following results: – Novel sound and complete algorithms for the containment and the query rewriting problem for XPath queries and views in XP(/,[],*) under a duplicate- free DTD or an acyclic and choice-free DTD. The algorithms are based on a novel containment test relying on the extraction of appropriate constraints from the DTD. – While the above mentioned algorithm is in PTIME for choice-free DTD, it is in EXPTIME for duplicate-free DTD. Hence we also develop sound polynomial algorithms for the containment and the XPath query rewriting using views problems for XPath queries and views in XP(/,[],*) under a duplicate-free DTD. The algorithms are shown to be complete when the query meets a specific condition, namely its C-satisfying set (section 4.1) is not empty. The paper is organized as follows: Section 2 reviews the necessary material con- cerning XPath, tree pattern queries and techniques for XPath containment. The problem of XPath query rewriting is formally defined in Section 3, where known results (in the absence of schema information) are also discussed. Section 4 devel- ops the containment tests for XP(/, [], ∗) as well as the two rewriting algorithms mentioned above. Section 5 presents the related work, and we conclude with Section 6 where we discuss ongoing and future work. 2 XPath, DTD and XPath Containment We first give some definitions for the XPath language and DTDs. Then we discuss the XPath query containment problem. The definitions follow closely [15] and [26]. 2.1 XPath and Tree Pattern Queries XML documents are modeled as rooted, ordered, unranked node-labeled trees, called XML trees, over an infinite alphabet S. We denote all XML trees over S as TS. Given an XML tree t, following [26], for any node n in t we denote the subtree of t rooted at n as (t)n sub.
- 34. On Equivalence andRewriting of XPath Queries 3 Every XPath query in XP(/, //, [], ∗) can be represented as a labeled tree, called a tree pattern, which we define in Definition 1. Definition 1. A tree pattern p is a tree Vp, Ep, rp, op over S ∪ {∗}, where Vp is the node set and Ep is the edge set, and: (i) Each node n ∈ Vp has a label from S ∪ {∗}, denoted as n.label, (ii) Each edge e ∈ Ep has a label from { / , // }, denoted as e.label, and (iii) rp, op ∈ Vp are the root and the selection node of p respectively. An edge with label / is called a child edge, otherwise a descendant edge. Given a pattern pVp, Ep, rt, op we say that p Vp , Ep , rp , op is a subpattern of p if the following conditions hold: (1) Vp ⊆ Vp, (2) Ep = (Vp × Vp ) ∩ Ep, and (3) if op ∈ Vp , then op is also the selection node of p . We denote with (p)n sub the subpattern of p rooted at node n ∈ Vp and containing all descendants of n. We next provide the definition of the notion of embedding from a tree pattern to an XML tree. Definition 2. [15] Given an XML tree tVt, Et, rt and a pattern pVp, Ep, rp, op, an embedding from p to t is a function e : Vp → Vt, with the following properties: (i) root preserving: e(rp) = rt, (ii) label preserving: ∀n ∈ Vp, if n.label = ∗ , then n.label = e(n).label, and (iii) structure preserving: ∀e = (n1, n2) ∈ Ep, if e .label = / then e(n2) is a child of e(n1) in t ; otherwise e(n2) is a descendant of e(n1) in t. Each embedding maps the selection node op of p to a node n in t. We say that the subtree (t)n sub of t is the result of the embedding. We can now define the result of evaluating the tree pattern query p over an XML tree t. Definition 3. Given a tree pattern query p and an XML tree t, then the result of evaluating p over t, denoted as p(t), is defined as: p(t) = ∪e∈EB{(t) e(op) sub }, where EB is the set of all embeddings from p to t. 2.2 Schema and DTDs DTDs provide a means for typing, and therefore constraining, the structure of XML documents. We provide a formal definition of a DTD. Definition 4. A DTD D over a finite alphabet S consists of a start symbol, denoted as root(D), and a mapping for each symbol a ∈ S to a regular expres- sion over S. Every such mapping is called a rule of the DTD and the regular expression corresponding to a is denoted as Ra . A tree t ∈ TS satisfies a DTD D over S if root(t).label = root(D).label and, for each node x in t with a sequence of children y1, . . . , yn, the string y1.label, . . ., yn.label is in L(Rx.label ), i.e., in the regular language described by the regular expression on the right-hand side of a rule with left-hand side x.label. The set of trees satisfying DTD D is denoted as SAT (D).
- 35. 4 P. Aravogliadisand V. Vassalos In the following, we will consider two categories of DTDs, duplicate-free DTDs and acyclic and choice-free DTDs. An acyclic and choice-free DTD does not contain alternation in any of its rules and also does not support recursion. A DTD is duplicate-free if in each right-hand side of any DTD rule each element name appears at most once. According to [17] most real-wold DTDs are duplicate-free DTDs. 2.3 XPath Query Containment XPath query containment is central to most query rewriting algorithms. We briefly discuss techniques applicable to this problem for subsets of the expressive XPath fragment XP(/, //, [].∗). We also present an overview of relevant results for XPath containment under DTD constraints, focusing especially on the use of the chase [14,25]. Table 1 in appendix shows known complexity results for the query containment problem with or without DTD constraints for several XPath fragments. Query Containment in the absence of schema. For an XML tree t and an XPath query q we have that t |= q if q(t) = ∅. We define the boolean XPath query containment problem. Definition 5. XPath query p is contained in q, denoted as p ⊆0 q, if and only if t |= p implies t |= q , for every XML tree t. According to [13] a more general setting of the containment problem, which is XPath query p is contained in q, denoted as p ⊆ q, if and only if p(t) ⊆ q(t) for every XML tree t, can be polynomially on the number of query nodes reduced to the boolean containment problem. Also, XPath query p is equivalent to q, denoted as p ≡ q, if and only if p ⊆ q and q ⊆ p. We can reason about query containment using the canonical models technique [15,20]. Although this technique provides a sound and complete algorithm for the containment problem in XP(/, //, [], ∗), it is exponential to the number of descendant edges of the query. On the contrary, the homomorphism technique [15,20] provides a sound and complete polynomial algorithm only for the con- tainment problem for the subfragments of XP(/, //, [], ∗), i.e. for XP(/, //, []), XP(/, //, ∗) and XP(/, [], ∗). We briefly describe the homomorphism technique as we will adapt and make use of it in what follows. Definition 6. A homomorphism from tree pattern q to tree pattern p is a func- tion h : nodes(q) → nodes(p) such that: (1) The root of q must be mapped to the root of p, (2) If (u, v) is a child-edge of q then (h(u), h(v)) is a child-edge in p, (3) If (u, v) is a descendant-edge of q then h(v) has to be below h(u) in p, and (4) if u is labeled with e = ∗ then h(u) also has to carry label e. The existence of a homomorphism is a necessary and sufficient condition for con- tainment decision in the subfragments of XP(/, //, [], ∗), though is only sufficient for fragment XP(/, //, [], ∗) [15].
- 36. On Equivalence andRewriting of XPath Queries 5 Query Containment with DTDs. In the presence of a DTD, the definition of containment between two XPath queries is as follows. Definition 7. Given two XPath queries p, q, then q D-contains p, written p ⊆SAT (D) q, if for every tree t ∈ SAT (D) it holds p(t) ⊆ q(t). The containment problem for XP(/, []) in the presence of a duplicate-free DTD is in PTIME [25] and for XP(/, //, []) under an acyclic and choice-free DTD is also in PTIME [14]. In order to decide efficiently the containment problem the chase technique [14,25] has been employed. The basic idea is to chase one of the two queries with a set of constraints extracted from the DTD and then decide containment between the queries with the homomorphism technique. Two of these constraints, which we use in section 4.1 are the Sibling (SC) and Functional (FC) Constraints [25]. An XML tree t satisfies the SC a: B ⇓ c if whenever a node labeled a in t has children labeled from set B then it has a child node labeled c. Also, t satisfies the FC a ↓ b if no node labeled a in t has two distinct children labeled with b. A chasing sequence of a query p by a set of Constraints C extracted from a DTD is a sequence p = p0, . . . , pk such that for each 0 ≤ i ≤ k − 1, pi+1 is the result of applying some constraint in C to pi , and no constraint can be applied to pk. We define the pk expression as the chase of p by C, denoted as chaseC(p). 3 XPath Query Rewriting We first provide the definition of the concatenation operator between two tree patterns, as in [26]. Definition 8. Given two tree pattern queries p, q then the concatenation op- erator, denoted as ⊕, when applied on them as p ⊕ q results in a tree pattern constructed by merging the root of p, denoted as rp, and the selection node of q, denoted as oq, into one node, provided that their labels are compatible that is (1) they carry the same label or (2) at least one of them is wildcard labeled. The merged node is labeled with their common label (case 1) or the non-wildcard label (case 2) and rq,op are the root and the selection node of p ⊕ q respectively. Given a tree pattern query q and a DTD D, then q is D-satisfiable if there is an XML tree t that satisfies D such that q(t) = ∅. We now give the definition of equivalent XPath query rewriting in the presence of a DTD. Definition 9. Given a DTD D, an XPath query q and a view v, which are D-satisfiable, the query rewriting problem is formulated as an: (1) Existence Problem: Decide if q is rewritable using view v, which is decide if a compensation query e exists s.t. e ⊕ v ≡SAT (D) q, or a (2) Construction Problem: Find this compensation query e and thus the equivalent rewriting is the XPath expression e ⊕ v 1 . 1 In the absence of intersection, we cannot use more than one view in a rewriting in this setting.
- 37. 6 P. Aravogliadisand V. Vassalos 3.1 Rewriting in the Absence of Schema To the best of our knowledge there is no existing work addressing the problem in Definition 9. However, the equivalent rewriting problem in the absence of schema is addressed by Xu and Özsoyoglu [26]. The following theorem from [26] provides a basis for deciding the equivalent rewriting existence problem. Theorem 1. Let u, p be two tree patterns in the subfragments of XP(/, //, [], ∗), and let the selection node of u be the i-th node in its selection path (i.e. the path from root of u to its selection node) while np be the i-th node in the selection path of p, then: if a compensation pattern p of p using u exist, the subpattern (p) np sub of p is a compensation pattern of p using u. Theorem 1 reduces the equivalent rewriting existence problem to the decision problem of whether (p) np sub ⊕ u ≡ p. Since for the subfragments of XP(/, //, [], ∗) the equivalence problem is in PTIME, the rewriting existence problem in these fragments is in PTIME. 4 Query Rewriting and Containment in the Presence of DTD In this section we address the query containment and the equivalent query rewrit- ing problem in the presence of a DTD for the XPath fragment XP(/, [], ∗). 4.1 The XPath Fragment XP (/, [], ∗) with Duplicate-Free DTD We discuss the containment and the equivalent rewriting problem for XPath expressions in XP(/, [], ∗) under the assumption that the considered DTDs are duplicate-free. In order to provide an efficient solution we will attempt to reduce the DTD into an appropriate set of constraints, thus reducing the necessary containment test under a DTD to a containment test under said constraints. Then we will develop the rewriting algorithm by again appropriately utilizing Theorem 1. Following this approach we will develop a sound algorithm. Getting a sound and complete algorithm will require a non-trivial extension, as shown in the remaining of section 4.1. Query Containment for XP (/, [], ∗). Containment of queries in XP(/, [], ∗) in the presence of a duplicate free DTD is CoNP-hard [17]. In this section we provide a PTIME algorithm for the containment problem that is always sound, and is complete when the query meets a particular condition (described in Defi- nition 10 and Theorem 2). Then we proceed to give an unconditional sound and complete EXPTIME algorithm. Towards the efficient solution we describe first the constraints that need to be inferred by a duplicate-free DTD D. Since the XPath fragment we consider includes only the child axis we will see that we need to infer only Sibling (SC), Functional (FC) [25] and the newly defined Unique child (UC) constraints. A
- 38. On Equivalence andRewriting of XPath Queries 7 Unique child constraint, denoted as UC: a → b, declares that if a node labeled a in an XML tree t has children, then all of them are labeled with b. Extracting a UC from a duplicate-free DTD is in PTIME since every rule a → b or a → b∗ from a DTD D yields the a → b UC. To handle the wildcard labeled nodes we define the corresponding wildcard versions of the previously mentioned constraints. Let S be the finite alphabet over which the DTD D is defined. From the set of SCs S ⊆ C, we extract the wildcard applicable SCs (WSC) as follows. If in S there are SCs of the form x: B ⇓ c for every x ∈ S such that x has children with labels in B, a WSC with the form ∗: B ⇓ c is derived. Also, from the set of FCs F ⊆ C, we extract the wildcard applicable FCs (WFC) as follows. If there are FCs in F of the form x ↓ b for every x ∈ S, a WFC of the form ∗ ↓ b is inferred . Finally, from the set of UCs UI ⊆ C, we extract the wildcard applicable UCs (WUC). If there are UCs in UI of the form x → b for every x ∈ S, a WUC the form ∗ → b is inferred. We observe that the alphabet S from which the labels of any tree t satisfying D come from is finite. Therefore, we note that the “options” for the wildcards in XPath expressions in XP(DT D, /, [], ∗) are finite (we will use this observation in the algorithms of Section 4.1). Furthermore, all the above constraints can be extracted in PTIME, just following the definitions and utilizing the PTIME extraction of SCs and FCs from duplicate-free DTDs [25]. In the following, we use terms FC, SC and UC meaning also the WFC, WSC and WUC constraints. Let p be a query in XP(/, [], ∗) and C be the set of SCs, FCs and UCs implied by a DTD D. We describe how to chase p applying these constraints inspired by [25]: Sibling Constraints: Let s ∈ C be a SC of the form a: B ⇓ c or a WSC of the form ∗: B ⇓ c, where B = {b1, . . . , bn}. Let u be a node of p with children v1, . . . , vn, such that u.label = a for the SC case or u.label = ∗ for the WSC case and vi.label = bi, 1 ≤ i ≤ n, and u does not have a child labeled c, then the chase result on p will be the adding of a child-edge to p between u and c. Functional Constraints: Let f ∈ C be a FC of the form a ↓ b or a WFC of the form ∗ ↓ b. Let u be a node of p with distinct children v and w, such that u.label = a for the FC case or u.label = ∗ for the WFC case and v.label = w.label = b, then the chase result on p is the merging of v, w of p. Unique Child Constraints: Let ui ∈ C be a UC of the form a → b or a WUC of the form ∗ → b. Let u be a node of p with distinct wildcard or b labeled children v1, . . . , vn (and for at least one vi, vi.label = b in the WUC case), such that u.label = a for the UC case or u.label = ∗ for the WUC case, then the chase result on p is turning the labels of v1, . . . , vn to b. Lemma 1. Let D be a duplicate-free DTD. Then q ≡SAT (D) chaseC(q). Moreover, containment of query q in p under the set of constraints C is reducible to containment of chaseC(q) in p.
- 39. 8 P. Aravogliadisand V. Vassalos Lemma 2. Let D be a duplicate-free DTD and C be the set of FCs, SCs and UCs implied by D. For XP(/, [], ∗) queries p and q it holds p ⊇SAT (C) q if and only if p ⊇ chaseC(q). Proof. See proof in [2]. Given the above, we would like to show that p ⊇SAT (D) q if and only if p ⊇SAT (C) q. As it turns out, this is not always the case for queries in XP(/, [], ∗). The following definition adopted from [25] helps in proving the required condition under which it holds that p ⊇SAT (D) q if and only if p ⊇SAT (C) q. Definition 10. [25] Let q be a D-satisfiable query in XP(/, [], ∗) in the presence of DTD D and R ⊆ SAT (D) be the set of trees with a subtree 1-1 homomorphic to chaseC(q). We call R the C-satisfying set for q. Each tree in R has a core subtree which is 1-1 homomorphic to chaseC(q) and each node in that core subtree is called core node. Each node which is not a core node is called a non-core node. Specifically, we have proven theorem 2 using lemma 3. Lemma 3. Let D be a duplicate-free DTD and p, q be queries in XP(/, [], ∗). Let q be D-satisfiable, C be the set of SCs, FCs and UCs implied by D, and R the C-satisfying set for q, such that R = ∅. If p ⊇SAT (D) q, then for each node w in p, either w can be mapped to a core node in every tree in R or w can be mapped to a non-core node in every tree in R. Theorem 2. Let D be a duplicate-free DTD and C be the set of FCs, SCs, UCs implied by D. Let p, q be queries in XP(/, [], ∗), such that the C-satisfying set for q R = ∅. Then p ⊇SAT (D) q if and only if p ⊇SAT (C) q. Proof. (if) Assume p ⊇SAT (C) q then p ⊇SAT (D) q, because SAT (C) ⊇ SAT (D). (only if) Assume p ⊇SAT (D) q but p ⊇SAT (C) q. We will derive a contradiction. Since there is an embedding from q to every t ∈ R and p ⊇SAT (D) q, then there is also an embedding from p to every tree t ∈ R. If p ⊇SAT (C) q then by lemma 2 there is no homomorphism from p to chaseC(q). We have the following two cases: Case A: a single path in p fails to map to any path in chaseC(q) : There is a node x in p with parent y, such that y is mapped to a node in chaseC(q) but no mapping from x to any node in chaseC(q) exists. We originally consider that node y does not map to a leaf in chaseC(q), which means that x.label can not be wildcard. So in any t ∈ R, x can never be mapped to a core node while y can always be mapped to a core node u. Lemma 3 and p ⊇SAT (D) q imply that x can always be mapped to a non-core node v in t ∈ R. Since v is a non-core node and x can not be mapped to a core node, D can not imply the SC u: B ⇓ v, where u and v are the nodes that y and x mapped in a t ∈ R and B is the set of core node children of u. The same holds, if node y is mapped to a wildcard labeled node in chaseC(q). This is because D can not imply the SC ∗: B ⇓ v, which means there is a node labeled u that D can not imply the SC u: B ⇓ v.
- 40. On Equivalence andRewriting of XPath Queries 9 So, there must be a tree U ∈ SAT (D) which has a node w labeled the same as u and children labeled from B, but with no child labeled as v node. Then, we can replace the non-core child subtrees of u by the non-core child subtrees of w and still have a tree t ∈ R. Node x can not be mapped to any non-core node in t . - A contradiction. If we consider that the path in p, which fails to map to any path in chaseC(q), is shorter from every path in chaseC(q) (i.e. y is mapped to a leaf node in chaseC(q)) then we have two cases: (i) For the non wildcard-labeled leaves in chaseC(q), D can not imply the SC u: ∅ ⇓ v, which means that either p is unsatisfiable or the arguments in the proof still hold, (ii) For the wildcard- labeled leafs in chaseC(q), D can not imply the SC ∗: ∅ ⇓ v, which means there is at least one node labeled say u that can not have as child a node labeled v. If all nodes can not have as child a node v, then p is unsatisfiable. If at least one node can have as child node v, then the proof’s arguments still hold. Case B: each path in p can map but for some node w which is common ancestor of nodes u and v, the two mapped paths for u and v in chaseC(q) can not be joint on w : Let wn be the common ancestor of u, v that is closest to the root of p, such that the path from root to u via wn (i.e. root → wn → u) maps to a path in chaseC(q) using homomorphism h1 and the path from root to v via wn (i.e. root → wn → v) maps to a path in chaseC(q) using homomorphism h2, but h1(wn) = h2(wn). Since chaseC(q) has a single root and h1(wn) = h2(wn), then none of these nodes labeled the same as wn is chaseC(q) root. Therefore, wo, the root of chaseC(q), is a parent of two children labeled the same as wn and for the root of p, say wpo, it holds h1(wpo) = h2(wpo) = wo. Case B1 wn.label = ∗ : Note that D can not imply the FC wo ↓ wn (since the wn-labeled nodes in chaseC(q) would have been merged) and that means that wo can have an unbounded number of children labeled the same as wn. Let t be the smallest tree in R, such that the node in t that corresponds to wo in chaseC(q) has two children nodes with labels the label of wn. Then there is no embedding from p to t. - A contradiction. Case B2 wn.label = ∗ : Note that D can not imply the UC wo → b and the FC wo ↓ wn, for some label b ∈ S, where S is the alphabet from D (since the wn-labeled nodes in chaseC(q) would have been merged) and that means that wo can have at least two children labeled from S. Let t be the smallest tree in R, such that the node in t that corresponds to wo in chaseC(q) has two children nodes with labels from S. Then there is no embedding from p to t. - A contradiction. Note that in theorem 2 the direction “if p ⊇SAT (D) q then p ⊇SAT (C) q” is trivial and always holds. The opposite direction requires R = ∅. Example 1 describes a case in which R = ∅ and the effects that this has in the decision of p ⊇SAT (D) q. Example 1. Let D be a duplicate-free DTD: a → (b, c), b → (i, j), c → (k), and let p ≡ /a[b[i][j]] and q ≡ /a[∗[i]][∗[j]] be two XPath expression in XP(/, [], ∗). It is obvious that chaseC(q) ≡ q and also that p ⊇ chaseC(q) since there is no homomorphism from p to chaseC(q). It is the case in the example that chaseC(q)
- 41. 10 P. Aravogliadisand V. Vassalos is not 1-1 homomorphic to any tree in SAT (D), or in other words R = ∅. Hence, theorem 2 does not hold. Indeed, while p ⊇SAT (D) q, according to the theorem it should not. Deciding the emptiness of R for a query q, which is a critical condition in theorem 2, is possible as proved in theorem 3. Theorem 3. Given an XPath query q in XP(/, [], ∗) and a duplicate-free DTD D the problem of determining whether the C-satisfying set for q is empty, i.e. R = ∅, is decidable. Proof. See proof in [2]. A Sound and Complete Algorithm for Query Containment. Given q, p XPath queries in XP(/, [], ∗) and a duplicate-free DTD D we provide a sound and complete algorithm for deciding p ⊇0 q. The basic idea is to eliminate wildcards from q and then by utilizing theorem 2 and lemma 2 we are able to decide the containment problem. First we describe how to reduce a query in XP(/, [], ∗) to a disjunction of queries in XP(/, []). We will do so by providing a useful definition and then describing a full procedure for eliminating wildcards from q. Definition 11. Let q be an XPath query in XP(/, [], ∗) and D be a duplicate- free DTD over S. Let also n be the number of nodes x ∈ Vq, such that x.label = ∗. We define as L(x1, . . . , xn) the n-ary tuple for which the following hold: (1) each xi ∈ S, with 1 ≤ i ≤ n, (2) each xi corresponds to a wildcard-labeled node from q and (3) the query q , that comes from q by replacing every wildcard node with its corresponding xi, is D-satisfiable. The wildcard-set of q, denoted L(q), is the maximum set that contains tuples of the form L(x1, . . . , xn). Example 2. Let q ≡ /a/∗/g be an XPath query in XP(/, [], ∗) and the following duplicate free DTD D: a → (b, c, d, e∗ ), b → (f, g, a∗ ), e → (f, g), c → (i), d → (i), f → (i), g → (i). The alphabet is S = {a, b, c, d, e, f, g, i} and the query q has only one wildcard label. It is obvious to see that L(q) = {(b), (e)}, since /a/b/g and /a/e/g are D-satisfiable queries. Algorithm 1. Wildcard Elimination Input: An XPath query q in XP(/, [], ∗) and a DTD D. Compute wildcard-set L(q). i ← 0 FOR EACH n-ary relation L(x1, . . . , xn) ∈ L(q) DO i ← i + 1 qi ← replace every wildcard in q with each corresponding xi q ← q | qi END FOR Output: XPath query q ≡ q1| . . . |qm, s.t. qi is in XP(/, [])
- 42. On Equivalence andRewriting of XPath Queries 11 The reduction of an XPath query from XP(/, [], ∗) to XP(/, []) under a duplicate-free DTD produces an XPath query that is formulated as the dis- junction of a set of queries in XP(/, []). Wildcard elimination algorithm is not polynomial due to the complexity of computing the wildcard-set. If we consider all possible values for each wildcard labeled node in computing the wildcard set this yields an exponential algorithm. Note that the D-satisfiability does not in- fluence this complexity, since its complexity for XPath queries in XP(/, []) under a duplicate-free DTD is PTIME [17]. It is obvious that the number of queries in the disjunction is equal to the cardinality of the wildcard-set, which in worst case is exponential to the number of the symbols in the alphabet of the DTD. Algorithm 2 below is a sound and complete decision procedure for the query containment problem for XPath expressions in XP(/, [], ∗). It reduces q into a set of XPath queries in XP(/, []) using algorithm 1. Then, using the fact that an XPath expression contains a disjunction of XPath expressions if it contains every single expression that constitutes the disjunction, theorem 2, and lemma 2 we can decide p ⊇0 q, since for each query in XP(/, []) R = ∅ [25] / The complexity is dominated by the wildcard-Set computation. Also note that chaseC(qi) is computed considering only FCs and SCs because each qi is in XP(/, []). Algorithm 2. Containment Check Input:q, p in XP(/, [], ∗), a set C of SCs, FCs inferred by a duplicate-free DTD. q ← Wildcard − Elimination(q) FOR EACH qi ∈ q DO qi ← chaseC(qi) IF there is no homomorphism from p to qi THEN RETURN “no” END FOR RETURN “yes”; Output: “yes” if p ⊇0 q ; “no” otherwise. Example 3. (continuing Example 1) Algorithm 2 first will call the Wildcard- elimination procedure on q that results in an expression q ≡ /a[b[i]][b[j]]. Then the FOR loop will be executed only once and in this execution first the chase of q will be computed that is chaseC(q ) ≡ /a[b[i][j]] applying the FC a ↓ b and then will be checked the homomorphism existence from p to chaseC(q ). Since there exists such a homomorphism then the algorithm will output “yes” meaning that p ⊇SAT (D) q. A PTIME Algorithm for Query Containment. Utilizing the equivalence p ⊇SAT (D) q if and only if p ⊇ chaseC(q), we can derive from theorem 2 and lemma 2 a PTIME procedure for deciding containment of queries in XP(/, [], ∗) under a duplicate-free DTD. Since theorem 2 requires the C−satisfying set R for query q to be non-empty, the algorithm is complete only for queries with this prop- erty. It is trivially sound since, if p ⊇ chaseC(q) then p ⊇SAT (D) q. It uses a
- 43. 12 P. Aravogliadisand V. Vassalos PTIME chase procedure inspired by [25], that in contrast to applying every rule to the query until no other rule application changes the query, applies the rules on q along its selection path in a way that takes into account the query p. Algorithm 3. PTIME Containment Check Input: q, p in XP(/, [], ∗), a duplicate free DTD D. Step 1: Compute the chaseC(p) – C set of FCs, SCs, UCs inferred by D. p ← chaseC(p, q, D) Step 2: Check Containment IF there exist a homomorphism from q to p THEN RETURN “yes” ELSE RETURN “no” Output: “yes” if p ⊆0 q ; “no” otherwise. Algorithm 4. Chase(p,q,D) Input: p, q ∈ XP(/, [], ∗), D a duplicate-free DTD. Apply UCs to p inferred by D Apply FCs to p inferred by D Traverse in top-down manner expressions p, q Find nodes x ∈ q and u ∈ p s.t. (x.label = u.label OR u.label = ∗ OR x.label = ∗) AND (x has a child y) AND (u has no child with y.label = ∗ OR y.label = ∗) IF (y.label = ∗) Apply the inferred SC u.label: B ⇓ y.label from D to p, where B is the set of labels of children of u ∈ p. ELSE −(y.label = ∗) Apply every inferred SC u.label: B ⇓ z.label from D to p, where B is the set of labels of children of u ∈ p. Output: chaseC(p). Query Rewriting for XP (/, [], ∗). For a view v and a query q in XP(/, [], ∗), it is CoNP-hard to decide if there is a rewriting of q using v in the presence of a duplicate free DTD D. This follows immediately from theorem 2 of [17] and lemma 4.1 of [26]. In what follows we provide two algorithms for the query rewriting problem described in definition 9. A Sound and Complete Query Rewriting Algorithm. Algorithm 5 below, when we use as containment test Algorithm 2, is a sound and complete algorithm for the rewriting problem for queries in XP(/, [], ∗) under a duplicate-free DTD. Algorithm 5. Query Rewriting using views Input: q, v in XP(/, [], ∗), D a duplicate-free DTD D. Step 1: Check rewriting existence let ov be the output node of v let nq be a node in q having the same position to ov IF ((q) nq sub ⊕ v ≡SAT (D) q) THEN RETURN “no”
- 44. On Equivalence andRewriting of XPath Queries 13 ELSE RETURN “yes” Step 2: Find rewriting IF (answer is “yes”) THEN (q) nq sub is a compensation pattern of q using v. Output: “yes” if q is rewritable using v and the compensation pattern (q) nq sub. Since the containment test is EXPTIME then algorithm 5, whose correctness follows from theorem 1, is in EXPTIME. Query Rewriting in PTIME. If in algorithm 5 we use instead the PTIME containment test of section 4.1 then we obtain a PTIME query rewriting algorithm. Example 4 shows a simple rewriting for a query in XP(/, [], ∗). Example 4. (continuing Example 2) Let v ≡ /a/ ∗ [f] be an XPath view in XP(/, [], ∗) under the duplicate free DTD D defined in example 2. Algorithm 5 will generate as compensation query c ≡ / ∗ /g. Notice that chasing q with the WSC ∗: {g} ⇓ f we get q ≡ /a/ ∗ [f]/g. 4.2 The XPath Fragment XP (/, [], ∗) with Acyclic and Choice-Free DTD We can adapt algorithm 5 to solve the query rewriting problem under acyclic and choice-free DTD. For the wildcard-labeled children of any node of a query in XP(/, [], ∗) under an acyclic and choice-free DTD, is easy to determine to which label from the alphabet correspond. Therefore following the wildcard elimination technique we obtain a PTIME algorithm for the containment problem for queries in XP(/, [], ∗) under acyclic and choice-free DTD. This problem is shown in [4] to be in PTIME. Therefore, we can obtain a PTIME sound and complete algorithm for the rewriting problem. 5 Related Work A framework for the problem of finding equivalent XPath query rewritings using views is provided in [3]. The problem is studied extensively in [1,26] for XPath queries in XP(/, //, [], ∗) providing either incomplete algorithms [26] or suffi- cient conditions under which the problems can be shown to be CoNP-Complete [1]. There is also recent work [9,21] on the problem of answering XPath queries using multiple views via the use of node intersection. Such work expands the scope of the XPath subset considered for the views and the rewritings, adding intersection. Finally, in [10] Cautis et al. have looked into the problem of rewrit- ing XPath queries using views generated by expressive declarative specifications resulting potentially in an infinite number of views. The specifications impose certain constraints on the structure of generated views, but these constraints are orthogonal to DTD constraints, which are imposed on the database.
- 45. 14 P. Aravogliadisand V. Vassalos The XPath query containment problem, which is very closely related to the XPath query rewriting problem, has been extensively studied for various XPath fragments in [5,11,14,15,18,24,25,27], including certain constraints such as DTDs. Relevant work have been discussed in section 2. We use and extend these results developing algorithms for the problem under DTD constraints for an XPath fragment with wildcard. These results are the basis for the rewriting algorithms we propose. 6 Conclusions and Future Work We develop sound and complete algorithms for XPath rewriting using views in the presence of DTD constraints for the XPath fragment XP(/, [], ∗). We also provide a novel reduction of a DTD into chase-able constraints and use it to develop two algorithms for checking containment for the XP(/,[],*) fragment; one is polynomial but is complete only when the query satisfies a certain condition (not empty C-Satisfying set), the other is complete but exponential, as it relies on the elimination of wildcards from one of the queries. We plan to continue our investigation into efficient techniques for rewrit- ing using views for more expressive XPath fragments including backward axes. Moreover, following recent results for the containment of XPath fragments that include union and intersection [22], we are working on rewriting for queries and views for those XPath fragments. References 1. Afrati, F., Chirkova, R., Gergatsoulis, M., Pavlaki, V., Kimelfeld, B., Sagiv, Y.: On rewriting xpath queries using views. In: EDBT, pp. 168–179 (2009) 2. Aravogliadis, P., Vassalos, V.: Rewriting xpath queries using views under dtd con- straints. Technical report, AUEB (2009), http://wim.aueb.gr/papers/xpathrewrite-ext.pdf 3. Balmin, A., Ozcan, F., Beyer, K., Cochrane, R., Pirahesh, H.: A framework for using materialized XPath views in XML query processing. In: Proc. of the 30th VLDB Conference, pp. 60–71 (2004) 4. Benedikt, M., Fan, W., Geerts, F.: XPath satisfiability in the presence of DTDs. In: Proc. PODS 2005, pp. 25–36 (2005) 5. Benedikt, M., Koch, C.: Xpath leashed. ACM Comptuting Survey 41(1) (2008) 6. Berglund, A., Boag, S., Chamberlin, D., et al.: XML Path Language (XPath) 2.0. W3C, http://www.w3.org/TR/XPath20 7. Boag, S., Chamberlin, D., Fernandez, M.F., et al.: XQuery 1.0: An XML Query Language. W3C, http://www.w3.org/TR/XQuery 8. Bruno, N., Koudas, N., Srivastava, D.: Holistic twig joins: optimal XML pattern matching. In: Proc. SIGMOD Conference, pp. 310–321 (2002) 9. Cautis, B., Deutsch, A., Onose, N.: Xpath rewriting using multiple views: Achieving completeness and efficiency. In: Proc. WebDB 2008 (2008) 10. Cautis, B., Deutsch, A., Onose, N., Vassalos, V.: Efficient rewriting of xpath queries using query set specifications. In: Proceedings of the VLDB Endowment, vol. 2, pp. 301–312 (2009)
- 46. On Equivalence andRewriting of XPath Queries 15 11. Deutsch, A., Tannen, V.: Containment and integrity constraints for xpath. In: KRDB Workshop (2001) 12. Halevy, A.: Answering queries using views: A survey. VLDB J. 10(4), 270–294 (2001) 13. Kimelfeld, B., Sagiv, Y.: Revisiting redundancy and minimization in an xpath fragment. In: EDBT, pp. 61–72 (2008) 14. Lakshmanan, L., Wang, H., Zhao, Z.: Answering tree pattern queries using views. In: Proc. of the 32th VLDB Conference, pp. 571–582 (2006) 15. Miklau, G., Suciu, D.: Containment and equivalence for an XPath fragment. Journal of the ACM 51(1) (2004) 16. Milo, T., Suciu, D.: Index structures for path expressions. In: Beeri, C., Bruneman, P. (eds.) ICDT 1999. LNCS, vol. 1540, pp. 277–295. Springer, Heidelberg (1998) 17. Montazerian, M., Wood, P., Mousavi, S.: XPath query satisfiability is in ptime for real-world dtds. In: XSym, pp. 17–30 (2007) 18. Neven, F., Schwentick, T.: On the complexity of XPath containment in the pres- ence of disjunction, DTDs, and variables. In: Proc. Logical Methods in Computer Science, vol. 2 (2006) 19. Papakonstantinou, Y., Vassalos, V.: The Enosys Markets Data Integration Plat- form: Lessons from the trenches. In: CIKM, pp. 538–540 (2001) 20. Schwentick, T.: XPath query containment. SIGMOD Record 33(1), 101–109 (2004) 21. Tang, N., Yu, J.X., Ozsu, M.T., Choi, B., Wong, K.: Multiple materialized view selection for xpath query rewriting. In: ICDE Proc.of the 2008, pp. 873–882 (2008) 22. ten Cate, B., Lutz, C.: The complexity of query containment in expressive frag- ments of XPath 2.0. In: Proc. PODS 2007, pp. 73–82 (2007) 23. Wang, J., Li, J., Yu, J.X.: Answering tree pattern queries using views: a revisit. In: Proc. EDBT (2011) 24. Wood, P.: Minimizing simple XPath expressions. In: Proc. WebDB 2001, pp. 13–18 (2001) 25. Wood, P.T.: Containment for xPath fragments under DTD constraints. In: Calvanese, D., Lenzerini, M., Motwani, R. (eds.) ICDT 2003. LNCS, vol. 2572, pp. 297–311. Springer, Heidelberg (2002) 26. Xu, W., Ozsoyoglu, Z.M.: Rewriting XPath queries using materialized views. In: VLDB 2001, pp. 121–132 (2005) 27. Zhou, R., Liu, C., Wang, J., Li, J.: Containment between unions of xpath queries. In: Zhou, X., Yokota, H., Deng, K., Liu, Q. (eds.) DASFAA 2009. LNCS, vol. 5463, pp. 405–420. Springer, Heidelberg (2009)
- 47. 16 P. Aravogliadisand V. Vassalos Appendix Constraints from DTD. In section 4.1 we define the SC, FC and UC constraints and their corresponding wildcard versions. The definition of WSC was such that no new wildcard nodes will be added to the query after chasing it. Also, rules of the form a ↓ ∗ are not extracted from DTD since such rules are a meaningless abbreviation of a set of functional constraints and also according to the semantics of chase such rules will not maintain the equivalence given in lemma 1. Finally, note that the UC constraint declares that a node will have children all labeled the same and obviously can not derive from SC or FC constraints. Table 1. XPath query containment complexity with-without DTD DTD / // [] * Complexity without + + + PTIME [15] without + + + PTIME [15,16] without + + + PTIME [15] without + + + + CoNP-Complete [15] general + + PTIME [18] general + + CoNP-complete [18] duplicate-free PTIME [25] general CoNP-hard [18] acyclic choice-free + + + PTIME [14] duplicate-free CoNP-hard [17] acyclic choice-free + + + PTIME [4] general + + + + EXPTIME-complete [25]
- 48. Incremental Maintenance ofMaterialized XML Views Leonidas Fegaras University of Texas at Arlington fegaras@cse.uta.edu Abstract. We investigate the problem of incremental maintenance of material- ized XML views. We are considering the case where the underlying database is a relational database and the view exposed to querying is a materialized XML view. Then, updates to the underlying database should be reflected to the stored XML view, to keep it consistent with the source data, without recreating the en- tire view from the database after each source update. Unlike related work that uses algebraic methods, we use source-to-source compositional transformations, guided by the database and view schemata. We first translate SQL updates to pure (update-free) XQuery expressions that reconstruct the entire database state, re- flecting the updated values in the new state. Then, we synthesize the right-inverse of the XQuery view function, guided by the view schema. This inverse function is applied to the old view to derive the old database state, which in turn is mapped to the new database state through the update function, and then is mapped to the new view through the view function. The resulting view-to-view function is normal- ized and translated to XQuery updates that destructively modify the materialized view efficiently to reflect the new view values. 1 Introduction We address the problem of incremental maintenance of materialized XML views. That is, given a materialized XML view over a database and an update against the database, our goal is to generate view updates so that the new view after the updates is exactly the same as the view we would have gotten if we had applied the view to the updated source data. We are considering the case where the underlying database is a relational database and the view exposed to querying is a materialized XML view, stored in a native XML database. A native XML database is a specialized database that supports storage, indexing methods, and query processing techniques specially tailored to XML data. There are already many native XML database management systems available, in- cluding Oracle Berkeley-DB XML, Qizx, Natix, etc. Our framework is targeting those native XML databases that support the XQuery Update Facility (XUF) [16] for updat- ing XML data. Exporting data through materialized XML views is very important to data integration, not only because it speeds up query processing, but also because it provides a light-weight interface to data services, without exposing the main database. The incremental maintenance of materialized views is important for applications that require freshness of view data, but it is too expensive to recompute the view data every time the source data change. In our framework, SQL updates are translated to plain XQueries applied to a canon- ical view of the relational database. More specifically, we derive a pure update function A. Hameurlain et al. (Eds.): DEXA 2011, Part II, LNCS 6861, pp. 17–32, 2011. c Springer-Verlag Berlin Heidelberg 2011
- 49. 18 L. Fegaras thatmaps the old database to the new database. Then, we synthesize the right-inverse of the XQuery view function, guided by the view schema. This inverse function is applied to the old view to derive the old database state, which in turn is mapped to the new database state through the update function, and then mapped to the new view through the view function. Although correct, the resulting view-to-view function can be very inefficient. We provide methods to optimize this program and translate it into efficient XQuery updates, so that only the relevant parts of the materialized view are updated. More specifically, our framework performs the following tasks: 1. We map the underlying relational database DB to the XML data db using a canoni- cal view of relational data. This wrapper, which is a virtual XML view, allows us to refer to relational data within the XQuery data model without any model extension. 2. We express the source SQL updates in pure (update-free) XQuery code over the virtual XML view, as a function u from the old to the new database state that re- constructs the entire canonical XML view of the relational database, reflecting the updates. 3. Given a view, view, expressed in XQuery, we synthesize the XQuery expression view that is a right-inverse of view, such that view(view (V )) = V for any valid view instance V (that is, view must be a surjective function). Note that, view (view(DB)) is not necessarily equal to DB; it is equal to DB for isomorphic views only, which are uncommon. 4. The composition F(V ) = view(u(view (V ))) is a function from the old view V to the new view (after the updates), where view (V ) creates an entire source database from the view V , then u reconstructs this database to reflect the updates, and finally view maps the new database to the new view. We use normalization techniques to optimize F(V ) so that the resulting program avoids the reconstruction of most parts of the intermediate database data. 5. Finally, we rewrite the normalized XQuery expression F(V ) into efficient XQuery updates that destructively modify the original view V using the XQuery Update Facility (XUF). That is, the resulting program is a direct view update expressed in XUF that, in most cases, does not access the underlying database. The key contribution of our work is in the development of a novel framework for the incremental maintenance of materialized XML views that uses source-to-source, com- positional transformations only. Unlike related approaches that use framework-specific algebras to achieve a similar goal [10,12], our work can be incorporated into any ex- isting XQuery engine. Since it breaks the task of view maintenance into a number of more manageable tasks that are easier to implement and verify their correctness, it has the potential of becoming a general methodology for incremental view maintenance. The most important limitation of our approach is that both the inverse synthesis (Step 3) and the XUF generation (Step 5) algorithms are schema-based. That is, they re- quire that both the underlying relational and the XML view schemata be known at query translation time. The second limitation is that our algorithms are heuristics that work only in certain cases, such as they can handle one-to-one and one-to-many but they can- not handle many-to-many joins in the view definition. Finally, due to the lack of space, we have not covered some parts the XQuery/XUF syntax, such as descendant-or-self
- 50. Incremental Maintenance ofMaterialized XML Views 19 steps, but we believe that there is no fundamental reason why our framework cannot be extended to cover most of the XQuery syntax. 2 Related Work Although the problem of incremental view maintenance has been extensively studied for relational views (see [11] for a literature survey), there is still little work on XML view maintenance [3,1]. One of the earliest works on incremental maintenance of ma- terialized views over semi-structured data was for the Lorel query language [2], which was based on the graph-based data model OEM. Their method produces a set of queries that compute the updates to the view based upon an update of the source. Although there are already a number of proposals for XQuery update languages, there is now a W3C candidate recommendation, called XQuery Update Facility (XUF) [16]. El-Sayed et al [12] use an algebraic approach for incremental XQuery view maintenance, where an update to the XML source is transformed into a set of update primitives that are prop- agated through the XML algebra tree and become incremental update primitives to be applied to the result view. The work by Sawires et al [14] considers the view mainte- nance problem for simple XPath views (without value-based predicates). It reduces the frequency of view recomputation by detecting cases where a source update is irrele- vant to a view and cases where a view is self-maintainable given a base update. BEA’s AquaLogic Data Services Platform allows developers to specify inverse functions that enable its XQuery optimizer to push predicates and perform updates on data services that involve the use of such functions [13]. Finally, although not directly applicable to incremental view maintenance, the inverse problem of updating XML views over relational data by translating XML view updates to embedded SQL updates has been studied by earlier work (see for example [4,6]). 3 Our Framework In this section, we use an XML view example to describe our approach to the incre- mental maintenance of XML views. Consider a bibliography database described by the following relational schema: Article ( aid, title, year ) Author ( aid, pid ) Person ( pid, name, email ) where the attributes Author.aid and Author.pid are foreign keys that reference the keys Article.aid and Person.pid, respectively. An XML view over a relational database can be defined over a canonical XML view of relational data, as is done in SilkRoute [9] and many other systems. The canonical XML view of the bibliography relational schema has the following XML type: element DB { element Article { element row { element aid { xs: int }, element title { xs: string }, element year { xs: string } }∗ },
- 51. 20 L. Fegaras elementAuthor { element row { element aid { xs: int }, element pid { xs: int } }∗ }, element Person { element row { element pid { xs: int }, element name { xs: string }, element email { xs: string } }∗ } } that is, a relational table is mapped to an XML element whose tag name is the table name and its children are row elements that correspond to the table tuples. In addition, this type satisfies the following key constraints: key { /DB/Article/row, aid / data () } key { /DB/Author/row, (aid ,pid )/ data () } key { /DB/Person/row, pid/ data () } As in XML-Schema [15], for a key { p1, p2 }, the selector path p1 defines the applicable elements and the field path p2 defines the data that are unique across the applicable elements. For example, key { /DB/Article/row, aid/data() } indicates that the aid elements are unique for the Article rows. Given this canonical view $DB of the relational database, an XML view, view($DB), can be defined using plain XQuery code: view($DB) = bib{ for $i in $DB/Article/row return article aid=’{$i/ aid/ data()}’{ $i / title , $i /year, for $a in $DB/Author/row[aid=$i/aid] return author pid=’{$a/pid/ data()}’{ $DB/Person/row[pid=$a/pid]/name/data() }/author }/ article }/bib This code converts the entire canonical view of the relational database into an XML tree that has the following type: element bib { element article { attribute aid { xs: int }, element title { xs: string }, element year { xs: string }, element author { attribute pid { xs: int }, xs: string }∗ }∗ } We assume that this view is materialized on a persistent storage, which is typically a native XML database. Our goal is to translate relational updates over the base data to XQuery updates (expressed in XUF) that incrementally modify the stored XML view to make it consistent with the base data. Our framework synthesizes a right-inversefuncton view’ of the view function, view, such that, for all valid view instances V , view(view’(V )) is equal to V . Since an inverted view reconstructs the relational database from the view, it must preserve the key constraints of the relational schema. This is accomplished with an extension of the FLWOR XQuery syntax that enforces these constraints. In its sim- plest form, our FLWOR syntax may contain an optional ‘unique’ part: for $v in e where p($v) unique k($v) return f($v)
- 52. Incremental Maintenance ofMaterialized XML Views 21 wherep($v),k($v),and f($v) areXQuery expressionsthatdepend on$v.Theunique k($v) part of the FLWOR syntax skips the duplicate nodes $v from e, where the equality for duplicate removal is modulo the key function k($v). More specifically, let the result of e be the sequence of nodes (x1, . . . , xn). Then the result of this FLWOR expression is the sequence concatenation (y1, . . . , yn), where yi = ⎧ ⎨ ⎩ () if ¬p(xi) () if ∃ji : k(xj) = k(xi) f(xi) (otherwise) Based on this extended syntax, we can define a function view’($V) that is precisely the right-inverse of the previous view: view’($V) = DBArticle{ for $ii in $V/article unique $ii /@aid/data() return rowaid{$ii/@aid/data()}/aid {$ii / title , $ii /year}/ }/Article Author{ for $ia in $V/ article , $aa in $ia/ author unique ($ia /@aid/data (), $aa/@pid/data()) return rowaid{$ia/@aid/data()}/aid pid{$aa/@pid/data()}/pid/row}/Author Person{ for $ip in $V/ article , $ap in $ip/ author unique $ap/@pid/data() return rowpid{$ap/@pid/data()}/pid name{$ap/data()}/name email∗/email/row }/Person/DB In fact, our framework is capable of synthesizing this function automatically from the view function for many different forms of XQuery code. It will also generate the unique constraints automatically from the DB schema key constraints. For example, our system will infer that the applicable key for the first for-loop in view’($V) is key { /DB/Article/row, aid/data() }, given that the for-loop body has the same type as /DB/Article/row and is inside DB and Article element constructions. By applying the field path ./aid/data() to the for-loop body and normalizing it, our system will add the constraint unique $ii/@aid/data() to the for-loop. (Note that this heuristic method is ap- plicable to relational key constraints only, where the key selector type corresponds to a relational table.) We can see that the unique parts of the for-loops in the inverted view are necessary in order to reconstruct the relational data without duplicate key values. The star in the email element content indicates that it can be any value, since this value is not used in the view. Stars in the inverted view signify that the view itself is a sur- jective function, so that we may not be able to reconstruct the complete database from the view. As we will show next, when our framework synthesizes the incremental view updates, the results of the inverted view must always pass through the view function to derive a view-to-view function, which eliminates the star values. To show that view(view’($V)) is indeed equal to $V, we have to use normalization rules and the properties of the key constraints. More specifically, view(view’($V)) is: bib{ for $i in view’($V)/ Article /row
- 53. 22 L. Fegaras returnarticle aid=’{$i/aid / data()}’{ $i / title , $i /year, for $a in view’($V)/Author/row[aid=$i/ aid] return author pid=’{$a/pid/ data()}’{ view’($V)/Person/row[pid=$a/pid ]/name/data() }/author }/ article }/bib XQuery normalization reduces general XQueries to normal forms, such as to for-loops whose domains are simple XPaths. In addition to fusing layers of XQuery compositions and eliminating their intermediate results, normal forms are simpler to analyze than their original forms. Normalization can be accomplished with the help of rules, such as: for $v in (for $w in e1 returne2) return e3 = for $w in e1, $v in e2 return e3 to normalize the for-loop domain. If we expand the view’($V) definition and normalize the resulting code, we get the following code: bib{ for $ii in $V/ article unique $ii /@aid/data() return article aid=’{$ii /@aid/data()}’{ $ii / title , $ii /year, for $ia in $V/ article , $aa in $ia / author where $ia/@aid/data()= $ii /@aid/data() unique ($ia/@aid/data (), $aa/@pid/data()) return author pid=’{$aa/@pid/data()}’{ for $ip in $V/ article , $ap in $ip/ author where $ap/@pid/data()=$aa/@pid/data() unique $ap/@pid/data() return $ap/data () }/author }/ article }/bib To simplify this code further, we would need to take into account the key constraints expressed in the unique parts of the for-loops. A general rule that eliminates nested for-loops is: for $v1 in f1($v), . . . , $vn in fn($v) return g( for $w1 in f1($w), . . . , $wn in fn($w) where key($w) = key($v) unique key($w) return h($w) ) = for $v1 in f1($v), . . . , $vn in fn($v) return g( h($v) ) where key, g, h, f1, . . . , fn are XQuery expressions that depend on the XQuery vari- ables, $v and $w, which are the variables $v1, . . . , $vn and $w1, . . . , $wn, respectively. This rule, indicates that if we have a nested for-loop, where the inner for-loop variables have the same domains as those of the outer variables, modulo variable renaming (that is, fi($v) and fi($w), for all i), and they have the same key values, for some key func- tion, then the values of $w must be identical to those of $v, and therefore the inner for-loop can be eliminated. We can apply this rule to our previous normalized code, by noticing that the outer for-loop is over the variables $ia and $aa and the inner for-loop is over the variables $ip and $ap, where the key is $ap/@pid/data() and the predicate is $ap/@pid/data()=$aa/@pid/data(). This means that the code can be simplified to:
- 54. Incremental Maintenance ofMaterialized XML Views 23 bib{ for $ii in $V/ article unique $ii /@aid/data() return article aid=’{$ii /@aid/data()}’{ $ii / title , $ii /year, for $ia in $V/ article , $aa in $ia / author where $ia/@aid/data()= $ii /@aid/data() unique ($ia/@aid/data (), $aa/@pid/data()) return author pid=’{$aa/@pid/data()}’{ $aa/data () }/author }/ article }/bib Similarly, we can apply the same rule to the resulting XQuery by noticing that the outer for-loop is over the variable $ii and the inner for-loop is over the variable $ia, where the key is $ia/@aid/data(), and simplify the code further: ID($V) = bib{ for $ii in $V/ article unique $ii /@aid/data() return article aid=’{$ii /@aid/data()}’{ $ii / title , $ii /year, for $aa in $ii / author return author pid=’{$aa/@pid/data()}’{ $aa/data () }/author }/ article }/bib ID($V) is precisely the copy function applied to the view $V, which copies every node in the view. That is, ID($V) is equal to $V. Consider now an SQL update over the base relational tables: update Article set year=2009 where exists ( select ∗ from Author a, Person p where a.aid=aid and a.pid=p.pid and p.name=’Smith’ and title =’XQuery’) which finds all articles authored by Smith that have XQuery as title and replaces their year with 2009. This update can be written in plain XQuery U($DB) over the canonical XML view $DB of the relational database that reconstructs the database reflecting the updates: DBArticle{ for $iu in $DB/Article/row return if some $au in $DB/Author/row, $pu in $DB/Person/row satisfies $au/aid = $iu/ aid and $au/pid = $pu/pid and $pu/name = ’Smith’ and $iu/ title = ’XQuery’ then row{$iu/aid, $iu/ title }year2009/year/row else $iu }/Article{$DB/Person, $DB/Author}/DB The new view after the update can be reconstructed from the old view $V using view(U(view’($V))). First, after normalization, U(view’($V)) becomes: DBArticle{ for $ii in $V/ article return if some $aa in $ii / author satisfies $aa/data () = ’Smith’ and $ii / title = ’XQuery’
- 55. Other documents randomlyhave different content
- 56. 2 3 4 5 6 7 The Earl ofNorthbrook, when Viceroy of India, presented a rich carpet to the tomb at Sikandra, to be placed over the stone which covers the remains of the greatest ruler of India. Salim, under the name of Jahangir, reigned from 1605 to 1627. His mother was a Rajpút. He was cruel, avaricious, and debauched. He suppressed the rebellion of his son Khusru with the most horrible cruelties. In 1608 Captain William Hawkins landed at Surat, and was received with great favour by Jahangir at Agra. But, after two years, he failed in securing trading privileges for the East India Company, and left Agra in 1611. The influence of Nur Mahal, his favourite wife, was paramount over Jahangir; but he had no children by her. Of his four sons, he kept the eldest, Khusru, in prison for rebellion. Parwiz, the second, was a drunkard. Khurram, afterwards known as Shah Jahan, succeeded his father. Shahryar was the youngest. In 1615 Sir Thomas Roe arrived at the court of Jahangir, as ambassador from James I., and remained until 1618. Jahangir died on October 12th, 1627, and was succeeded by his rebellious son as Shah Jahan. Shah Jahan reigned from 1628 to 1658. Aurangzíb reigned from 1658 to 1707. It was Nur Mahal who induced Jahangir to be more moderate in his cups. Best known as the “Tarikh-i-Badauni.” This invitation is, of course, not historical. Our author, as he tells us in his Introduction, has prolonged the life of Faizi for the purposes of his story. In reality, Faizi died before the murder of his brother Abú-l Fazl.
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- 58. BY PERMISSION OFTHE SECRETARY OF STATE FOR INDIA IN COUNCIL. CONTENTS. CIVIL.—Gradation Lists of Civil Service, Bengal, Madras and Bombay. Civil Annuitants. Legislative Council, Ecclesiastical Establishments, Educational, Public Works, Judicial, Marine, Medical, Land Revenue, Political, Postal, Police, Customs and Salt, Forest, Registration and Railway and Telegraph Departments, Law Courts, Surveys, c., c. MILITARY.—Gradation List of the General and Field Officers (British and Local) of the three Presidencies, Staff Corps, Adjutants-General’s and Quartermasters-General’s Offices, Army Commissariat Departments, British Troops Serving in India (including Royal Artillery, Royal Engineers, Cavalry, Infantry, and Medical Department), List of Native Regiments, Commander-in-Chief and Staff, Garrison Instruction Staff, Indian Medical Department, Ordnance Departments, Punjab Frontier Force, Military Departments of the three Presidencies, Veterinary Departments, Tables showing the Distribution of the Army in India, Lists of Retired Officers of the three Presidencies. HOME.—Departments of the Office of the Secretary of State, Coopers Hill College, List of Selected Candidates for the Civil and Forest Services, Indian Troop Service. MISCELLANEOUS.—Orders of the Bath, Star of India, and St. Michael and St. George. Order of Precedence in India. Regulations for Admission to Civil Service. Regulations for Admission of Chaplains. Civil Leave Code and Supplements. Civil Service Pension Code—relating to the Covenanted and Uncovenanted Services. Rules for the Indian Medical Service. Furlough and Retirement Regulations of the Indian Army. Family Pension Fund. Staff Corps Regulations. Salaries of Staff Officers. Regulations for Promotion. English Furlough Pay. Works issued from the India Office, and Sold by Wm. H. ALLEN Co.
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- 81. Max Muller’s Rig-Veda-Sanhita.The Sacred Hymns of the Brahmins; together with the Commentary of Sayanacharya. Published under the Patronage of the Right Honourable the Secretary of State for India in Council. 6 vols., 4to. £2 10s. per volume. Meadow’s (T.) Notes on China. 8vo. 9s. Military Works—chiefly issued by the Government. Field Exercises and Evolutions of Infantry. Pocket edition, 1s. Queen’s Regulations and Orders for the Army. Corrected to 1874. 8vo. 3s. 6d. Interleaved, 5s. 6d. Pocket Edition, 1s. Musketry Regulations, as used at Hythe. 1s. Dress Regulations for the Army. 1875. 1s. 6d. Infantry Sword Exercise. 1875. 6d. Infantry Bugle Sounds. 6d. Handbook of Battalion Drill. By Lieut. H. C. Slack. 2s; or with Company Drill, 2s. 6d. Handbook of Brigade Drill. By Lieut. H. C. Slack. 3s. Red Book for Sergeants. By William Bright, Colour-Sergeant, 87th Middlesex R.V. 1s. Handbook of Company Drill; also of Skirmishing, Battalion, and Shelter Trench Drill. By Lieut. Charles Slack. 1s. Elementary and Battalion Drill. Condensed and Illustrated, together with duties of Company Officers, Markers, c., in Battalion. By Captain Malton. 2s. 6d.
- 82. Cavalry Regulations. Forthe Instruction, Formations, and Movements of Cavalry. Royal 8vo. 4s. 6d. Cavalry Sword, Carbine, Pistol and Lance Exercises, together with Field Gun Drill. Pocket Edition. 1s. Manual of Artillery Exercises, 1873. 8vo. 5s. Manual of Field Artillery Exercises. 1877. 3s. Standing Orders for Royal Artillery. 8vo. 3s. Principles and Practice of Modern Artillery. By Lt.-Col. C. H. Owen, R.A. 8vo. Illustrated. 15s. Artillerist’s Manual and British Soldiers’ Compendium. By Major F. A. Griffiths. 11th Edition. 5s. Compendium of Artillery Exercises—Smooth Bore, Field, and Garrison Artillery for Reserve Forces. By Captain J. M. McKenzie. 8s. 6d. Principles of Gunnery. By John T. Hyde, M.A., late Professor of Fortification and Artillery, Royal Indian Military College, Addiscombe. Second edition, revised and enlarged. With many Plates and Cuts, and Photograph of Armstrong Gun. Royal 8vo. 14s. Notes on Gunnery. By Captain Goodeve. Revised Edition. 1s. Text Book of the Construction and Manufacture of Rifled Ordnance in the British Service. By Stoney Jones. Second Edition. Paper, 3s. 6d., Cloth, 4s. 6d. Handbooks of the 9, 16, and 64-Pounder R. M. L. Converted Guns. 6d. each. Handbook of the 9 and 10-inch R. M. L. Guns. 6d. each. Handbook of 40-Pounder B. L. Gun. 6d.
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