This document proposes a query model called RSEP-QL to capture event pattern matching in RDF stream processing languages. It presents RSEP-QL's data model of RDF streams and windows, basic operators like EVENT and SEQ, and evaluation semantics. The goal is to provide a reference model for comparing different RSP query languages and studying related problems in a standardized way.

1. A Query Model to Capture Event Pattern
Matching in RDF Stream Processing Languages
Daniele Dell’Aglio, Minh Dao-Tran, Jean-Paul Calbimonte,
Danh Le Phuoc and Emanuele Della Valle
dellaglio@ifi.uzh.ch http://dellaglio.org @dandellaglio
Bologna, Italy, 21.11.2016

2. RDF Stream Processing
Stream
Processing
RDF
&
SPARQL
RDF
Stream
Processing
(RSP)
Real-time
processing of
highly dynamic
data
SemanticWeb
technologies for
data exchange
through theWeb
2/20 EKAW, 21 November 2016A Query Model to Capture Event Patterns in RSP languages

3. Stream Processing and RSP
 In literature there are two different main approaches to
process streams
 Data Stream Management Systems (DSMSs)
 Aggregations and filters
 Relies on the notion of sliding window
 DSMS-inspired RSPs: C-SPARQL, CQELS, SPARQLstream
 Complex Event Processors (CEPs)
 Search of relevant patterns in the stream
 Non-equi-join on timestamps (after, before, etc.)
 CEP-inspired RSP: EP-SPARQL
 C-SPARQL offers some CEP features through a timestamp function
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4. The problem
Execution 7 8 9
EP-SPARQL
(unrestricted)
:a, :c [1,5]
:b, :d [3,5]
:b, :d [3,7]
:a, :c [1,9]
EP-SPARQL
(chronological)
:a, :c [1,5]
:b, :d [3,5]
EP-SPARQL
(recent)
:b, :d [3,5]
:b, :d [3,7]
C-SPARQL
(sl. win. 7,1)
:b, :d [3,7]
Who is listening to who?
{?att :isIn ?room}
SEQ
{?spe :talksIn ?room}
G1 G2 G3 G4
t
{:a :isIn :rRoom}
{:b :isIn :bRoom}
{:c :talksIn :rRoom,
:d :talksIn :bRoom}
{:d :talksIn :bRoom}
G6
{:e :isIn :bRoom}
S G5
{:c :talksIn :rRoom}
3 5 91 7 8
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5. Motivation
 In the context of evaluation of the CEP operators over
RDF streams, how can the behaviour of existing systems
be captured, compared and contrasted?
 Why do we need it?
• Comparison and contrast
• Interoperability
• Study RDF Stream Processing related problems
• Standard RSP query language
 RSEP-QL as a reference model for RSP query languages
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6. Data model: RDF streams and windows
 An RDF stream is an (infinite) ordered sequence of time-
annotated data items (RDF graphs)
t
𝕎(3,1,0)
𝕃(1)
𝑊(2,5]
G1 G2 G3 G4
t
{:a :isIn :rRoom}
{:b :isIn :bRoom}
{:c :talksIn :rRoom,
:d :talksIn :bRoom}
{:d :talksIn :bRoom}
G6
{:e :isIn :bRoom}
S G5
{:c :talksIn :rRoom}
3 5 91 7 8
Window
Landmark window
Sliding window
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7. Operators
 Event Processing operators
• EVENT w P (Basic Event Pattern)
• E1 SEQ E2
• FIRST E1
• LAST E1
• MATCH to describe an event pattern
 Stream Processing operators
 SPARQL operators
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8. Evaluation Semantics
 The SPARQL evaluation function is defined as
⟦𝑃⟧ 𝐷𝑆(𝐺)
 The RSEP-QL evaluation function is ⟪⋅⟫ 𝑜,𝑐
𝑡
• t is the evaluation time instant
• 𝑜, 𝑐 is an additional window to identify the portion of
the data on which the event may happen
 Event pattern evaluation produces event mappings
𝜇, 𝑡1, 𝑡2
• 𝜇 is a solution mapping
• 𝑡1 and 𝑡2 denote the time inverval justifying 𝜇
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9. Basic Event Pattern: EVENT w P
 Look for mappings in graphs selected by the windows
 Example: ⟪EVENT 𝕃(0) {?spe :talksIn ?room}⟫ 0,7
7
𝑊 0,7 , 𝕃(0)
G ?spe ?room 𝒕 𝟏, 𝒕 𝟐
𝐺3 :c :rRoom 5,5
𝐺3 :d :bRoom 5,5
𝐺4 :d :bRoom 7,7
G1 G2 G3 G4
t
{:a :isIn :rRoom}
{:b :isIn :bRoom}
{:c :talksIn :rRoom,
:d :talksIn :bRoom}
{:d :talksIn :bRoom}
G6
{:e :isIn :bRoom}
S G5
{:c :talksIn :rRoom}
3 5 91 7 8
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10. Basic Event Pattern: EVENT w P
 Example: ⟪EVENT 𝕃(5) {?spe :talksIn ?room}⟫ 0,7
7
𝑊 0,7
G ?spe ?room 𝒕 𝟏, 𝒕 𝟐
𝐺4 :d :bRoom 7,7
𝕃(5)
𝑊 0,7 𝕃(5)
G1 G2 G3 G4
t
{:a :isIn :rRoom}
{:b :isIn :bRoom}
{:c :talksIn :rRoom,
:d :talksIn :bRoom}
{:d :talksIn :bRoom}
G6
{:e :isIn :bRoom}
S G5
{:c :talksIn :rRoom}
3 5 91 7 8
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11. E1 SEQ E2
 Event mappings that matches 𝐸2 should be joined with
event mappings from 𝐸1 that are:
 Compatible – the solution mappings in the event mappings
should be compatible
 Before – the interval that justifies the first solution mapping
should finish before the interval that justifies the second one
opens
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12. Example
⟪EVENT 𝕃(0) {?att :isIn ?room} SEQ EVENT 𝕃(0) {?spe :talksIn ?room} ⟫(0,8]
8
𝑊 0,8 , 𝕃(0)
G1 G2 G3 G4
t
{:a :isIn :rRoom}
{:b :isIn :bRoom}
{:c :talksIn :rRoom,
:d :talksIn :bRoom}
{:d :talksIn :bRoom}
G6
{:e :isIn :bRoom}
?spe ?room 𝒕 𝟏, 𝒕 𝟐
:c :rRoom 5,5
:d :bRoom 5,5
:d :bRoom 7,7
?att ?room 𝒕 𝟏, 𝒕 𝟐
:a :rRoom 1,1
:b :bRoom 3,3
:e :bRoom 8,8
?att ?spe ?room 𝒕 𝟏, 𝒕 𝟐
S G5
{:c :talksIn :rRoom}
3 5 91 7 8
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13. Example
⟪EVENT 𝕃(0) {?att :isIn ?room} SEQ EVENT 𝕃(0) {?spe :talksIn ?room} ⟫(0,8]
8
?spe ?room 𝒕 𝟏, 𝒕 𝟐
:c :rRoom 5,5
:d :bRoom 5,5
:d :bRoom 7,7
?att ?room 𝒕 𝟏, 𝒕 𝟐
:a :rRoom 1,1
:b :bRoom 3,3
:e :bRoom 8,8
?att ?spe ?room 𝒕 𝟏, 𝒕 𝟐
𝑊 0,8 , 𝕃(0)
G1 G2 G3 G4
t3 5 91
{:a :isIn :rRoom}
{:b :isIn :bRoom}
{:c :talksIn :rRoom,
:d :talksIn :bRoom}
{:d :talksIn :bRoom}
G6
{:e :isIn :bRoom}
7
S G5
{:c :talksIn :rRoom}
8
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14. Example
⟪EVENT 𝕃(0) {?att :isIn ?room} SEQ EVENT 𝕃(0) {?spe :talksIn ?room} ⟫(0,8]
8
?spe ?room 𝒕 𝟏, 𝒕 𝟐
:c :rRoom 5,5
:d :bRoom 5,5
:d :bRoom 7,7
?att ?room 𝒕 𝟏, 𝒕 𝟐
:a :rRoom 1,1
:b :bRoom 3,3
:e :bRoom 8,8
?att ?spe ?room 𝒕 𝟏, 𝒕 𝟐
𝑊 0,8 , 𝕃(0)
G1 G2 G3 G4
t3 5 91
{:a :isIn :rRoom}
{:b :isIn :bRoom}
{:c :talksIn :rRoom,
:d :talksIn :bRoom}
{:d :talksIn :bRoom}
G6
{:e :isIn :bRoom}
7
S G5
{:c :talksIn :rRoom}
8
not compatible
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15. Example
⟪EVENT 𝕃(0) {?att :isIn ?room} SEQ EVENT 𝕃(0) {?spe :talksIn ?room} ⟫(0,8]
8
?spe ?room 𝒕 𝟏, 𝒕 𝟐
:c :rRoom 5,5
:d :bRoom 5,5
:d :bRoom 7,7
?att ?room 𝒕 𝟏, 𝒕 𝟐
:a :rRoom 1,1
:b :bRoom 3,3
:e :bRoom 8,8
?att ?spe ?room 𝒕 𝟏, 𝒕 𝟐
𝑊 0,8 , 𝕃(0)
G1 G2 G3 G4
t3 5 91
{:a :isIn :rRoom}
{:b :isIn :bRoom}
{:c :talksIn :rRoom,
:d :talksIn :bRoom}
{:d :talksIn :bRoom}
G6
{:e :isIn :bRoom}
7
S G5
{:c :talksIn :rRoom}
8
not compatible
not before
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16. Example
⟪EVENT 𝕃(0) {?att :isIn ?room} SEQ EVENT 𝕃(0) {?spe :talksIn ?room} ⟫(0,8]
8
?spe ?room 𝒕 𝟏, 𝒕 𝟐
:c :rRoom 5,5
:d :bRoom 5,5
:d :bRoom 7,7
?att ?room 𝒕 𝟏, 𝒕 𝟐
:a :rRoom 1,1
:b :bRoom 3,3
:e :bRoom 8,8
?att ?spe ?room 𝒕 𝟏, 𝒕 𝟐
:b :d :bRoom 3,5
𝑊 0,8 , 𝕃(0)
G1 G2 G3 G4
t3 5 91
{:a :isIn :rRoom}
{:b :isIn :bRoom}
{:c :talksIn :rRoom,
:d :talksIn :bRoom}
{:d :talksIn :bRoom}
G6
{:e :isIn :bRoom}
7
S G5
{:c :talksIn :rRoom}
8
not compatible
not before
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17. Example
⟪EVENT 𝕃(0) {?att :isIn ?room} SEQ EVENT 𝕃(0) {?spe :talksIn ?room} ⟫(0,8]
8
?spe ?room 𝒕 𝟏, 𝒕 𝟐
:c :rRoom 5,5
:d :bRoom 5,5
:d :bRoom 7,7
?att ?room 𝒕 𝟏, 𝒕 𝟐
:a :rRoom 1,1
:b :bRoom 3,3
:e :bRoom 8,8
?att ?spe ?room 𝒕 𝟏, 𝒕 𝟐
:b :d :bRoom 3,5
:a :c :rRoom 1,5
:b :d :bRoom 3,7
𝑊 0,8 , 𝕃(0)
G1 G2 G3 G4
t3 5 91
{:a :isIn :rRoom}
{:b :isIn :bRoom}
{:c :talksIn :rRoom,
:d :talksIn :bRoom}
{:d :talksIn :bRoom}
G6
{:e :isIn :bRoom}
7
S G5
{:c :talksIn :rRoom}
8
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18. SEQc, SEQr and SEQn
 We need futher SEQ operators to model the behaviour
of the engines
 They basically works as SEQ, with some additional constraint
 To model the engines we analysed, we defined:
 SEQ
 SEQc
 SEQr
 SEQn
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19. E1 SEQc E2
 If there are two output event mappings with the same solution mapping,
SEQc considers the least recent one
⟪EVENT 𝕃(0) {?att :isIn ?room} SEQc EVENT 𝕃(0) {?spe :talksIn ?room}⟫(0,8]
8
?spe ?room 𝒕 𝟏, 𝒕 𝟐
:c :rRoom 5,5
:d :bRoom 5,5
:d :bRoom 7,7
?att ?room 𝒕 𝟏, 𝒕 𝟐
:a :rRoom 1,1
:b :bRoom 3,3
:e :bRoom 8,8
?att ?spe ?room 𝒕 𝟏, 𝒕 𝟐
:a :c :rRoom 1,5
:b :d :bRoom 3,5
:b :d :bRoom 3,7
G1 G2 G3 G4
t3 5 91
{:a :isIn :rRoom}
{:b :isIn :bRoom}
{:c :talksIn :rRoom,
:d :talksIn :bRoom}
{:d :talksIn :bRoom}
G6
{:e :isIn :bRoom}
7
S G5
{:c :talksIn :rRoom}
8
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20. E1 SEQr E2
 If there are two output event mappings with the same solution mapping,
SEQr considers the most recent one
⟪EVENT 𝕃(0) {?att :isIn ?room} SEQr EVENT 𝕃(0) {?spe :talksIn ?room}⟫(0,8]
8
?spe ?room 𝒕 𝟏, 𝒕 𝟐
:c :rRoom 5,5
:d :bRoom 5,5
:d :bRoom 7,7
?att ?room 𝒕 𝟏, 𝒕 𝟐
:a :rRoom 1,1
:b :bRoom 3,3
:e :bRoom 8,8
?att ?spe ?room 𝒕 𝟏, 𝒕 𝟐
:a :c :rRoom 1,5
:b :d :bRoom 3,5
:b :d :bRoom 3,7
G1 G2 G3 G4
t3 5 91
{:a :isIn :rRoom}
{:b :isIn :bRoom}
{:c :talksIn :rRoom,
:d :talksIn :bRoom}
{:d :talksIn :bRoom}
G6
{:e :isIn :bRoom}
7
S G5
{:c :talksIn :rRoom}
8
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21. E1 SEQn E2
 If there are two input event mappings with the same solution mapping,
SEQn considers the most recent one
⟪EVENT 𝕃(0) {?att :isIn ?room} SEQn EVENT 𝕃(0) {?spe :talksIn ?room}⟫(0,8]
8
?spe ?room 𝒕 𝟏, 𝒕 𝟐
:c :rRoom 5,5
:d :bRoom 5,5
:d :bRoom 7,7
?att ?room 𝒕 𝟏, 𝒕 𝟐
:a :rRoom 1,1
:b :bRoom 3,3
:e :bRoom 8,8
?att ?spe ?room 𝒕 𝟏, 𝒕 𝟐
:a :c :rRoom 1,5
:b :d :bRoom 3,7
G1 G2 G3 G4
t3 5 91
{:a :isIn :rRoom}
{:b :isIn :bRoom}
{:c :talksIn :rRoom,
:d :talksIn :bRoom}
{:d :talksIn :bRoom}
G6
{:e :isIn :bRoom}
7
S G5
{:c :talksIn :rRoom}
8
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22. Consumption policies
 The evaluations in a sequence are not always independent
 Some data may be involved in the current evaluation only if it
was not before
 We need a mechanism to move this information between
evaluations
 Consumption policies are functions to describe the input for
the next evaluation
 Two policies for our needs
 𝒫 𝑢
: the input is the one selected by the window
 𝒫 𝑛
: the input is the one selected by the window and that
was not used to produce results in the previous
evaluations
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23. Captured behaviours
System Window
operator
Sequencing
operator
Consumption
policy
EP-SPARQL
(unrestricted)
Landmark SEQ 𝒫 𝑢
EP-SPARQL
(chronological)
Landmark SEQc 𝒫 𝑛
EP-SPARQL
(recent)
Landmark SEQr 𝒫 𝑛
C-SPARQL Sliding SEQn 𝒫 𝑢
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24. Conclusions
 The dynamics introduced in the continuous query
evlauation process have not been totally understood
 Not fully captured by existing models
 RSEP-QL captures those dynamics, and the behaviours of
existing RDF Stream Processing engines
 CEP-based (today’s talk)
 DSMS-based (RSP-QL)
 What’s next
 Extension of futher operators (NOT, DURING)
 Further investigation about the relation of RSEP-QL with other
models (SPARQL, LARS)
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25. Thank you! Questions?
A Query Model to Capture Event Pattern
Matching in RDF Stream Processing Languages
Daniele Dell’Aglio, Minh Dao-Tran, Jean-Paul Calbimonte,
Danh Le Phuoc and Emanuele DellaValle
dellaglio@ifi.uzh.ch
http://dellaglio.org
@dandellaglio
20/20 EKAW, 21 November 2016A Query Model to Capture Event Patterns in RSP languages