This document summarizes research on trade-offs in data integration systems. It discusses three main contributions: 1. A method to estimate response freshness using existing data summaries, which was able to estimate freshness with 6% error. 2. A maintenance process to maximize consistency under latency constraints by querying cached entries and maintaining stale or slowly changing entries. This outperformed baseline policies. 3. An extension of the maintenance policy to consider both latency and space constraints, including cache replacement policies. This outperformed state-of-the-art replacement policies when implemented in CSPARQL. The document concludes that balancing latency and consistency in data integration is challenging due to their trade-off relationship, and discusses

1. oheila Dehghanzadeh

2. Agenda
ntroduction to Trade-offs in Integration Systems
equirements and Research Questions
ontributions
onclusions and Future Work

3. Introduction
hat is data integration?
• “Combining data from different distributed sources”1
.
hy is it important?
• Most queries requires integrating data from various sources.
hy is it challenging?
• Sources are autonomous and distributed.
• Distributing query among sources to provide the response has
performance, scalability and availability problems.
• Caching solves above problems but leads to inconsistencies.
• Maintaining cache increases latency.
3
1. https://en.wikipedia.org/wiki/Data_integration

4. The latency/consistency trade-off
4
High consistencyLow consistency
Low latency
High latency
Ideal case
Data
warehouse
Mediator
systems

5. Data integration
ata integration approaches
• Data warehouse (DW)
• Low latency
• Low consistency
High consistencyLow consistency
Low latency
High latency
Ideal case
Data
warehouse
Mediator
systems

6. Data warehouse
Low latency
Low consistency

7. Data Market: Lowest latency with a
consistency threshold
Minimize cost (financial and latency) as
far as consistency is above a threshold
Find me
emails of
“The North
Face”
customers.
My existing
data can
provide you
a response
with 60%
freshness.
Ok
Here is the
responseNo, I want
the fastest
response
with at least
80%
freshness
To provide
80%
freshness
you need to
wait 30 sec
and pay 60$

8. Research Question 1
How to optimally maintain data when
consistency is restricted and latency is demanded
to be minimized?
8

9. Summary of contribution 1
method to estimate the response freshness using the existing data
(JIST2014, ISWC2014).
• Extend summarization techniques to trace the freshness.
• Indexing, histogram and Qtree
• Use summary to estimate the response freshness.
valuation
• We managed to estimate the freshness of a query with 6% error rate.
uture work
• Use more advanced summarizations to lower the error rate.
9

10. Data integration
ata integration approaches
• Data warehouse (DW)
• Low latency
• Low consistency
• Mediator systems (MS)
• High latency
• High consistency
High consistencyLow consistency
Low latency
High latency
Ideal case
Data
warehouse
Mediator
systems
Data
warehouse

11. Mediator System
High latency
high consistency

12. Mediator system: Highest consistency
with a latency threshold
Join
RDF Stream
Generator
Background data
(SPARQL endpoint)
12

13. Mediator system: Highest consistency
with a latency threshold
Join
RDF Stream
Generator
Background data
(SPARQL endpoint)
Local
View
13

14. Mediator system: Highest consistency
with a latency threshold
Join
RDF Stream
Generator
Background data
(SPARQL endpoint)
Local
View
Maintenance
Process
Freshness decreases Refresh
Cost/Quality trade-
off
14

15. Research Question 2
How to optimally maintain data when the latency
is restricted and consistency is demanded to be
maximized?
15

16. Summary of contribution 2
maintenance process to maximize consistency with respect to latency
constraint (WWW2015, ICWE2015).
• Query driven: maintain cache entries that are involved in current
evaluation
• Freshness driven: maintain cache entries that
• Are stale
• Change less frequently
• Affect future evaluations
valuation
• The proposed approach outperforms a set of baseline policies.
his work has already been followed up
• Queries with FILTER clauses (ICWE2016)
• Queries with complex join patterns (ISWC2016) 16

17. Data integration
ata integration approaches
• Data warehouse (DW)
• Low latency
• Low consistency
• Mediator systems (MS)
• High latency
• High consistency
ntegration in a real system
High consistencyLow consistency
Low latency
High latency
Ideal case
Data
warehouse
Mediator
systems
Data
warehouse
Mediator
systems

18. Contributing the proposed policies to
CSPARQL
• So far we assumed all
required data to provide
the response exists in
the local cache but
needs to be maintained.
• What if required data
does not fit in the local
cache?
18
entries
SERVICE
Provider
Local cache

19. Research Question 3
How to take into account space constraint while
optimizing data integration with regards to
latency or consistency constraints?
19

20. 20
Summary of contribution 3
• An extension of the maintenance policy (contribution 2) to take into
account both latency and space constraints.
• Fetching policies to cope with cache incompleteness
• A freshness based cache replacement policy
• An implementation in CSPARQL
• Evaluation
• The proposed replacement policy outperforms state-of-the-art
replacement policies.
• Future work
• Investigating more complex queries (e.g., with multiple SERVICE
clauses, complex join patterns)

21. Conclusions
n ideal integration engine (low latency and high consistency) is not
possible because these two dimensions are in trade-off.
ontributions:
• Optimizing response latency with consistency threshold has been
studied in the context of Data Marketplace.
• A maintenance policy to optimize response consistency with latency
threshold in the context of knowledge-based event processing.
• Introduction of space constraints to integrate my approach in CSPARQL.
21
High consistencyLow consistency
Low latency
High latency
Ideal case
Data
warehouse
Mediator
systems
Data
warehouse
Mediator
systems

22. Slide 22

23. Data Integration
Data Stream Data Source
Cache
Maintenance
Process
Freshness
decreases
Refresh based on latency constraint
Query (critical latency)
Data Source Data Source
Cache
Maintenance
Process
Freshness
decreases
Refresh based on consistency constraint
Query (critical consistency)
1. Maintaining
cache based on
latency
constraint of
query
(Event
Detection)
2. Maintaining
cache based on
consistency
constraint of
query
(Data Market)
Soheila.dehghanzdeh@insight-centre.org Unit for Reasoning
and Querying

24. Mediator system: Highest consistency with
a latency threshold
24
Query: find Twitter users that have been
mentioned more than 5 times in the last
minute and are followed by more than
1000 users
Stream Processor
Twitter mention stream
#X has 1007 followers
#Y has 2000 followers
#Z has 500 followers
Twitter Follower API
#X is super hero
#X won the gold medal
#X broke the world record
#X is awesome
#X
…
#Y is super hero
#Y won the bronze medal
#Y broke the world record
#Y is awesome
#Y
…
#Z is great
#Z won the silver
medal
#Z broke the world record
#Z is awesome
Well done to #Z, #Y,
#X
User Mentione
d
Followed
by
#X 7 1007
#Y 6 2000
#X has 1007 followers
#Y has 2000 followers
#Z has 600 followers
#X has 998
followers

25. Contributing the proposed policies to
CSPARQL
Requirements
•A local cache R
•Fetch SERVICE from R
•Maintain R
•ESPER external time
25
The modified engine is available on github
Time stamp
entries
SERVICE
Provider
Local cache

26. Workloads with significant improvements
with proposed policy
e hypothesize that WSJ-WBM is more influential if :
• Hypothesis 1: the BKG data change slower
• Hypothesis 2: the BKG data changes with more diversity in change rate
• Hypothesis 3: there is a negative correlation between the streaming rate
and the change rate
• Hypothesis 4: total number of possible events (i.e., caching space) is
larger
he time overhead of WSJ-WBM is negligible
26

27. Experiments set up
data generator to generate various workloads with
• Various change rate distributions within an interval- random or normal
distribution
• Various streaming rates- the inter arrival time of elements follows a
Poisson distribution with various lambda intervals
27

28. Hypothesis 1: BKG data change slower.
28

29. Hypothesis 2: BKG data changes with
more diversity in change rate.
29

30. Hypothesis 3: negative correlation
between the streaming and change rate
30

31. Hypothesis 4: total number of possible
events (i.e., caching space) is larger
31

32. Hypothesis 4: The time overhead of WSJ-
WBM is negligible
32
LocalRemote

33. Combining RDF Streams and Remotely Stored
Background Data
e move to an approximate setting, and we introduce a local view to
store part of the data involved in the query processing, and update part
of it to capture the dynamicity
33

34. A query-driven maintenance process
ELECT * WHERE WINDOW(S, ω, β) PW
. SERVICE(BKG) PS
34
WINDOW clause
JOIN Proposer Ranker
Maintaine
r
Local View
4 2
3
1
SERVICE clause
E
C
RND
LRU
WBM
CWSJ
WSJ
GNR
LRU
FRP

35. Evaluation
35