Optimizing SPARQL Query Processing On Dynamic and Static Data Based on Query Time/Freshness...

JIST 2014Optimizing SPARQL Query Processing On

Dynamic and Static Data Based on Query

Time/Freshness Requirements Using

MaterializationSoheila Dehghanzadeh, Marcel Karnstedt, Stefan Decker, Josiane Xavier Parreira, Juergen Umbrich and Manfred Hauswirth

Insight Centre for Data Analytics

Outline

•Introduction

•Terminology

•Problem definition

•Proposed solution

•Experimental results

•Conclusion

Slide 2

Introduction: Query Processing On Linked Data

•Report changes to the local store (maintenance)• sources pro-actively report changes or their existence (pushing).• query processor discover new sources and changes by crawling

(pulling).

•Fast maintenance leads high quality but slow response and vice versa.

•Problem: On-demand maintenance according to response quality requirements.

•Why it is important? It eliminates unnecessary maintenance and leads to faster response and better scalability.

Replication (database) or Caching (web)Off-line

materialization

Local Store

Query Processo

r

Query Response

UPDATES NEW source

s


Terminology

•Quality requirements:• Freshness B/(A+B)• Completeness B/(B+C)

•Maintenance plan• Each set of views chosen for maintenance is called a

maintenance plan.• Having n views, number of maintenance plans is .• Each maintenance plan leads to a different response

quality.

20 October 2014 Slide 4

V1 V2 V3 V4

20% 90% 10% 80%


Freshness Examplea1 b1 T

a2 b2 T

a3 b3 F

a4 b4 T

a5 b5 F

20 October 2014 Slide 5

a1

c1

F

a1

c2

F

a1

c3

T

a2

c4

T

a6

c5

F

a1 b1

c1 F

a1 b1

c2 F

a1 b1

c3 T

a2 b2

c4 T

60%

40%

50%

a1

b1

T

a2

b2

T

a3

b3

T

a4

b4

T

a5

b5

T

a1 c1 F

a1 c2 F

a1 c3 T

a2 c4 T

a6 c5 F

a1 b1 c1 F

a1 b1 c2 F

a1 b1 c3 T

a2 b2 c4 T100%

40%

50%

a1 b1 T

a2 b2 T

a3 b3 F

a4 b4 T

a5 b5 F

a1

c1

T

a1

c2

T

a1

c3

T

a2

c4

T

a6

c5

T

a1 b1 c1 T

a1 b1 c2 T

a1 b1 c3 T

a2 b2 c4 T60%

100%

100%

Research questions

•What is the least costly maintenance plan that fulfils response quality requirements.

• What is the quality of response without maintenance?• what is the quality of response of each “maintenance

plan”.


Experiment

•We use BSBM benchmark to create a dataset and a query set.

•We label triples with true/false to specify their freshness status.

•We summarize the cache to estimate the quality of a query response without actually executing the query on cache.

•To summarize the cache we extended the cardinality estimation techniques for freshness estimation problem.

Slide 7

Alice

Lives Dublin True

Bob Lives Berlin False

Alice

Job Teacher True

Bob Job Developer

False


Cardinality Estimation•Capture the data distribution by splitting data into buckets and only keep the bucket cardinality in the summary.

Slide 8

Alice Job Teacher

Alice Lives Dublin

Alice Job PhD student

Alice Lives Athlon

Bob Job Manager

Bob Lives Berlin

Bob Lives Chicago

Bob Lives Munich

Bob Lives Belfast

Bob Lives Limerick

Bob Job CEO

Bob Job Consultant

Alice Job * 2 Bob Job * 3 Alice Lives * 2 Bob Lives * 5

* Job * 5 * Lives * 7

Freshness

True

True

False

False

True

True

True

False

False

False

False

False

2

3

1

1

1

2

Q1: ?a Job ?bQ2: (?a Job ?b)^(?a Lives ?c)

Estimated

Actual

5 5

35 19

Estimated

Actual

5 5

19 19

Estimated

Actual

2/5 2/5

6/35 3/19

Estimated

Actual

2/5 2/5

3/19 3/19


Cardinality Estimation Approaches•System R assumptions for cardinality estimation:

• data is uniformly distributed per attribute.• join predicates are independent.

• Indexing approaches make both assumptions.

•Histogram captures the distribution of attributes for more accurate estimation.

•Probabilistic Graphical Models captures dependencies among attributes.

Slide 9


Measure accuracy of the estimation approach

Slide 10

n is the number of queries

Measure the difference between the actual and estimated freshness of queries in a query set.

Preliminary results


Conclusion

•We proposed a new approach for on-demand view maintenance based on the response quality requirements.

•We defined quality requirements based on freshness and completeness.

•We summarized a synthetic dataset to estimate the freshness of various queries using indexing and histogram for our freshness estimation problem.

•Using probabilistic graphical model to summarize the dataset is the future work and it is promising to reduce the estimation error.

Slide 12


Thanks a lot for your attention !

Any question is welcomed!

Slide 13

Optimizing SPARQL Query Processing On Dynamic and Static Data Based on Query Time/Freshness...

Education

Transcript of Optimizing SPARQL Query Processing On Dynamic and Static Data Based on Query Time/Freshness...