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1 gStore: Answering SPARQL Queries Via Subgraph Matching Presented by Guan Wang Kent State University October 24, 2011.

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Presentation on theme: "1 gStore: Answering SPARQL Queries Via Subgraph Matching Presented by Guan Wang Kent State University October 24, 2011."— Presentation transcript:

1 1 gStore: Answering SPARQL Queries Via Subgraph Matching Presented by Guan Wang Kent State University October 24, 2011

2 2 Outline RDF & SPARQL Previous Solutions for SPARQL Queries Overview of gStore Encoding Technique VS*-tree & Query Algorithm Experiments Conclusions

3 3 RDF & SPARQL Previous Solutions for SPARQL Queries Overview of gStore Encoding Technique VS*-tree & Query Algorithm Experiments Conclusions Outline

4 4 What is RDF A general-purpose framework provides structured, machine-understandable metadata for the Web It is based upon the idea of making statements about resources in the form of subject-predicate-object expressions. These expressions are known as triples in RDF. SubjectObject Predicate Statement

5 5 RDF Model Example page.html Guan Guan’s Home Page Creator Title SubjectPredicateObject page.htmlCreatorGuan page.htmlCreatorGuan's Home Page

6 6 What is SPARQL SPARQL is a query language for RDF. It provides a standard format for writing queries that target RDF data and a set of standard rules for processing those queries and returning the results. The building blocks of a SPARQL queries are graph patterns that include variables. The result of the query will be the values that these variables must take to match the RDF graph.

7 7 Example of SPARQL Select ?name Where { ?m ?name. ?m “ ”. ?m “ ”. } Names beginning with a ? or a $ are variables. Graph patterns are given as a list of triple patterns enclosed within braces {} The variables named after the SELECT keyword are the variables that will be returned as results. (~SQL) Here each of the conjunctions, denoted by a dot, corresponds to a join.

8 8 RDF Graph

9 9 SPARQL Queries Query Graph SPARQL Query: Select ?name Where { ?m ?name. ?m “ ”. ?m “ ”. }

10 10 Subgraph Match vs. SPARQL Queries

11 11 RDF & SPARQL Previous Solutions for SPARQL Queries Overview of gStore Encoding Technique VS*-tree & Query Algorithm Experiments Conclusions Outline

12 12 Existing Solutions-Three Column Table SPARQL Query: Select ?name Where { ?m ?name. ?m “ ”. ?m “ ”. } Shortage: Too Many Self-Joins

13 13 Shortage: A Big Waste of Space Existing Solutions-Property Table

14 14 Existing Solutions-Vertically Partitioned Shortage: Too Many Merge Joins

15 15 Existing Solutions-RDF-3x Shortage: Different to Handle Updates Utilize the characteristic of RDF, that there are only three elements(subject, object and predicate) in RDF. Construct all six possible indexes and optimalize merge orders.

16 16 RDF & SPARQL Previous Solutions for SPARQL Queries Overview of gStore Encoding Technique VS*-tree & Query Algorithm Experiments Conclusions Outline

17 17 Overview of gStore(Store) Represent an RDF dataset by an RDF graph G and store it by its adjacency list table.

18 18 Overview of gStore(Encoding) Encode each entity and class vertex into a bitstring, called signature. Link these vertex signatures to form a data signature graph G according to RDF graph’s structure

19 19 Overview of gStore(VS*-tree)

20 20 RDF & SPARQL Previous Solutions for SPARQL Queries Overview of gStore Encoding Technique VS*-tree & Query Algorithm Experiments Conclusions Outline

21 21 Encoding Technique

22 22 Encoding Technique

23 23 RDF & SPARQL Previous Solutions for SPARQL Queries Overview of gStore Encoding Technique VS*-tree & Query Algorithm Experiments Conclusions Outline

24 24 VS*-tree Each leaf node of the tree corresponds to one vertex signature in G. Given two leaf nodes d1 and d2 in the tree, we introduce an edge between them, if and only if there is an edge between d1 and d2 in G Given nodes d1 and d2 in the tree, we introduce a super edge from d1 to d2, if and only if there is at least one edge from d1’s children to d2’s children. Assign an edge label for the edge d1→ d2 by performing bitwise “OR” over these n edge labels from d1’s children to d2’s children.

25 25 VS*-tree

26 26 Query Algorithm

27 27 RDF & SPARQL Previous Solutions for SPARQL Queries Overview of gStore Encoding Technique VS*-tree & Query Algorithm Experiments Conclusions Outline

28 28 Experiments Used datasets: Yago, DBLP which are popular semantic datasets with millions of triples. Data size: approximately 4GB.

29 29 Experiments(Exact Queries)

30 30 Experiments(Wildcard Queries)

31 31 RDF & SPARQL Previous Solutions for SPARQL Queries Overview of gStore Encoding Technique VS*-tree & Query Algorithm Experiments Conclusions Outline

32 32 Conclusions Propose to store and query RDF data from graph database perspective. Using VS*-tree as indexing method for bitstring of vertices, which supports the SPARQL queries in a scalable manner. False positive.

33 33 Reference [ICDE09]Thanh Tran, Haofen Wang, Sebastian Rudolph, Philipp Cimiano, "Top-k Exploration of Query Candidates for Efficient Keyword Search on Graph-Shaped (RDF) Data", DOI /ICDE [VLDB07]Daniel J. Abadi, Adam Marcus, Samuel R. Madden,Kate Hollenbach, "Scalable Semantic Web Data Management Using Vertical Partitioning", VLDB ‘07, September 2328, 2007, Vienna, Austria. [PVLDB08]Cathrin Weiss, Panagiotis Karras, Abraham Bernstein, "Hexastore:Sextuple Indexing for Semantic Web Data Management",PVLDB '08, August 23-28, 2008, Auckland, New Zealand [PVLDB08]Thomas Neumann, Gerhard Weikum, "RDF3X:a RISCstyle Engine for RDF",PVLDB '08, August 23-28, 2008, Auckland, New Zealand [VLDB11]Lei Zou, Jinghui Mo, Lei Chen, M. Tamer O¨ zsu, Dongyan Zhao, "gStore: Answering SPARQL Queries via Subgraph Matching" VLDB‘11,August 29th - September 3rd 2011, Seattle, Washington. Thank you!


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