This document summarizes different approaches for managing web data and querying semi-structured data. It discusses challenges like lack of schemas, scale, and volatility of web data. It then describes approaches like property tables, binary tables, and graph-based approaches using the gStore and VS-Tree systems. The document concludes that graph-based approaches like VS-Tree have the best performance and that gStore is more efficient than other approaches for querying RDF triple stores on the web.

1. Web Data Management
Advanced Database Presentation
By:
Navid Sedighpour
Professor :
Dr. Alireza Bagheri
Nevember 2015
1

2. Interest
Lack of schema
Data is unstructured or at best “semi-structured”
Missing data, additional attributes, similar data but not identical
Volatility
May confirm to one schema now, but not later
Scale
How to capture everything?
Querying Difficulty
What is the user language?
What are the primitives?
Aren’t Search Engines sufficient?
2

3. Fusion Tables
Users contribute data in spreadsheet
Possible joins between multiple data sets
Extensive visualization
3
More Recent Approaches to Web Querying

4. More Recent Approaches to Web Querying
XML
Data exchange language
Tree based structure
4

5. More Recent Approaches to Web Querying
RDF
W3C Recommendation
Simple, self-descriptive model
5

6. RDF Data Volumes
90% of world's data generated over last two years
Data are growing fast
Size almost doubling every year
6

7. RDF Data Volumes
March 2009 – 89 Datasets
7

8. RDF Data Volumes
September 2010 – 203 datasets
8

9. RDF Data Volumes
September 2011 – 295 Datasets
9

10. RDF Data Volumes
10
April 2014 – 1091 Datasets

11. RDF Introduction
Everything is an uniquely named resource
Prefixes can be used to shorten names
Properties of resources can be defined
Relationships with other resources can be defined
Resource description can be contributed by different people/groups and can be located anywhere
in the web
Integrated web “database”
11

12. RDF Data Model
Triple : Subject, Predicate (Property) , Object
Subject : The entity that is described (URI or Blank Node)
Predicate : a feature of the entity
Object : value of the feature
Set of RDF Triples is called “RDF Graph”
12

13. RDF Example Instance
13

14. RDF Graph
14

15. SPARQL Queries
15

16. Naïve Triple Store Design
16

17. 17
Naïve Triple Store Design
Easy to Implement
But
Too Many self-joins

18. Property Tables
Grouping by Entities
Types :
Clustered Property Tables
Property Class Tables
18

19. Clustered Property Tables
Group together the properties that tend to occur in the same (or similar) subjects
19

20. Property Class Tables
Cluster the subjects with the same type of property into one property table
20

21. Property Tables
Advantages :
Fewer Joins
Disadvantages :
Lots of NULLs
Clustering is not trivial
Multi-valued properties are complicated
21

22. Binary Tables
Grouping by Properties: for each property build a two column table containing both subject and
object, ordered by subjects
Also called “Vertically Partitioned Approach”
N two column tables (n is the number of unique properties in the data)
22

23. Binary Tables
Advantages :
Support multi-valued Properties
No NULLs
No Clustering
Good performance for subject-subject joins
Disadvantages:
Not useful for subject-subject joins
Expensive inserts
23

24. Graph-Based Approach
Answering SPARQL query = Subgraph Matching
gStore
24

25. Two steps need to be done :
1. For each node of Q* get the lists of nodes in G* that include that node
2. Do a multi-way join to get the candidate list
Alternatives :
Sequential scan of G*
 Both steps are inefficient
S-Tree
 Height Balanced Tree over signatures
 Run an inclusion query for each node of Q* and get lists of nodes in G* that include that node (q & s = q)
VS-Tree
 Support both steps efficiently
 Grouping by vertices
25
Graph-Based Approach

26. S-Tree
26
Pruning

27. S-Tree
27

28. S-Tree
28

29. S-Tree
29

30. S-Tree
30

31. VS-Tree
31

32. VS-Tree
32

33. Conclusion
RDF Data seem to have considerable promise for web data management
We talked about four approaches to web data management including Naïve triple store design,
Property Tables, Binary Tables and Graph-Based approach
VS-Tree has the best performance in Graph-Base approaches
gStore is more efficient than other approaches
33

34. References
34
[1] D. J. Abadi, A. Marcus, S. R. Madden, and K. Hollenbach, "Scalable semantic web data
management using vertical partitioning," in Proceedings of the 33rd international conference on Very large
data bases, 2007, pp. 411-422.
[2] L. Zou, J. Mo, L. Chen, M. T. Özsu, and D. Zhao, "gStore: answering SPARQL queries via
subgraph matching," Proceedings of the VLDB Endowment, vol. 4, pp. 482-493, 2011.
[3] L. Zou, M. T. Özsu, L. Chen, X. Shen, R. Huang, and D. Zhao, "gStore: a graph-based SPARQL
query engine," The VLDB Journal—The International Journal on Very Large Data Bases, vol. 23, pp. 565-
590, 2014.
[4] X. Shen, L. Zou, M. T. Ozsu, L. Chen, Y. Li, S. Han, et al., "A Graph-based RDF Triple Store."

35. 35