efficient multi-view maintenance in the social semantic web
DESCRIPTION
This poster describes an efficient approach to maintaining multiple views on large, evolving social networks. Abstract: The Social Semantic Web (SSW) refers to the mix of RDF data in web content, and social network data associated with those who posted that content. Applications to monitor the SSW are becoming increasingly popular. For instance, marketers want to look for semantic patterns relating to the content of tweets and Facebook posts relating to their products. Such applications allow multiple users to specify patterns of interest, and monitor them in real-time as new data gets added to the web or to a social network. In this paper, we develop the concept of SSW view servers in which all of these types of applications can be simultaneously monitored from such servers. The patterns of interest are views. We show that a given set of views can be compiled in multiple possible ways to take advantage of common substructures, and de fine the concept of an optimal merge. We develop a very fast MultiView algorithm that scalably and efficiently maintains multiple subgraph views. We show that our algorithm is correct, study its complexity, and experimentally demonstrate that our algorithm can scalably handle updates to hundreds of views on real-world SSW databases with up to 540M edges.TRANSCRIPT
Efficient Multi-View Maintenance in the Social Semantic Web
Views on Social Networks
Query = Subgraph matching query View = Query for which the answer set is maintained as the social network database is updated
1 Multiple Views
2 Merging Views
3
Example Merge
4 Merge Optimality
5 Optimal Merge
6
7 8 9
Matthias Broecheler, Andrea Pugliese, and VS Subrahmanian
• On large social networks, multiple views are often maintained concurrently. • Maintaining multiple views is very expensive, in particular for rapidly changing databases. • e.g. Twitter has over 340 million tweets / day
IDEA: Very often, view queries have overlapping subgraph structures (bold arcs). If we can overlay the different view queries such that these shared substructures can be matched jointly rather than independently, we can save a lot of time. Social network updates are edge insertions (removals), hence graph merging has to center around the inserted edge type. Developed subgraph matching algorithm that can process merged view queries efficiently.
Many possible ways to merge query graphs. We want high connected overlap which results in most savings at update time. Define merged view score as the sum of edge
overlaps. Finding the optimal view wrt the merged view score is NP–hard.
Our greedy view merging algorithm finds near optimal views in practice.
Experiments
Compared our merged multi-view maintenance algorithm against standard independent view maintenance. 6 real world social network datasets with up to 540 million edges.
Randomly generated 12,000 queries with varying degree of overlap and averaged results over 750 trials.
All algorithm implemented in Java on top of the COSI graph database middleware.
Performance improvement of the Multi-View Maintenance algorithm on 6 different social networks
Applications
Maintaining multiple views jointly as a merged view leads to significant improvements. 477% faster than standard view maintenance
Applications include: Monitoring social networks Fraud, security applications, alerts
Business Analytics Knowledge Discovery Caching frequently asked queries
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