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.

1. Efficient Multi-View Maintenance in
the Social Semantic Web
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Views on Social Networks Multiple Views Merging Views
 Query = Subgraph matching query • On large social networks, multiple views are often IDEA:Very often, view queries have overlapping
 View = Query for which the answer set is maintained concurrently. subgraph structures (bold arcs). If we can overlay the
maintained as the social network database is • Maintaining multiple views is very expensive, in different view queries such that these shared
updated particular for rapidly changing databases. substructures can be matched jointly rather than
publish • e.g. Twitter has over 340 million tweets / day independently, we can save a lot of time.
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Example Merge Merge Optimality Optimal Merge
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 Define merged view score as the sum of edge
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LEGEND LEGEND
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Experiments Mul2-View-Maintenance-Performance- Applications
900%% 100.0%%
800%%
Outperforming-
95.0%%
Maintaining multiple views jointly as a merged view
Improvement-
Compared our merged multi-view maintenance 700%%
600%% 90.0%%
algorithm against standard independent view 500%%
85.0%%
leads to significant improvements.
400%%
maintenance. 300%% 80.0%%  477% faster than standard view maintenance
200%%
 6 real world social network datasets with up to 540 100%%
75.0%%
million edges. 0%% 70.0%% Applications include:
 Monitoring social networks
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 Randomly generated 12,000 queries with varying
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degree of overlap and averaged results over 750 trials.
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 Business Analytics
 All algorithm implemented in Java on top of the COSI Performance improvement of the Multi-View  Knowledge Discovery
graph database middleware.  Caching frequently asked queries
Maintenance algorithm on 6 different social networks
Matthias Broecheler, Andrea Pugliese,
and VS Subrahmanian