Maintaining views incrementally
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Graph Structured Views and Their Incremental Maintenance
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
RDF Aggregate Queries and Views
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Scalable join processing on very large RDF graphs
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
YARS2: a federated repository for querying graph structured data from the web
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
A budget-based algorithm for efficient subgraph matching on Huge Networks
ICDEW '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering Workshops
Live linked data: synchronising semantic stores with commutative replicated data types
International Journal of Metadata, Semantics and Ontologies
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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 define 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.