Efficient query subscription processing for prospective search engines
Proceedings of the 15th international conference on World Wide Web
Keyword search on relational data streams
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Keyword search over relational tables and streams
ACM Transactions on Database Systems (TODS)
Vlogging: A survey of videoblogging technology on the web
ACM Computing Surveys (CSUR)
Incremental diversification for very large sets: a streaming-based approach
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Characterizing web syndication behavior and content
WISE'11 Proceedings of the 12th international conference on Web information system engineering
Subscription indexes for web syndication systems
Proceedings of the 15th International Conference on Extending Database Technology
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Current web search engines are retrospective in that they limit users to searches against already existing pages. Prospective search engines, on the other hand, allow users to upload queries that will be applied to newly discovered pages in the future. Some examples of prospective search are the subscription features in Google News and in RSS-based blog search engines. In this paper, we study the problem of efficiently processing large numbers of keyword query subscriptions against a stream of newly discovered documents, and propose several query processing optimizations for prospective search. Our experimental evaluation shows that these techniques can improve the throughput of a well known algorithm by more than a factor of 20, and allow matching hundreds or thousands of incoming documents per second against millions of subscription queries per node.