The gist of everything new: personalized top-k processing over web 2.0 streams
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Efficient monitoring of personalized hot news over Web 2.0 streams
Computer Science - Research and Development
Distributed top-k full-text content dissemination
Distributed and Parallel Databases
Processing continuous text queries featuring non-homogeneous scoring functions
Proceedings of the 21st ACM international conference on Information and knowledge management
Evaluating continuous top-k queries over document streams
World Wide Web
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A text filtering system monitors a stream of incoming documents, to identify those that match the interest profiles of its users. The user interests are registered at a server as continuous text search queries. The server constantly maintains for each query a ranked result list, comprising the recent documents (drawn from a sliding window) with the highest similarity to the query. Such a system underlies many text monitoring applications that need to cope with heavy document traffic, such as news and email monitoring. In this paper, we propose the first solution for processing continuous text queries efficiently. Our objective is to support a large number of user queries while sustaining high document arrival rates. Our solution indexes the streamed documents with a structure based on the principles of the inverted file, and processes document arrival and expiration events with an incremental threshold-based method. Using a stream of real documents, we experimentally verify the efficiency of our approach, which is at least an order of magnitude faster than a competitor constructed from existing techniques.