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Approximate Top-k Queries Monitoring on Document Streams

Abstract
Document stream is the stream where documents are flows continuously. By monitoring these documents it is possible to have different applications of the real world like demand presentation, contextual advertisements, filtering of news updates, and general filtering of information to meet the needs of users. User preferences are used to process the top-k monitoring of documents streams continuously. However, it is a tedious and challenging task to fulfil the aspirations of various users and their preferences. In the literature many solutions are found. However an adaptive approach is essential to achieve better results. In this paper we proposed a framework and implemented to have continuous monitoring and approximation of document streams to Top-k queries of different users. Thus the proposed system yields more utility to end users than existing system. Top-k queries instead of preferences can provide the intent of users more clearly. Thus the filtered documents can reveal the user intention in making such queries. An algorithm named Adaptive Identifier Ordering (AIO) is implemented to achieve this. AIO adapts to the runtime dynamics of streaming besides using top-k queries to reports users with most appropriate documents. We build a prototype application to demonstrate proof of the concept.

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Keywords – Document streams, top-k queries, continuous monitoring, adaptive identifier ordering

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