Information discovery across multiple streams

In this paper we address the issue of continuous keyword queries on multiple textual streams and explore techniques for extracting useful information from them. The paper represents, to our best knowledge, the first approach that performs keyword search on a multiplicity of textual streams. The scen...

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Veröffentlicht in:Information sciences 2009-09, Vol.179 (19), p.3268-3285
Hauptverfasser: Hristidis, Vagelis, Valdivia, Oscar, Vlachos, Michail, Yu, Philip S.
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container_title Information sciences
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creator Hristidis, Vagelis
Valdivia, Oscar
Vlachos, Michail
Yu, Philip S.
description In this paper we address the issue of continuous keyword queries on multiple textual streams and explore techniques for extracting useful information from them. The paper represents, to our best knowledge, the first approach that performs keyword search on a multiplicity of textual streams. The scenario that we consider is quite intuitive; let’s assume that a research or financial analyst is searching for information on a topic, continuously polling data from multiple (and possibly heterogeneous) text streams, such as RSS feeds, blogs, etc. The topic of interest can be described with the aid of several keywords. Current filtering approaches would just identify single text streams containing some of the keywords. However, it would be more flexible and powerful to search across multiple streams, which may collectively answer the analyst’s question. We present such model that takes in consideration the continuous flow of text in streams and uses efficient pipelined algorithms such that results are output as soon as they are available. The proposed model is evaluated analytically and experimentally, where the Enron dataset and a variety of blog datasets are used for our experiments.
doi_str_mv 10.1016/j.ins.2009.06.008
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subjects Continuous queries
Correlation
Keyword search
Real-time search
Streams
title Information discovery across multiple streams
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