A System for Keyword Search on Textual Streams

Abstract

An increasing amount of data is produced in the form of text streams − these can be RSS news feeds, TV closed captions, emails, etc. We study the problem of answering keyword queries on multiple textual streams. We define the result of a keyword query inspired by previous work on keyword search on static databases. A result to a query is a combination of streams "sufficiently correlated" to each other that collectively contain all query keywords within a specified time span. On the algorithmic side, in this paper we focus on the component of continuously monitoring the streams and outputting results as soon as they are available.

Characterizing Web Syndication Behavior and Content

“…Unfortunately, previous works on RSS/Atom statistical characteristics [15,27,13] do not provide a precise and updated characterization of feeds' behavior and content which could be effectively used for tuning refreshing policies of RSS aggregators [24,22], benchmarking scalability and performance of RSS continuous monitoring mechanisms [19,9,7,8,25,5] or comparing various techniques for RSS items mining, recommendation, enrichment and archiving [3,26]. In this paper, we present the first thorough analysis of three complementary features of real-scale RSS/Atom feeds, namely, publication activity, items structure and length, as well as, vocabulary of the textual content.…”