Publication Type

Journal Article

Version

acceptedVersion

Publication Date

5-2017

Abstract

The efficient processing of document streams plays an important role in many information filtering systems. Emerging applications, such as news update filtering and social network notifications, demand presenting end-users with the most relevant content to their preferences. In this work, user preferences are indicated by a set of keywords. A central server monitors the document stream and continuously reports to each user the top-k documents that are most relevant to her keywords. Our objective is to support large numbers of users and high stream rates, while refreshing the top-k results almost instantaneously. Our solution abandons the traditional frequency-ordered indexing approach. Instead, it follows an identifier-ordering paradigm that suits better the nature of the problem. When complemented with a novel, locally adaptive technique, our method offers (i) proven optimality w.r.t. the number of considered queries per stream event, and (ii) an order of magnitude shorter response time (i.e., time to refresh the query results) than the current state-of-the-art.

Keywords

Top-k query, Continuous query, Document stream

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Intelligent Systems and Optimization

Publication

IEEE Transactions on Knowledge and Data Engineering

Volume

29

Issue

5

First Page

991

Last Page

1003

ISSN

1041-4347

Identifier

10.1109/TKDE.2017.2657622

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/TKDE.2017.2657622

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