p37-cheng - Adaptive Stream Filters for Entity-based...

Info iconThis preview shows pages 1–2. Sign up to view the full content.

View Full Document Right Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: Adaptive Stream Filters for Entity-based Queries with Non-Value Tolerance Reynold Cheng Ben Kao Sunil Prabhakar Alan Kwan Yicheng Tu Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong. Email: csckcheng@comp.polyu.edu.hk Department of Computer Science, The University of Hong Kong, Pokfulam Road, Hong Kong. Email: { kao,klkwan } @cs.hku.hk Department of Computer Science, Purdue University, West Lafayette, IN 47907-1398, USA. Email: { sunil,tuyc } @cs.purdue.edu Abstract We study the problem of applying adaptive filters for approximate query processing in a distributed stream environment. We propose filter bound assignment protocols with the ob- jective of reducing communication cost. Most previous works focus on value-based queries (e.g., average) with numerical error tolerance. In this paper, we cover entity-based queries (e.g., nearest neighbor) with non-value-based error tolerance. We investigate different non- value-based error tolerance definitions and discuss how they are applied to two classes of entity-based queries: non-rank-based and rank-based queries. Extensive experiments show that our protocols achieve significant savings in both communication overhead and server computation. 1 Introduction Due to the rapid development of low-cost sensors and networking technologies, stream applications have at- tracted tremendous research interests lately. In par- ticular, long-standing continuous queries are common in a stream environment for monitoring various net- work activities. Some examples include intrusion de- tection over security-sensitive regions; identification of Denial-of-Service (DOS) attacks on the Internet [2]; Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Very Large Data Base Endowment. To copy otherwise, or to republish, requires a fee and/or special permission from the Endowment. Proceedings of the 31st VLDB Conference, Trondheim, Norway, 2005 road traffic monitoring; network fault-detection; email spams detection; and web statistics collection. In such systems, streams are installed that collect and report the states of various entities. For example, in DoS detection, routes through which traffic is abnor- mally high are identified. Addresses from and to which packet frequencies rank among the top few might sig- nal alerts. The number of streams could be large and they are continuously reporting updates. A stream server could thus be crippled by the large volume of data, slowing its response to standing queries that re- quire real-time processing [1]. One possible solution is to trade query answer accuracy for speed. For exam- ple, a sensor that is reporting a temperature reading can be instructed not to transmit updates to the server...
View Full Document

Page1 / 12

p37-cheng - Adaptive Stream Filters for Entity-based...

This preview shows document pages 1 - 2. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online