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MINT Views: Materialized In-Network Top-k Views in Sensor Networks

DocUID: 2007-009 Full Text: PDF

Author: Demetrios Zeinalipour-Yazti, Panayiotis Andreou, Panos K. Chrysanthis, George Samaras

Abstract: In this paper we introduce MINT (Materialized In- Network Top-k) Views, a novel framework for optimizing the execution of continuous monitoring queries in sensor networks. A typical materialized view V maintains the complete results of a query Q in order to minimize the cost of future query executions. In a sensor network context, maintaining consistency between V and the underlying and distributed base relation R is very expensive in terms of communication. Thus, our approach focuses on a subset V' (⊆V ) that unveils only the k highest-ranked answers at the sink for some user defined parameter k. We additionally provide an elaborate description of energy-conscious algorithms for constructing, pruning and maintaining such recursivelydefined in-network views. Our trace-driven experimentation with real datasets show that MINT offers significant energy reductions compared to other predominant data acquisition models.

Keywords: View Management, Top-K Query Processing,

Published In: Proc. of the 7th International Conference in Mobile Data Management

Pages: pp. 182-189

Place Published: Mannheim, Germany

Year Published: 2007

Project: AQSIOS,  MINT Subject Area: Sensor Databases

Publication Type: Conference Paper

Sponsor: NSF IIS-0534531, NSF ITR ANI-0325353

Citation:Text Latex BibTex XML Demetrios Zeinalipour-Yazti, Panayiotis Andreou, Panos K. Chrysanthis, and George Samaras. MINT Views: Materialized In-Network Top-k Views in Sensor Networks. Proc. of the 7th International Conference in Mobile Data Management. pp. 182-189. 2007. Mannheim, Germany.