Validated cost models for sensor network queries

Generating a good execution plan for a declarative query has long been a central problem in data management research. With the rise in interest in wireless sensor networks (WSNs) as query processing platforms, it was quickly noticed that the corresponding optimization problem is even more challenging than the classical one, since, in comparison to classical platforms, a WSN is a very constrained computational infrastructure (in terms of memory, processing, and communication capabilities, and, crucially, depletable energy stocks). Optimizing a declarative query for execution in WSNs is thereby made both more important and more challenging. One of the requirements for effective query optimization is the availability of effective models for estimating the cost of alternative execution plans. This paper describes how query cost models for space, time and energy were methodically derived and validated for an expressive algebra for continuous queries over sensor streams.

[1]  B Praveen Kumar,et al.  Mariposa a Wide-Area Distributed Database System , 2010, ICCA 2010.

[2]  Laura M. Haas,et al.  Cost Models DO Matter: Providing Cost Information for Diverse Data Sources in a Federated System , 1999, VLDB.

[3]  Qiang Zhu,et al.  Developing cost models with qualitative variables for dynamic multidatabase environments , 2000, Proceedings of 16th International Conference on Data Engineering (Cat. No.00CB37073).

[4]  David J. DeWitt,et al.  Design and evaluation of alternative selection placement strategies in optimizing continuous queries , 2002, Proceedings 18th International Conference on Data Engineering.

[5]  Donald D. Chamberlin,et al.  Access Path Selection in a Relational Database Management System , 1989 .

[6]  Jeffrey F. Naughton,et al.  Rate-based query optimization for streaming information sources , 2002, SIGMOD '02.

[7]  Jeffrey F. Naughton,et al.  Toward a progress indicator for database queries , 2004, SIGMOD '04.

[8]  Jennifer Widom,et al.  STREAM: The Stanford Stream Data Manager , 2003, IEEE Data Eng. Bull..

[9]  Christian Y. A. Brenninkmeijer,et al.  A Semantics for a Query Language over Sensors, Streams and Relations , 2008, BNCOD.

[10]  Jim Smith,et al.  Measuring and modelling the performance of a parallel ODMG compliant object database server , 2006, Concurr. Comput. Pract. Exp..

[11]  Christian Y. A. Brenninkmeijer,et al.  Comprehensive Optimization of Declarative Sensor Network Queries , 2009, SSDBM.

[12]  Minos N. Garofalakis,et al.  Parallel Query Scheduling and Optimization with Time- and Space-Shared Resources , 1997, VLDB.

[13]  Johannes Gehrke,et al.  Query Processing in Sensor Networks , 2003, CIDR.

[14]  Surajit Chaudhuri,et al.  An overview of query optimization in relational systems , 1998, PODS.

[15]  Christian Y. A. Brenninkmeijer,et al.  An Architecture for Query Optimization in Sensor Networks , 2008, 2008 IEEE 24th International Conference on Data Engineering.

[16]  Jens Palsberg,et al.  Avrora: scalable sensor network simulation with precise timing , 2005, IPSN 2005. Fourth International Symposium on Information Processing in Sensor Networks, 2005..