Article: Efficient iceberg join processing in wireless sensor networks Journal: International Journal of Embedded Systems (IJES) 2017 Vol.9 No.4 pp.365 - 378 Abstract: A new class of monitoring applications is emerging, in which multiple embedded devices are deployed to sense the physical world and a large amount of data is injected into the network. Yet, existing monitoring algorithms usually output a result set that is trivial for users and too expensive for the resource-constraint network. In this paper, we study the problem of iceberg join processing in wireless sensor networks. The iceberg join query only includes a small fraction of data in its result set, yet, still contains the most 'interesting' and useful data relationships and linkages of the sensing data. The proposed algorithm SRJA is output sensitive and adopts a progressive refinement strategy for the query processing. Our algorithm first constructs flexible synopses according to the characteristics of the joining data, and then progressively refines these synopses to identify tuples that can match and meet the iceberg threshold in the joining regions. It fully utilises the iceberg threshold to filter out tuples that do not contribute to the final result set at early stages, saving lots of transmissions. Extensive experiments indicate that our algorithm gains a reduction up to 25% of message transmissions compared with other schemes. Inderscience Publishers - linking academia, business and industry through research

Title: Efficient iceberg join processing in wireless sensor networks

Authors: Yongxuan Lai; Xing Gao; Tian Wang; Ziyu Lin

Addresses: School of Software, Xiamen University, 422 Siminnan Road, Xiamen 361005, Fujian, China ' School of Software, Xiamen University, 422 Siminnan Road, Xiamen 361005, Fujian, China ' College of Computer Science and Technology, Huaqiao University, 668 Jimei Avenue, Xiamen 361021, Fujian, China ' Department of Computer Science, Xiamen University, 422 Siminnan Road, Xiamen 361005, Fujian, China

Abstract: A new class of monitoring applications is emerging, in which multiple embedded devices are deployed to sense the physical world and a large amount of data is injected into the network. Yet, existing monitoring algorithms usually output a result set that is trivial for users and too expensive for the resource-constraint network. In this paper, we study the problem of iceberg join processing in wireless sensor networks. The iceberg join query only includes a small fraction of data in its result set, yet, still contains the most 'interesting' and useful data relationships and linkages of the sensing data. The proposed algorithm SRJA is output sensitive and adopts a progressive refinement strategy for the query processing. Our algorithm first constructs flexible synopses according to the characteristics of the joining data, and then progressively refines these synopses to identify tuples that can match and meet the iceberg threshold in the joining regions. It fully utilises the iceberg threshold to filter out tuples that do not contribute to the final result set at early stages, saving lots of transmissions. Extensive experiments indicate that our algorithm gains a reduction up to 25% of message transmissions compared with other schemes.

Keywords: synopsis refinement; iceberg join; query processing; sensor network.

DOI: 10.1504/IJES.2017.086120

International Journal of Embedded Systems, 2017 Vol.9 No.4, pp.365 - 378

Received: 23 Oct 2015
Accepted: 19 Dec 2015
Published online: 27 Aug 2017 *

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Title: Efficient iceberg join processing in wireless sensor networks

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