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CIRCE: correcting imprecise readings and compressing excrescent points for querying common patterns in uncertain sensor streams

He, Jing, Zhang, Yanchun, Huang, Guangyan and de Souza, Paulo 2013, CIRCE: correcting imprecise readings and compressing excrescent points for querying common patterns in uncertain sensor streams, Information systems, vol. 38, no. 8, pp. 1234-1251, doi: 10.1016/j.is.2012.01.003.

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Title CIRCE: correcting imprecise readings and compressing excrescent points for querying common patterns in uncertain sensor streams
Author(s) He, Jing
Zhang, Yanchun
Huang, GuangyanORCID iD for Huang, Guangyan orcid.org/0000-0002-1821-8644
de Souza, Paulo
Journal name Information systems
Volume number 38
Issue number 8
Start page 1234
End page 1251
Total pages 18
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2013-11
ISSN 0306-4379
Summary Continuous sensor stream data are often recorded as a series of discrete points in a database from which knowledge can be retrieved through queries. Two classes of uncertainties inevitably happen in sensor streams that we present as follows. The first is Uncertainty due to Discrete Sampling (DS Uncertainty); even if every discrete point is correct, the discrete sensor stream is uncertain – that is, it is not exactly like the continuous stream – since some critical points are missing due to the limited capabilities of the sensing equipment and the database server. The second is Uncertainty due to Sampling Error (SE Uncertainty); sensor readings for the same situation cannot be repeated exactly when we record them at different times or use different sensors since different sampling errors exist. These two uncertainties reduce the efficiency and accuracy of querying common patterns. However, already known algorithms generally only resolve SE Uncertainty. In this paper, we propose a novel method of Correcting Imprecise Readings and Compressing Excrescent (CIRCE) points. Particularly, to resolve DS Uncertainty, a novel CIRCE core algorithm is developed in the CIRCE method to correct the missing critical points while compressing the original sensor streams. The experimental study based on various sizes of sensor stream datasets validates that the CIRCE core algorithm is more efficient and more accurate than a counterpart algorithm to compress sensor streams. We also resolve the SE Uncertainty problem in the CIRCE method. The application for querying longest common route patterns validates the effectiveness of our CIRCE method.
Language eng
DOI 10.1016/j.is.2012.01.003
Field of Research 080109 Pattern Recognition and Data Mining
0806 Information Systems
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1.1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2013, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30083662

Document type: Journal Article
Collection: School of Information Technology
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