Semantic preprocessing for mining sensor streams from heterogeneous environments
- Authors
- Jung, Jason J.
- Issue Date
- May-2011
- Publisher
- PERGAMON-ELSEVIER SCIENCE LTD
- Keywords
- Ontology; Semantic sensor networks; Data streams; Preprocessing; Stream mining
- Citation
- EXPERT SYSTEMS WITH APPLICATIONS, v.38, no.5, pp 6107 - 6111
- Pages
- 5
- Journal Title
- EXPERT SYSTEMS WITH APPLICATIONS
- Volume
- 38
- Number
- 5
- Start Page
- 6107
- End Page
- 6111
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/37751
- DOI
- 10.1016/j.eswa.2010.11.017
- ISSN
- 0957-4174
1873-6793
- Abstract
- Many studies have tried to employ data mining methods to discover useful patterns and knowledge from data streams on sensor networks. However, it is difficult to apply such data mining methods to the sensor streams intermixed from heterogeneous sensor networks. In this paper, to improve the performance of conventional data mining methods, we propose an ontology-based data preprocessing scheme, which is composed of two main phases: (i) session identification and (ii) error detection. The ontology can provide and describe semantics of data measured by each sensor. Thus, by comparing the semantics, we can find out not only relationships between sensor streams but also temporal dynamics of a data stream. To evaluate the proposed method, we have collected sensor streams from in our building during 30 days. By using two well-known data mining methods (i.e., co-occurrence pattern and sequential pattern), the results from raw sensor streams and ones from sensor streams with preprocessing were compared with respect to two measurements recall and precision. (C) 2010 Elsevier Ltd. All rights reserved.
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Collections - College of Software > School of Computer Science and Engineering > 1. Journal Articles
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