Semantic preprocessing for mining sensor streams from heterogeneous environments

Title
Semantic preprocessing for mining sensor streams from heterogeneous environments
Author(s)
정재은
Keywords
WEB; NETWORK; FRAMEWORK
Issue Date
201105
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Citation
EXPERT SYSTEMS WITH APPLICATIONS, v.38, no.5, pp.6107 - 6111
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.
URI
http://hdl.handle.net/YU.REPOSITORY/25218http://dx.doi.org/10.1016/j.eswa.2010.11.017
ISSN
0957-4174
Appears in Collections:
공과대학 > 컴퓨터공학과 > Articles
Files in This Item:
There are no files associated with this item.
Export
RIS (EndNote)
XLS (Excel)
XML


qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

BROWSE