2012年
A stream query language TPQL for anomaly detection in facility management
ACM International Conference Proceeding Series
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- 開始ページ
- 235
- 終了ページ
- 238
- 記述言語
- 英語
- 掲載種別
- 研究論文(国際会議プロシーディングス)
- DOI
- 10.1145/2351476.2351506
In facility management for plants and buildings, needs of facility diagnosis for saving energy or facility management cost by analyzing time series data from sensors of equipments in facilities have been increasing. This paper proposes a relation-based stream query language TPQL (Trend Pattern Query Language) for expressing constraints in time series data for anomalies detection in facilities. The features of TPQL are the following. (1) TPQL introduces a convolution operator into a stream query language in order to describe constraints over sliding window. A convolution operator which takes a window function as an argument can express various domain dependent functions extracting feature over sliding windows such as duration constraint and hunting constraint. (2) TPQL introduces time-interval based join into stream query language in order to join time series data with different sampling rates. Copyright © 2012 ACM.
- リンク情報
- ID情報
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- DOI : 10.1145/2351476.2351506
- SCOPUS ID : 84866618098