A stream query language TPQL for anomaly detection in facility management
A stream query language TPQL for anomaly detection in facility management
复制标题
用于设施管理中异常检测的流查询语言 TPQL
DOI：
10.1145/2351476.2351506
复制
发表时间：
2012
影响因子：
4.8
通讯作者：
T. Munaka
中科院分区：
文献类型：
--
作者：
Makoto Imamura;S. Takayama;T. Munaka
在工厂和建筑物的设施管理中，通过分析来自设施内设备的传感器的时间序列数据来进行设施诊断以节省能源或设施管理成本的需求不断增加。本文提出了一种基于关系的流查询语言TPQL（趋势模式查询语言），用于表达时间序列数据中的约束，以进行设施异常检测。 TPQL的特点如下。 (1) TPQL在流查询语言中引入卷积算子来描述滑动窗口的约束。以窗口函数为参数的卷积算子可以表达各种域相关函数，在滑动窗口上提取特征，例如持续时间约束和狩猎约束。 (2) TPQL在流查询语言中引入基于时间间隔的连接，以连接不同采样率的时间序列数据。
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.
影响因子：
4.2
作者：
Arasu, A;Babu, S;Widom, J
通讯作者：
Widom, J