Sensors to Events: Semantic Modeling and Recognition of Events from Data Streams

被引:6
|
作者
Patri, Om Prasad [1 ]
Panangadan, Anand V. [2 ]
Sorathia, Vikrambhai S. [3 ]
Prasanna, Viktor K. [4 ]
机构
[1] Univ Southern Calif, Dept Comp Sci, 3740 McClintock Ave, Los Angeles, CA 90089 USA
[2] Calif State Univ Fullerton, Dept Comp Sci, 800 N State Coll Blvd, Fullerton, CA 92381 USA
[3] Kensemble Tech Labs LLP, Gandhinagar, India
[4] Univ Southern Calif, Ming Hsieh Dept Elect Engn, 3740 McClintock Ave, Los Angeles, CA 90089 USA
关键词
Event stream processing; event ontology; information integration; temporal pattern mining; time series shapelets; non-intrusive load monitoring;
D O I
10.1142/S1793351X16400171
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Detecting and responding to real-world events is an integral part of any enterprise or organization, but Semantic Computing has been largely underutilized for complex event processing (CEP) applications. A primary reason for this gap is the difference in the level of abstraction between the high-level semantic models for events and the low-level raw data values received from sensor data streams. In this work, we investigate the need for Semantic Computing in various aspects of CEP, and intend to bridge this gap by utilizing recent advances in time series analytics and machine learning. We build upon the Process-oriented Event Model, which provides a formal approach to model real-world objects and events, and specifies the process of moving from sensors to events. We extend this model to facilitate Semantic Computing and time series data mining directly over the sensor data, which provides the advantage of automatically learning the required background knowledge without domain expertise. We illustrate the expressive power of our model in case studies from diverse applications, with particular emphasis on non-intrusive load monitoring in smart energy grids. We also demonstrate that this powerful semantic representation is still highly accurate and performs at par with existing approaches for event detection and classification.
引用
收藏
页码:461 / 501
页数:41
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