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Modeling and Recognition of Events F...
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Patri, Om Prasad.
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Modeling and Recognition of Events From Temporal Sensor Data for Energy Applications.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Modeling and Recognition of Events From Temporal Sensor Data for Energy Applications./
作者:
Patri, Om Prasad.
出版者:
Ann Arbor : ProQuest Dissertations & Theses, : 2017,
面頁冊數:
216 p.
附註:
Source: Dissertation Abstracts International, Volume: 79-06(E), Section: A.
Contained By:
Dissertation Abstracts International79-06A(E).
標題:
Computer science. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10801510
Modeling and Recognition of Events From Temporal Sensor Data for Energy Applications.
Patri, Om Prasad.
Modeling and Recognition of Events From Temporal Sensor Data for Energy Applications.
- Ann Arbor : ProQuest Dissertations & Theses, 2017 - 216 p.
Source: Dissertation Abstracts International, Volume: 79-06(E), Section: A.
Thesis (Ph.D.)--University of Southern California, 2017.
The ubiquitous nature of sensors and smart devices collecting more and more data from industrial and engineering equipment (such as pumps and compressors in oilfields or smart meters in energy grids) has led to new challenges in faster processing of temporal data to identify critical happenings (events) and respond to them. We deal with two primary challenges in processing events from temporal sensor data: (i) how to comprehensively model events and related happenings (event modeling), and (ii) how to automatically recognize event patterns from raw multi-sensor data (event recognition).Subjects--Topical Terms:
523869
Computer science.
Modeling and Recognition of Events From Temporal Sensor Data for Energy Applications.
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The ubiquitous nature of sensors and smart devices collecting more and more data from industrial and engineering equipment (such as pumps and compressors in oilfields or smart meters in energy grids) has led to new challenges in faster processing of temporal data to identify critical happenings (events) and respond to them. We deal with two primary challenges in processing events from temporal sensor data: (i) how to comprehensively model events and related happenings (event modeling), and (ii) how to automatically recognize event patterns from raw multi-sensor data (event recognition).
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The event modeling problem is to build a comprehensive event model enabling complex event analysis across diverse underlying systems, people, entities, actions and happenings. We propose the Process-oriented Event Model for event processing that attempts a comprehensive representation of these processes, particularly those seen in modern energy industries and sensor data processing applications. This model brings together, in a unified framework, the different types of entities that are expected to be present at different stages of an event processing workflow and a formal specification of relationships between them.
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Using event models in practice requires detailed domain knowledge about a variety of events based on raw data. We propose to learn this domain knowledge automatically by using recent advances in time series classification and shape mining, which provide methods of identifying discriminative patterns or subsequences (called shapelets). These methods show great potential for real sensor data as they don't make assumptions about the nature, source, structure, distribution, or stationarity of input time series, provide visual intuition, and perform fast event classification. By combining shape extraction and feature selection, we extend this temporal shape mining paradigm for processing data from multiple sensors. We present evaluation results to illustrate the performance of our approaches on real-world sensor data.
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