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Modeling and Recognition of Events From Temporal Sensor Data for Energy Applications.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Modeling and Recognition of Events From Temporal Sensor Data for Energy Applications./
Author:	Patri, Om Prasad.
Description:	1 online resource (216 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 79-06(E), Section: A.
Contained By:	Dissertation Abstracts International79-06A(E).
Subject:	Computer science. -
Online resource:	click for full text (PQDT)

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. - 1 online resource (216 pages)

Source: Dissertation Abstracts International, Volume: 79-06(E), Section: A.

Thesis (Ph.D.)--University of Southern California, 2017.

Includes bibliographical references

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).

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Modeling and Recognition of Events From Temporal Sensor Data for Energy Applications.
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245 1 0 $a Modeling and Recognition of Events From Temporal Sensor Data for Energy Applications.
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300 $a 1 online resource (216 pages)
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500 $a Source: Dissertation Abstracts International, Volume: 79-06(E), Section: A.
500 $a Adviser: Viktor K. Prasanna.
502 $a Thesis (Ph.D.)--University of Southern California, 2017.
504 $a Includes bibliographical references
520 $a 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).
520 $a 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.
520 $a 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.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of Southern California. $3 1182588
773 0 $t Dissertation Abstracts International $g 79-06A(E).
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