東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Modeling and Recognition of Events F...

Patri, Om Prasad.

Linked to FindBook

Google Book

Amazon

博客來

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

Record Type:	Electronic resources : Monograph/item
Title/Author:	Modeling and Recognition of Events From Temporal Sensor Data for Energy Applications./
Author:	Patri, Om Prasad.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
Description:	216 p.
Notes:	Source: Dissertation Abstracts International, Volume: 79-06(E), Section: A.
Contained By:	Dissertation Abstracts International79-06A(E).
Subject:	Computer science. -
Online resource:	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.
LDR:02997nmm a2200301 4500 001 2162616
005 20181005115848.5
008 190424s2017 ||||||||||||||||| ||eng d
035 $a (MiAaPQ)AAI10801510
035 $a (MiAaPQ)US_Calif_362277
035 $a AAI10801510
040 $a MiAaPQ $c MiAaPQ
100 1 $a Patri, Om Prasad. $3 3350612
245 1 0 $a Modeling and Recognition of Events From Temporal Sensor Data for Energy Applications.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2017
300 $a 216 p.
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.
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.
590 $a School code: 0208.
650 4 $a Computer science. $3 523869
690 $a 0984
710 2 $a University of Southern California. $3 700129
773 0 $t Dissertation Abstracts International $g 79-06A(E).
790 $a 0208
791 $a Ph.D.
792 $a 2017
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10801510