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

A general framework for mining spati...

Yang, Hui.

Linked to FindBook

Google Book

Amazon

博客來

A general framework for mining spatial and spatio-temporal object association patterns in scientific data.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	A general framework for mining spatial and spatio-temporal object association patterns in scientific data./
Author:	Yang, Hui.
Description:	172 p.
Notes:	Adviser: Srinivasan Parthasarathy.
Contained By:	Dissertation Abstracts International67-07B.
Subject:	Computer Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3226466
ISBN:	9780542782299

A general framework for mining spatial and spatio-temporal object association patterns in scientific data.
Yang, Hui.

A general framework for mining spatial and spatio-temporal object association patterns in scientific data. - 172 p.

Adviser: Srinivasan Parthasarathy.

Thesis (Ph.D.)--The Ohio State University, 2006.

Advances in computational sciences and data collection techniques have resulted in the accumulation of huge amounts of spatial or spatio-temporal data in a wide range of scientific disciplines, such as bioinformatics, astrophysics, meteorology, and computational fluid dynamics. As a result, data mining---the process of discovering hidden and useful information in datasets---has been employed to facilitate the understanding of important phenomena in such disciplines. Many approaches have been proposed to analyze spatial or spatio-temporal data. However, they often suffer from several major limitations. First, they often model spatial entities as points. However, this leads to a loss in information since the geometric properties of such entities (or features) can play an important role in many scientific applications. Second, they lack effective schemes to model the diversity of spatial or spatio-temporal relationships among features. Modeling such relationships are key to understanding the evolutionary behavior of features in many scientific domains. Finally, they are often not cognizant of domain knowledge when modeling these relationships. This can limit the usefulness of the data mining process and inhibit our ability to effectively reason about important scientific phenomena.

ISBN: 9780542782299Subjects--Topical Terms:

626642
Computer Science.

A general framework for mining spatial and spatio-temporal object association patterns in scientific data.
LDR:03474nam 2200277 a 45 001 948524
005 20110524
008 110524s2006 ||||||||||||||||| ||eng d
020 $a 9780542782299
035 $a (UMI)AAI3226466
035 $a AAI3226466
040 $a UMI $c UMI
100 1 $a Yang, Hui. $3 1271979
245 1 2 $a A general framework for mining spatial and spatio-temporal object association patterns in scientific data.
300 $a 172 p.
500 $a Adviser: Srinivasan Parthasarathy.
500 $a Source: Dissertation Abstracts International, Volume: 67-07, Section: B, page: 3923.
502 $a Thesis (Ph.D.)--The Ohio State University, 2006.
520 $a Advances in computational sciences and data collection techniques have resulted in the accumulation of huge amounts of spatial or spatio-temporal data in a wide range of scientific disciplines, such as bioinformatics, astrophysics, meteorology, and computational fluid dynamics. As a result, data mining---the process of discovering hidden and useful information in datasets---has been employed to facilitate the understanding of important phenomena in such disciplines. Many approaches have been proposed to analyze spatial or spatio-temporal data. However, they often suffer from several major limitations. First, they often model spatial entities as points. However, this leads to a loss in information since the geometric properties of such entities (or features) can play an important role in many scientific applications. Second, they lack effective schemes to model the diversity of spatial or spatio-temporal relationships among features. Modeling such relationships are key to understanding the evolutionary behavior of features in many scientific domains. Finally, they are often not cognizant of domain knowledge when modeling these relationships. This can limit the usefulness of the data mining process and inhibit our ability to effectively reason about important scientific phenomena.
520 $a In this dissertation, we present a general and modularized framework to address these limitations when mining spatial or spatio-temporal scientific data. We propose different representation schemes to model the geometric properties of spatial entities. We define Spatial Object Association Patterns (SOAPs) to characterize a variety of relationships among entities. Furthermore, we introduce SOAP episodes to capture the evolutionary nature of such relationships. In addition, we propose multiple reasoning strategies to infer important events based on SOAPs or SOAP episodes. We empirically demonstrate the efficacy of this framework on applications originating from the following scientific disciplines: bioinformatics, computational molecular dynamics, and computational fluid dynamics. Our results show that the framework can discover meaningful and important spatial or spatio-temporal patterns. We also demonstrate that the proposed reasoning strategies can make meaningful inferences on important phenomena in such scientific disciplines. Finally, through such applications, we have empirically shown the potential of employing the proposed framework to realize automated or semi-automated data analysis in different scientific disciplines.
590 $a School code: 0168.
650 4 $a Computer Science. $3 626642
690 $a 0984
710 2 $a The Ohio State University. $3 718944
773 0 $t Dissertation Abstracts International $g 67-07B.
790 $a 0168
790 1 0 $a Parthasarathy, Srinivasan, $e advisor
791 $a Ph.D.
792 $a 2006
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3226466