Decision theoretic fusion framework for actionability using data mining on an embedded system

Heungkyu Lee, Sunmee Kang, Hanseok Ko

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper proposes a decision theoretic fusion framework for actionability using data mining techniques in an embedded car navigation system. An embedded system having limited resources is not easy to manage the abundant information in the database. Thus, the proposed system stores and manages only multiple level-of-abstraction in the database to resolve the problem of resource limitations, and then represents the information received from the Web via the wireless network after connecting a communication channel with the data mining server. To do this, we propose a decision theoretic fusion framework that includes the multiple level-of-abstraction approach combining multiple-level association rules and the summary table, as well as an active interaction rule generation algorithm for actionability in an embedded car navigation system. In addition, it includes the sensory and data fusion level rule extraction algorithm to cope with simultaneous events occurring from multimodal interface. The proposed framework can make interactive data mining flexible, effective, and instantaneous in extracting the proper action item.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages90-104
Number of pages15
Volume3755 LNAI
Publication statusPublished - 2006 Dec 1

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3755 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Fingerprint

Data Mining
Embedded systems
Embedded Systems
Data mining
Fusion
Navigation System
Navigation systems
Railroad cars
Databases
Proper Action
Multimodal Interfaces
Rule Generation
Rule Extraction
Resources
Association rules
Data Fusion
Data fusion
Association Rules
Communication Channels
Instantaneous

Keywords

  • Data mining
  • Embedded data mining
  • Speech interactive approach

ASJC Scopus subject areas

  • Computer Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Theoretical Computer Science

Cite this

Lee, H., Kang, S., & Ko, H. (2006). Decision theoretic fusion framework for actionability using data mining on an embedded system. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3755 LNAI, pp. 90-104). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3755 LNAI).

Decision theoretic fusion framework for actionability using data mining on an embedded system. / Lee, Heungkyu; Kang, Sunmee; Ko, Hanseok.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3755 LNAI 2006. p. 90-104 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3755 LNAI).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Lee, H, Kang, S & Ko, H 2006, Decision theoretic fusion framework for actionability using data mining on an embedded system. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 3755 LNAI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3755 LNAI, pp. 90-104.
Lee H, Kang S, Ko H. Decision theoretic fusion framework for actionability using data mining on an embedded system. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3755 LNAI. 2006. p. 90-104. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
Lee, Heungkyu ; Kang, Sunmee ; Ko, Hanseok. / Decision theoretic fusion framework for actionability using data mining on an embedded system. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3755 LNAI 2006. pp. 90-104 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{c552fa23add24ab58e41f1243bd38f0e,
title = "Decision theoretic fusion framework for actionability using data mining on an embedded system",
abstract = "This paper proposes a decision theoretic fusion framework for actionability using data mining techniques in an embedded car navigation system. An embedded system having limited resources is not easy to manage the abundant information in the database. Thus, the proposed system stores and manages only multiple level-of-abstraction in the database to resolve the problem of resource limitations, and then represents the information received from the Web via the wireless network after connecting a communication channel with the data mining server. To do this, we propose a decision theoretic fusion framework that includes the multiple level-of-abstraction approach combining multiple-level association rules and the summary table, as well as an active interaction rule generation algorithm for actionability in an embedded car navigation system. In addition, it includes the sensory and data fusion level rule extraction algorithm to cope with simultaneous events occurring from multimodal interface. The proposed framework can make interactive data mining flexible, effective, and instantaneous in extracting the proper action item.",
keywords = "Data mining, Embedded data mining, Speech interactive approach",
author = "Heungkyu Lee and Sunmee Kang and Hanseok Ko",
year = "2006",
month = "12",
day = "1",
language = "English",
isbn = "3540325476",
volume = "3755 LNAI",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "90--104",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

}

TY - GEN

T1 - Decision theoretic fusion framework for actionability using data mining on an embedded system

AU - Lee, Heungkyu

AU - Kang, Sunmee

AU - Ko, Hanseok

PY - 2006/12/1

Y1 - 2006/12/1

N2 - This paper proposes a decision theoretic fusion framework for actionability using data mining techniques in an embedded car navigation system. An embedded system having limited resources is not easy to manage the abundant information in the database. Thus, the proposed system stores and manages only multiple level-of-abstraction in the database to resolve the problem of resource limitations, and then represents the information received from the Web via the wireless network after connecting a communication channel with the data mining server. To do this, we propose a decision theoretic fusion framework that includes the multiple level-of-abstraction approach combining multiple-level association rules and the summary table, as well as an active interaction rule generation algorithm for actionability in an embedded car navigation system. In addition, it includes the sensory and data fusion level rule extraction algorithm to cope with simultaneous events occurring from multimodal interface. The proposed framework can make interactive data mining flexible, effective, and instantaneous in extracting the proper action item.

AB - This paper proposes a decision theoretic fusion framework for actionability using data mining techniques in an embedded car navigation system. An embedded system having limited resources is not easy to manage the abundant information in the database. Thus, the proposed system stores and manages only multiple level-of-abstraction in the database to resolve the problem of resource limitations, and then represents the information received from the Web via the wireless network after connecting a communication channel with the data mining server. To do this, we propose a decision theoretic fusion framework that includes the multiple level-of-abstraction approach combining multiple-level association rules and the summary table, as well as an active interaction rule generation algorithm for actionability in an embedded car navigation system. In addition, it includes the sensory and data fusion level rule extraction algorithm to cope with simultaneous events occurring from multimodal interface. The proposed framework can make interactive data mining flexible, effective, and instantaneous in extracting the proper action item.

KW - Data mining

KW - Embedded data mining

KW - Speech interactive approach

UR - http://www.scopus.com/inward/record.url?scp=37149016313&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=37149016313&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:37149016313

SN - 3540325476

SN - 9783540325475

VL - 3755 LNAI

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 90

EP - 104

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ER -