Decision Theoretic Fusion Framework for Actionability Using Data Mining on an Embedded System - Data Mining: Theory, Methodology, Techniques, and Applications - page 90

Decision Theoretic Fusion Framework for Actionability
Using Data Mining on an Embedded System
Heungkyu Lee 1 , Sunmee Kang 2 , and Hanseok Ko 3
1 Dept. of Visual Information Processing, Korea University, Seoul, Korea
2 Dept. of Computer Science, Seokyeong University
3 Dept. of Electronics and Computer Engineering, Korea University, Seoul, Korea
[e-mail], [e-mail],
[e-mail]
Abstract. This paper proposes a decision theoretic fusion framework for ac-
tionability using data mining techniques in an embedded car navigation system.
An embedded system having limited resources is not easy to manage the abun-
dant information in the database. Thus, the proposed system stores and man-
ages 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 navi-
gation system. In addition, it includes the sensory and data fusion level rule
extraction algorithm to cope with simultaneous events occurring from multi-
modal 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, and Speech interactive
approach.
1 Introduction
As detailed and accurate data are accumulated and stored in databases at various
stages, the large amounts of data in databases makes it almost impractical to manually
analyze them for valuable information. Thus, the need for automated analysis and
discovery tools to extract useful knowledge from huge amounts of raw data has been
urgent. To cope with this problem, data mining methodologies are emerging as effi-
cient tools in realizing the above objectives. Data mining [1][15][11] is the process of
extracting previously unknown information in the form of patterns, trends, and struc-
tures from large quantities of data. These methodologies are being used in many
fields, such as financial, business, medical, manufacturing and production, scientific
domains, and the World Wide Web (WWW). Especially, autonomous decision-
making process using a data mining approach has been useful in various fields for
sourcing efficient and reliable information [3][20].