A Study of an Efficient Indexing Technology for Knowledge Systems

博士 === 國立交通大學 === 資訊科學系所 === 93 === Recently, the Knowledge Discovery in Database (KDD) has grown rapidly, as IT and AI technologies have become widely discussed and researched. Relevant research, applications, and tool development in business, science, government, and academia are becoming increasi...

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Bibliographic Details
Main Authors: Wei-Chou Chen, 陳威州
Other Authors: Shian-Shyong Tseng
Format: Others
Language:en_US
Published: 2005
Online Access:http://ndltd.ncl.edu.tw/handle/82432045059717254979
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Summary:博士 === 國立交通大學 === 資訊科學系所 === 93 === Recently, the Knowledge Discovery in Database (KDD) has grown rapidly, as IT and AI technologies have become widely discussed and researched. Relevant research, applications, and tool development in business, science, government, and academia are becoming increasingly popular. Particularly in some worldwide enterprises, KDD systems are applied to discover useful business intelligence and customer behavior patterns using data mining technology. However, since the quantity of data is continuously and rapidly growing in such enterprises, correctly and efficiently discovering useful information is becoming a significant issue. In this thesis, we will propose an efficient indexing technology of knowledge and database systems, called Bit-wise Indexing Technology. There are three indexing models in this technology, including Simple Bit-wise Indexing Method, Encapsulated Bit-wise Indexing Method and Compacted Bit-wise Indexing Method. Also, the corresponding indexing and matching algorithms for such indexing models are also proposed. In order to demonstrate the suitability, flexibility and efficiency of the proposed indexing methods, we will try to apply the proposed method in four kinds of KDD applications, including reinforcement learning, pattern matching, supervised learning and unsupervised-learning data mining applications, in this thesis. For enhancing the system performance, the simple bit-wise indexing method was applied to the manufacturing defect detection problem, time aspect (MDDP-t) for manufacturing domains. For improving the flexibility and accuracy, the encapsulated bit-wise indexing method is applied to the pattern matching module of an Internet intrusion detection system. To reduce the processing time, the compacted bit-wise indexing method is applied to the data-driven rough-set based feature selection. Additionally, the proposed feature selection method was adopted in a KA project to discover the desired feature sets to construct a CBR system for a world-wide financial group customer relationship management system’s loan promotion function. In the last application, three proposed methods are hybridly applied to the data mining module of a defect detection mechanism in a semiconductor manufacturing system to improving the accuracy and usability. The proposed method was officially employed in the Yield Explorer Function of Intelligent Engineering Data Analysis system (iEDA) in Taiwan Semiconductor Manufacturing Corporation (TSMC) for root cause detection of manufacturing defects and yield enhancement.