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ABSTRACT: Commerical databases often contain critical business information concerning past performance which could be used to predict
the future. However, the huge amounts of data can make the extraction of this business information almost impossible by manual
methods or standard software techniques. Data mining techniques can analyze, understand and visualize the huge amounts ofstored
data gathered from business applications and thus help companies sta stored data gathered from business applications and thus
help companies stay competitive in today’s marketplace. Currently, a number of data mining applications and prototypes have
been developed for a variety of business domains. Most of these applications are targeted at predictive modeling that finds
pattern of data to help predict the future trend and behaviors of some entities. Apart from predictive modeling, other data
mining tasks such as summarization, association, classification and clustering could also be applied to business databases.
In this paper, we will illustrate the different data mining tasks applied to a real-life business database for risk analysis
and targeted marketing.
11/2006: pages 158-169;
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ABSTRACT: As companies seek to automate more of their processes, they are
finding that decision support requires a significantly different data
management approach than day-to-day operations. Online transaction
processing applications simply automate data processing, which is
sufficient to handle day-to-day operations. The paper considers a
seven-step process which combines online analytical processing, data
cube analysis, and data mining to streamline decision making in
companies with multidimensional databases
IT Professional 04/2002;
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Information & Management. 01/2000; 38:1-13.
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Proceedings of the Third ICSC Symposia on Intelligent Industrial Automation (IIA'99) and Soft Computing (SOCO'99), June 1-4, 1999, Genova, Italy; 01/1999
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PRICAI'98, Topics in Artificial Intelligence, 5th Pacific Rim International Conference on Artificial Intelligence, Singapore, November 22-27, 1998, Proceedings; 01/1998
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Health Physics 07/1979; 36(6):738-40. · 1.68 Impact Factor
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Health Physics 12/1975; 29(5):782-5. · 1.68 Impact Factor
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Health Phys., v. 29, no. 5, pp. 782-785. 10/1975; 29(5).
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ABSTRACT: In traditional customer service support of a manufacturing environment, a customer service database usually stores two types of service information: (1) unstructured customer service reports record machine problems and its remedial actions and (2) structured data on sales, employees, and customers for day-to-day management operations. This paper investigates how to apply data mining techniques to extract knowledge from the database to support two kinds of customer service activities: decision support and machine fault diagnosis. A data mining process, based on the data mining tool DBMiner, was investigated to provide structured management data for decision support. In addition, a data mining technique that integrates neural network, case-based reasoning, and rule-based reasoning is proposed; it would search the unstructured customer service records for machine fault diagnosis. The proposed technique has been implemented to support intelligent fault diagnosis over the World Wide Web.
Information & Management.
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ABSTRACT: In traditional help desk service centres, service engineers provide a world-wide customer support service through the use of long-distance telephone calls. Such a mode of support is found to be inefficient, ineffective and generally results in high costs, long service cycles, and poor quality of service. With the advent of the Internet technology, it is possible to deliver customer service support over the World Wide Web. This paper describes a Web-based intelligent fault diagnosis system, known as WebService, to support customer service over the Web. In the WebService system, a hybrid case-based reasoning (CBR) and artificial neural network (ANN) approach is adopted as the intelligent technique for machine fault diagnosis. Instead of using traditional CBR technique for indexing, retrieval and adaptation, the hybrid CBR–ANN approach integrates ANN with the CBR cycle to extract knowledge from service records of the customer service database and subsequently recall the appropriate service records using this knowledge during the retrieval phase.
Engineering Applications of Artificial Intelligence.
