Data Mining is used to discover patterns, clusters and models from data which are then rendered in operational forms that are easy for people to visualize and understand, and also to automate knowledge discovery in databases. Data Warehousing is a continual process which enables a corporation to assemble operational and other data from a variety of internal and external sources, and transform that data into consistent, high-quality, business information, distribute that information to the points of maximum value within the organizations, and provide easy, flexible and fast access for busy non-technical users. A client/server database system increases processing power by separating the database management system from the application; the client as the front-end system handling the user interface and the server as the back-end system accessing the database, which cooperate to run an application.
The Program Committee have selected 21 papers to be presented in the Academic Program at the Workshop. The papers cover all three themes and address both theoretical issues and practical applications. A special track devoted to industrial applications has been included in the Workshop to reflect the strong interest from representatives of commerce and industry.
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Keanote Paper: Building virtual warehouses using workflows and the Web (K. Ramamritham & M. Kamath); Some Accepted Papers: PAPERS: Differentiation for schema discovery (T. Miura & I. Shioya); A hybrid knowledge-based approach to discover dynamic semantics from relational schema to object-oriented schema (I. Kwan); Avoiding re-computation: view adaptation in data warehouses (M. Mohania); Client-based transactions across database boundaries (W.H. Yu & H.O. Rafaelsen); Three tier client/server database design: a practical approach to incorporate GUI inot existing DBMS (J. Fong & R. Hui).
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