Parallel Processing Techniques for Data Warehousing and Mining
Application and Challenges
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Data Warehousing and Mining is becoming an increasingly important technology for information integration and data analysis. In data warehousing, OLAP queries incur enormous processing overheads due to the huge size of the data warehouses. Parallel processing techniques, using cluster of workstations, have of late emerged as a practical solution to many computational and data intensive problems. Further, the tasks of DM and KDD with an emphasis on multiple objectives are time consuming, considering the large amounts of data that need to be processed. Hence parallel processing techniques can be ...
Data Warehousing and Mining is becoming an increasingly important technology for information integration and data analysis. In data warehousing, OLAP queries incur enormous processing overheads due to the huge size of the data warehouses. Parallel processing techniques, using cluster of workstations, have of late emerged as a practical solution to many computational and data intensive problems. Further, the tasks of DM and KDD with an emphasis on multiple objectives are time consuming, considering the large amounts of data that need to be processed. Hence parallel processing techniques can be viewed as a practical solutions.
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