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URN etd-0217109-094315 Statistics This thesis had been viewed 3342 times. Download 1614 times. Author Kuo-Wei Ho Author's Email Address No Public. Department Computer Science and Enginerring Year 2008 Semester 1 Degree Ph.D. Type of Document Doctoral Dissertation Language English Page Count 81 Title Data Cube Implementation and Maintenance via Subcubes Keyword subcube materialization data cube on-line analytical processing data warehouse business intelligence business intelligence data warehouse on-line analytical processing data cube materialization subcube Abstract Business Intelligence (BI) systems are repidly becoming a key to gaining competitive advantage for businesses in recent years. Many corporations are building data warehouses from operational databases for business intelligence systems. Users of data warehouses typically carry out on-line analytical processing (OLAP) to provide a data cube which allows a better understanding of data for analysis and provide better performance for queries. Besides, computing multidimensional aggregates and selecting part of them for materialization is the bottleneck for these applications.
OLAP queries are complex and time-consuming and hence materializing data cube is a commonly used technique to reduce response time. To our knowledge, most previous OLAP cube implementation techniques apply a static view selection algorithm on the search lattice. These static methods first treat each node in the lattice as an undividable unit and then pick some of them for materialization. We propose to further partition nodes in the lattice into subcubes into each of which multiple OLAP queries via a dynamic materialization algorithm can be mapped. Experiments show that the locality effects do exist in OLAP queries, and our dynamic method keeps a reasonable performance even though the available space is very limited and is practical for OLAP query processing.
The experiments demonstrate that the the new partition we proposed is insensitive to the density change and the storage space utilization is more efficient than previoous methods. Meanwhile, the prospects of future research directions and topics are also given in the conclusion chapter.
Advisor Committee Huei-Huang Chen - advisor
Files Date of Defense 2009-01-21 Date of Submission 2009-02-17