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We describe the design and implementation of a new data layout scheme, called multi-dimensional clustering, in DB2 Universal Database Version 8. Many applications, e.g., OLAP and data warehousing, process a table or tables in a database using a multi-dimensional access paradigm. Currently, most database systems can only support organization of a table using a primary clustering index. Secondary indexes are cre- ated to access the tables when the primary key index is not applicable. Unfortunately, secondary indexes perform many random I/O accesses against the table for a simple opera- tion such as a range query. Our work in multi-dimensional clustering addresses this important de fciencyin database systems. Multi-Dimensional Clustering is based on the def- inition of one or more orthogonal clustering attributes (or expressions) of a table. The table is organized physically by associating records with similar values for the dimension attributes in a cluster. We describe novel techniques for maintaining this physical layout e ciently and methods of processing database operations that provide signi fcan per- formance improvements. We show results from experiments using a star-schema database to validate our claims of per- formance with minimal overhead.
@inproceedings {DBLP:conf/sigmod/PadmanabhanBMCH03, author = {Sriram Padmanabhan and Bishwaranjan Bhattacharjee and Timothy Malkemus and Leslie Cranston and Matthew Huras}, booktitle = {SIGMOD Conference}, title = {Multi-Dimensional Clustering: A New Data Layout Scheme in DB2.}, pages = {637-641}, year = {2003}, url = {db/conf/sigmod/sigmod2003.html#PadmanabhanBMCH03}, ee = {http://www.acm.org/sigmod/sigmod03/eproceedings/papers/ind01.pdf}, crossref = {conf/sigmod/2003}, bibsource = {DBLP, http://dblp.uni-trier.de} }  ©2004 Association for Computing Machinery |
