Heuristic optimization of OLAP queries in multidimensionally hierarchically clustered databases
Proceedings of the 4th ACM international workshop on Data warehousing and OLAP
Processing OLAP queries in hierarchically clustered databases
Data & Knowledge Engineering - Special issue: Advances in OLAP
Exploiting hierarchical clustering in evaluating multidimensional aggregation queries
DOLAP '03 Proceedings of the 6th ACM international workshop on Data warehousing and OLAP
Efficient evaluation of partially-dimensional range queries using adaptive r*-tree
DEXA'06 Proceedings of the 17th international conference on Database and Expert Systems Applications
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Most operations of the relational algebra or SQL require a sorted stream of tuples for efficient processing. Therefore, processing complex relational queries relies on efficient access to a table in some sort order. In principle, indexes could be used, but they are superior to a full table scan only, if the result set is sufficiently restricted in the index attribute. In this paper we present the Tetris algorithm, which utilizes restrictions to process a table in sort order of any attribute without the need of external sorting. The algorithm relies on the space partitioning of a multidimensional access method. A sweep line technique is used to read data in sort order of any attribute, while accessing each disk page of a table only once. Results are produced earlier than with traditional sorting techniques, allowing better response times for interactive applications and pipelined processing of the result set. We describe a prototype implementation of the Tetris algorithm using UB-Trees on top of Oracle 8, define a cost model and present performance measurements for some queries of the TPC-D benchmark.