Optimization Algorithms for Simultaneous Multidimensional Queries in OLAP Environments
Panos Kalnis,Dimitris Papadias +1 more
- 05 Sep 2001
- pp 264-273
TL;DR: This paper developed two novel greedy algorithms that construct the execution plan in a top-down manner by identifying in each step the most beneficial view, instead of finding the most promising query.
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Abstract: Multi-Dimensional Expressions (MDX) provide an interface for asking several related OLAP queries simultaneously. An interesting problem is how to optimize the execution of an MDX query, given that most data warehouses maintain a set of redundant materialized views to accelerate OLAP operations. A number of greedy and approximation algorithms have been proposed for different versions of the problem. In this paper we evaluate experimentally their performance using the APB and TPC-H benchmarks, concluding that they do not scale well for realistic workloads. Motivated by this fact, we developed two novel greedy algorithms. Our algorithms construct the execution plan in a top-down manner by identifying in each step the most beneficial view, instead of finding the most promising query. We show by extensive experimentation that our methods outperform the existing ones in most cases.
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References
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Patrick O'Neil,Dallan Quass +1 more
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Efficient and Extensible Algorithms for Multi Query Optimization
TL;DR: This paper proposes three cost-based heuristic algorithms: Volcano-SH and Volcano-RU, which are based on simple modifications to the Volcano search strategy, and a greedy heuristic that incorporates novel optimizations that improve efficiency greatly.
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