ACM Transactions on Database Systems (TODS)
Faster scaling algorithms for general graph matching problems
Journal of the ACM (JACM)
The Stanford GraphBase: a platform for combinatorial computing
The Stanford GraphBase: a platform for combinatorial computing
Improvements on a heuristic algorithm for multiple-query optimization
Data & Knowledge Engineering
View maintenance in a warehousing environment
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Implementing data cubes efficiently
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Materialized view maintenance and integrity constraint checking: trading space for time
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
A framework for supporting data integration using the materialized and virtual approaches
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Improved query performance with variant indexes
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
An array-based algorithm for simultaneous multidimensional aggregates
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Efficient view maintenance at data warehouses
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Simultaneous optimization and evaluation of multiple dimensional queries
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Data warehousing features in Informix OnLine XPS
DIS '96 Proceedings of the fourth international conference on on Parallel and distributed information systems
Complex Aggregation at Multiple Granularities
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
Using Common Subexpressions to Optimize Multiple Queries
Proceedings of the Fourth International Conference on Data Engineering
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Selection of Views to Materialize in a Data Warehouse
ICDT '97 Proceedings of the 6th International Conference on Database Theory
Fast Computation of Sparse Datacubes
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Materialized Views Selection in a Multidimensional Database
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
On the Computation of Multidimensional Aggregates
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Algorithms for Materialized View Design in Data Warehousing Environment
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Optimization Algorithms for Simultaneous Multidimensional Queries in OLAP Environments
DaWaK '01 Proceedings of the Third International Conference on Data Warehousing and Knowledge Discovery
Efficient Constraint-Based Exploratory Mining on Large Data Cubes
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Multi-query optimization for on-line analytical processing
Information Systems
Concise descriptions of subsets of structured sets
ACM Transactions on Database Systems (TODS) - Special Issue: SIGMOD/PODS 2003
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Some significant progress related to multidimensional data analysis has been achieved in the past few years, including the design of fast algorithms for computing datacubes, selecting some precomputed group-bys to materialize, and designing efficient storage structures for multidimensional data. However, little work has been carried out on multidimensional query optimization issues. Particularly the response time (or evaluation cost) for answering several related dimensional queries simultaneously is crucial to the OLAP applications. Recently, Zhao et al. first exploited this problem by presenting three heuristic algorithms. In this paper we first consider in detail two cases of the problem in which all the queries are either hash-based star joins or index-based star joins only. In the case of the hash-based star join, we devise a polynomial approximation algorithm which delivers a plan whose evaluation cost is $ O(n^{\epsilon }$) times the optimal, where n is the number of queries and $ \epsilon $ is a fixed constant with $0n times the optimal, and an exponential algorithm which delivers a plan with the optimal evaluation cost. We then consider a general case in which both hash-based star-join and index-based star-join queries are included. For this case, we give a possible improvement on the work of Zhao et al., based on an analysis of their solutions. We also develop another heuristic and an exact algorithm for the problem. We finally conduct a performance study by implementing our algorithms. The experimental results demonstrate that the solutions delivered for the restricted cases are always within two times of the optimal, which confirms our theoretical upper bounds. Actually these experiments produce much better results than our theoretical estimates. To the best of our knowledge, this is the only development of polynomial algorithms for the first two cases which are able to deliver plans with deterministic performance guarantees in terms of the qualities of the plans generated. The previous approaches including that of [ZDNS98] may generate a feasible plan for the problem in these two cases, but they do not provide any performance guarantee, i.e., the plans generated by their algorithms can be arbitrarily far from the optimal one.