Optimizing multiple dimensional queries simultaneously in multidimensional databases

Liang, Weifa; Orłowska, Maria E.; Yu, Jeffrey Xu

doi:10.1007/s007780050011

Cited by 6 publications

(8 citation statements)

References 19 publications

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“…It is important to point out that in Liang and Orlowska [2000], Zhao et al [1998], and Kalnis and Papadias [2001], the content of the dimensions is not considered by the optimizer as it is thought of as part of the database. But our computation of query expression reduction makes explicit use of the dimensional structures.…”

Section: Some Related Query Optimization Techniquesmentioning

confidence: 99%

“…Multi-OLAP query optimization has been considered by Liang and Orlowska [2000], Zhao et al [1998], and Kalnis and Papadias [2001]. The underlying model for an OLAP cube is relational, and they considered the very-low-level costs of physical data access such as input/output cost, table join, and table scan cost.…”

Section: Some Related Query Optimization Techniquesmentioning

confidence: 99%

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Concise descriptions of subsets of structured sets

Mendelzon

2005

ACM Trans. Database Syst.

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We study the problem of economical representation of subsets of structured sets, which are sets equipped with a set cover or a family of preorders. Given a structured set U , and a language L whose expressions define subsets of U , the problem of minimum description length in L (L-MDL) is: "given a subset V of U , find a shortest string in L that defines V ." Depending on the structure and the language, the MDL-problem is in general intractable. We study the complexity of the MDL-problem for various structures and show that certain specializations are tractable. The families of focus are hierarchy, linear order, and their multidimensional extensions; these are found in the context of statistical and OLAP databases. In the case of general OLAP databases, data organization is a mixture of multidimensionality, hierarchy, and ordering, which can also be viewed naturally as a cover-structured ordered set. Efficient algorithms are provided for the MDLproblem for hierarchical and linearly ordered structures, and we prove that the multidimensional extensions are NP-complete. Finally, we illustrate the application of the theory to summarization of large result sets and (multi) query optimization for ROLAP queries.

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Section: Some Related Query Optimization Techniquesmentioning

confidence: 99%

Section: Some Related Query Optimization Techniquesmentioning

confidence: 99%

Concise descriptions of subsets of structured sets

Mendelzon

2005

ACM Trans. Database Syst.

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“…The rest of the paper is organized as follows: In section 2 we introduce some basic concepts and we review the work of [ZDNS98] and [LOY00]. In section 3 we identify the drawbacks of the current approaches and in section 4 we describe our methods.…”

Section: Introductionmentioning

confidence: 99%

“…which is likely to be NP-hard. [LOY00] provide an exhaustive algorithm which runs in exponential time. Since the algorithm is impractical for real life applications, they also describe an approximation algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Optimization Algorithms for Simultaneous Multidimensional Queries in OLAP Environments

Kalnis

Papadias

2001

Lecture Notes in Computer Science

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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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Single-scan: a fast star-join query processing algorithm

PURDILĂ

Pentiuc

2015

Softw. Pract. Exper.

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Summary A data warehouse can store very large amounts of data that should be processed in parallel in order to achieve reasonable query execution times. The MapReduce programming model is a very convenient way to process large amounts of data in parallel on commodity hardware clusters. A very popular query used in data warehouses is star‐join. In this paper, we present a fast and efficient star‐join query execution algorithm built on top of a MapReduce framework called Hadoop. By using dynamic filters against dimension tables, the algorithm needs a single scan of the fact table, which means a significant reduction of input/output operations and computational complexity. Also, the algorithm requires only two MapReduce iterations in total–one to build the filters against dimension tables and one to scan the fact table. Our experiments show that the proposed algorithm performs much better than the existing solutions in terms of execution time and input/output. Copyright © 2014 John Wiley & Sons, Ltd.

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Optimizing multiple dimensional queries simultaneously in multidimensional databases

Cited by 6 publications

References 19 publications

Concise descriptions of subsets of structured sets

Concise descriptions of subsets of structured sets

Optimization Algorithms for Simultaneous Multidimensional Queries in OLAP Environments

Single-scan: a fast star-join query processing algorithm

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