On Multiple Query Optimization in Data Mining

Wojciechowski, Marek; Zakrzewicz, Maciej

doi:10.1007/11430919_80

Cited by 6 publications

(4 citation statements)

References 9 publications

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“…Multiple-query processing was first studied in late 1980s [32,33], and it still is an active research area [1,14,35,36,38]. But existing works do not consider multiple top-N queries.…”

Section: Related Workmentioning

confidence: 99%

Region clustering based evaluation of multiple top-N selection queries

Zhu

Meng

Yang

et al. 2008

Data & Knowledge Engineering

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In many database applications, there are opportunities for multiple top-N queries to be evaluated at the same time. Often it is more cost effective to evaluate multiple such queries collectively than individually.In this paper, we propose a new method for evaluating multiple top-N queries concurrently over a relational database. The basic idea of this method is region clustering that groups the search regions of individual top-N queries into larger regions and retrieves the tuples from the larger regions. This method avoids having the same region accessed multiple times and reduces the number of random I/O accesses to the underlying databases. Extensive experiments are carried out to measure the performance of this new strategy and the results indicate that it is significantly better than the naïve method of evaluating these queries one by one for both low-dimensional (2, 3, and 4) and high-dimensional (25, 50, and 104) data.

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“…Multiple-query processing was first studied in late 1980s [32,33], and it still is an active research area [1,14,35,36,38]. But existing works do not consider multiple top-N queries.…”

Section: Related Workmentioning

confidence: 99%

Region clustering based evaluation of multiple top-N selection queries

Zhu

Meng

Yang

et al. 2008

Data & Knowledge Engineering

View full text Add to dashboard Cite

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“…To the best of our knowledge, the only two multiple-query processing methods for data mining queries are Mine Merge [19] and Common Counting [20], mentioned above. Recently, the need for multiple-query optimization has been postulated in the somewhat related research area of inductive logic programming, where a technique based on similar ideas as Common Counting has been proposed, consisting in combining similar queries into query packs [6].…”

Section: Related Workmentioning

confidence: 99%

“…Recently, a new problem of optimizing processing of batches of frequent itemset queries has been considered [19] [20]. The problem was motivated by data mining systems working in a batch mode or periodically refreshed data warehouses, but is also relevant in the context of multi-user, interactive data mining environments.…”

Section: Introductionmentioning

confidence: 99%

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Partition-Based Approach to Processing Batches of Frequent Itemset Queries

Grudziński

Wojciechowski

Zakrzewicz

2006

Flexible Query Answering Systems

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We consider the problem of optimizing processing of batches of frequent itemset queries. The problem is a particular case of multiple-query optimization, where the goal is to minimize the total execution time of the set of queries. We propose an algorithm that is a combination of the Mine Merge method, previously proposed for processing of batches of frequent itemset queries, and the Partition algorithm for memory-based frequent itemset mining. The experiments show that the novel approach outperforms the original Mine Merge and sequential processing in majority of cases.

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Machine Learning for Analyzing Human Brain Function

Mitchell

2005

Advances in Knowledge Discovery and Data Mining

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On Multiple Query Optimization in Data Mining

Cited by 6 publications

References 9 publications

Region clustering based evaluation of multiple top-N selection queries

Region clustering based evaluation of multiple top-N selection queries

Partition-Based Approach to Processing Batches of Frequent Itemset Queries

Machine Learning for Analyzing Human Brain Function

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