Materialized Data Mining Views

Morzy, Tadeusz; Wojciechowski, Marek; Zakrzewicz, Maciej

doi:10.1007/3-540-45372-5_7

Cited by 24 publications

(17 citation statements)

References 8 publications

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“…A summary of view maintenance techniques can be found in [6]. Materialized data mining views were first proposed in [10], and quickly became an important tool in data mining query optimization [8,9,14]. To the best of our knowledge the idea of concurrent online refresh of a set of materialized data mining views has not been presented yet.…”

Section: Related Workmentioning

confidence: 99%

Incremental Data Mining Using Concurrent Online Refresh of Materialized Data Mining Views

Morzy

Wojciechowski

et al. 2005

Data Warehousing and Knowledge Discovery

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Abstract. Data mining is an iterative process. Users issue series of similar data mining queries, in each consecutive run slightly modifying either the definition of the mined dataset, or the parameters of the mining algorithm. This model of processing is most suitable for incremental mining algorithms that reuse the results of previous queries when answering a given query. Incremental mining algorithms require the results of previous queries to be available. One way to preserve those results is to use materialized data mining views. Materialized data mining views store the mined patterns and refresh them as the underlying data change. Data mining and knowledge discovery often take place in a data warehouse environment. There can be many relatively small materialized data mining views defined over the data warehouse. Separate refresh of each materialized view can be expensive, if the refresh process has to re-discover patterns in the original database. In this paper we present a novel approach to materialized data mining view refresh process. We show that the concurrent on-line refresh of a set of materialized data mining views is more efficient than the sequential refresh of individual views. We present the framework for the integration of data warehouse refresh process with the maintenance of materialized data mining views. Finally, we prove the feasibility of our approach by conducting several experiments on synthetic data sets.

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Section: Related Workmentioning

confidence: 99%

Incremental Data Mining Using Concurrent Online Refresh of Materialized Data Mining Views

Morzy

Wojciechowski

et al. 2005

Data Warehousing and Knowledge Discovery

Self Cite

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“…In our previous work we introduced the concept of materialized data mining views, providing a general discussion on their possible usage in mining various classes of frequent patterns [8] [9]. In this paper we focus on the most prominent class of patterns -frequent itemsets.…”

Section: Introductionmentioning

confidence: 99%

A Study on Answering a Data Mining Query Using a Materialized View

Zakrzewicz

Morzy

Wojciechowski

2004

Lecture Notes in Computer Science

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Abstract.One of the classic data mining problems is discovery of frequent itemsets. This problem particularly attracts database community as it resembles traditional database querying. In this paper we consider a data mining system which supports storing of previous query results in the form of materialized data mining views. While numerous works have shown that reusing results of previous frequent itemset queries can significantly improve performance of data mining query processing, a thorough study of possible differences between the current query and a materialized view has not been presented yet. In this paper we classify possible differences into six classes, provide I/O cost analysis for all the classes, and experimentally evaluate the most promising ones.

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“…Also, most of these works require that some strong relation holds between the queries like inclusion or equivalence. In [14], a more general problem is addressed. A query consists of the constraints defining the subset of a relational database to be mined and the constraints on the association rules themselves.…”

Section: Introductionmentioning

confidence: 99%

Using Condensed Representations for Interactive Association Rule Mining

Jeudy

Boulicaut

2002

Principles of Data Mining and Knowledge Discovery

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Abstract. Association rule mining is a popular data mining task. It has an interactive and iterative nature, i.e., the user has to refine his mining queries until he is satisfied with the discovered patterns. To support such an interactive process, we propose to optimize sequences of queries by means of a cache that stores information from previous queries. Unlike related works, we use condensed representations like free and closed itemsets for both data mining and caching. This results in a much more efficient mining technique in highly correlated data and a much smaller cache than in previous approaches.

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Materialized Data Mining Views

Cited by 24 publications

References 8 publications

Incremental Data Mining Using Concurrent Online Refresh of Materialized Data Mining Views

Incremental Data Mining Using Concurrent Online Refresh of Materialized Data Mining Views

A Study on Answering a Data Mining Query Using a Materialized View

Using Condensed Representations for Interactive Association Rule Mining

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