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

Zakrzewicz, Maciej; Morzy, Mikołaj; Wojciechowski, Marek

doi:10.1007/978-3-540-30182-0_50

Cited by 6 publications

(2 citation statements)

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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%

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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%

“…These methods include incremental mining, complementary mining, verifying mining, and full mining. Data mining query optimization using materialized data mining views is covered in [8,14].…”

Section: Create Materialized View V_saint_emilionmentioning

confidence: 99%