Pattern Mining over Star Schemas in the Onto4AR Framework

C. Antunes
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引用次数: 7

Abstract

Storing data according to the multidimensional model, in particular following star schemas, has demonstrated to be one of the most adequate forms to ease the exploration of data. However, this exploration has been limited to be query-based, leaving the discovery of hidden information to a second plan. The main reason for this, relates to the inability of traditional mining techniques to deal with several data tables at the same time. In this paper, we propose a new approach to mine patterns among data stored as a star schema, based in a domain driven framework, where available knowledge is represented in a domain ontology. Pattern mining is performed by an apriori-based algorithm - the D2Apriori, but more efficient algorithms are being implemented and tested, in order to solve performance issues related with the large amount of data stored in data warehouses.
Onto4AR框架中星型模式的模式挖掘
根据多维模型存储数据,特别是按照星型模式存储数据,已被证明是简化数据探索的最合适的形式之一。然而,这种探索被限制为基于查询的,将隐藏信息的发现留给了第二个计划。造成这种情况的主要原因是传统的挖掘技术无法同时处理多个数据表。在本文中,我们提出了一种新的方法来挖掘以星型模式存储的数据中的模式,该方法基于领域驱动框架,其中可用的知识以领域本体表示。模式挖掘是由基于先验的算法(D2Apriori)执行的,但是为了解决与存储在数据仓库中的大量数据相关的性能问题,正在实现和测试更有效的算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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