Abstract
Statistical reasoning was one of the earliest methods to draw insights from data. However, over the last three decades, association rule mining and online analytical processing have gained massive ground in practice and theory. Logically, both association rule mining and online analytical processing have some common objectives, but they have been introduced with their own set of mathematical formalizations and have developed their specific terminologies. Therefore, it is difficult to reuse results from one domain in another. Furthermore, it is not easy to unlock the potential of statistical results in their application scenarios. The target of this paper is to bridge the artificial gaps between association rule mining, online analytical processing and statistical reasoning. We first provide an elaboration of the semantic correspondences between their foundations, i.e., itemset apparatus, relational algebra and probability theory. Subsequently, we propose a novel framework for the unification of association rule mining, online analytical processing and statistical reasoning. Additionally, an instance of the proposed framework is developed by implementing a sample decision support tool. The tool is compared with a state-of-the-art decision support tool and evaluated by a series of experiments using two real data sets and one synthetic data set. The results of the tool validate the framework for the unified usage of association rule mining, online analytical processing, and statistical reasoning. The tool clarifies in how far the operations of association rule mining and online analytical processing can complement each other in understanding data, data visualization and decision making.
Highlights
D ECISION support techniques play an essential role in today’s business environment
(iii), the online analytical processing (OLAP) average aggregate function turns out to correspond to conditional expected values, which closes the loop between association rule mining (ARM), OLAP, and probability theory with respect to the most important constructs in ARM and OLAP
In this paper, we analyzed a series of approaches to overcome the divide between the three most popular decision support techniques (DSTs), i.e., statistical reasoning (SR), OLAP and ARM
Summary
D ECISION support techniques play an essential role in today’s business environment. Since the 17th century, statistical reasoning (SR) has been used extensively to shape business decisions [1] and it was the earliest method to draw insights from data. With the rise of information technology in the 1990s, online analytical processing (OLAP) [5] and association rule mining (ARM) [6] have emerged as powerful decision support techniques (DSTs) [7], both with their specific rationales, objectives, and attitudes. Over the years, both OLAP and ARM have gained massive ground in practice We appraise all of these decision support frameworks and different ways of integrating DSTs; the concept of semantic correspondences between DSTs is yet to be elaborated in state-of-the-art.
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