Multi Level Mining of Warehouse Schema

Abstract

Abstract. The two mature disciplines, namely Data Mining and Data Warehousing have broadly the same set of objectives. Yet, they have developed largely separate from each other resulting in different techniques being used in each discipline. It has been recognized that mining techniques developed for pattern recognition such as Clustering and Visualization can assist in designing data warehouse schema. However, a suitable methodology is required for the seamless integration of mining methods in the design of warehouse schema. In previous work, we presented a methodology that employs hierarchical clustering to derive a tree structure that can be used by a data warehouse designer to build a schema. We believe that, in order to strengthen the decision making process, there is a strong need for a method that automatically extracts knowledge present at different levels of abstraction from a warehouse. We demonstrate with examples how mining at different levels of a hierarchical warehouse schema can give new insights about the underlying data cluster which not only helps in building more meaningful dimensions and facts for data warehouse design but can also improve the decision making process.

Mining predicate rules without minimum support threshold

“…The prominent algorithms in-clude the positive AR (Agrawal et al, 1993;Bar-alis et al, 2008;Krishnapuram, 2016), negative AR (Balakrishna et al, 2019;Jabbour et al, 2018;Kong et al, 2018), and combined approach for neg-ative and positive rules for large datasets (Bagui & Dhar, 2018;Bemarisika & Totohasina, 2018;Par-fait et al, 2018;Zhao et al, 2017). Moreover, ap-plication specific ARM algorithms have been de-veloped for various areas including medicine (Bo-rah & Nath, 2018;Harahap et al, 2018;Moses et al, 2015), crime (Buczak & Gifford, 2010;Has-sani et al, 2016), agriculture (Bhatia & Gupta, 2014;Bisht & Samantaray, 2015;Geetha, 2015), distributed environments (Qin et al, 2016;Salah et al, 2017), data warehousing (Usman, 2017) etc. Mustafa et al (2006) presented a technique called the Enhanced Apriori Algorithm (EAA) based on second support and confidence for the discovery of association rules for significant rare data.…”

Measuring Diversity of Associations Rules Extracted from A Data Warehouse

“…The model was implemented by the same authors in [3], which included two case studies on real world data sets. Authors extracted patterns using the proposed model in previous study and evaluated these patterns using advanced measures namely Rae, Con and Hill.…”

“…In this research work, we have enhanced our previous work done in [3]. The previous work included a model which extracted patterns in a data warehouse environment at multiple levels of abstraction.…”

Towards Data Warehouse Schema Design from Social Networks - Dynamic Discovery of Multidimensional Concepts

“…To overcome these problems, many authors have worked on dynamic discovery of MC and have used data mining to build a DW. In this context, (Usman and Pears, 2011) provide a methodology to design semi-automatically DWs schema with hierarchical clustering. This latter is used to perform a pre-processing on the data.…”