A Conceptual Model for Combining Enhanced OLAP and Data Mining Systems

Usman, Muhammad; Asghar, Sohail; Fong, Simon

doi:10.1109/ncm.2009.354

Cited by 11 publications

(7 citation statements)

References 22 publications

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“…It is evident from the literature review that none of the previous work done in the past has addressed the efficient analysis of numeric and high cardinality nominal variables, effective visualization, automatic schema generation and integration of ware housing and mining in a single framework. Several works [8,32,33,35,36,40,41] suggested that with the integration of data mining with the warehousing system a number of benefits can be achieved. In this paper, we have used the hierarchical clustering technique for the efficient analysis of data as a pre-processing step of data mining.…”

Section: Proposed Methodologymentioning

confidence: 99%

Integration of Data Mining and Data Warehousing: A Practical Methodology

Usman¹,

Pears²

2010

IJACT

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Section: Proposed Methodologymentioning

confidence: 99%

Integration of Data Mining and Data Warehousing: A Practical Methodology

Usman¹,

Pears²

2010

IJACT

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“…These dimensions contain classifications obtained via clustering or other algorithms on the original facts and can be materialized and used (with some limitations) just like ordinary OLAP dimensions. Usman et al [7] review the research literature on coupling OLAP and DM and propose a conceptual model for combining enhanced OLAP with data mining systems. The urge to enhance the analysis by integrating OLAP and DM was expressed in multiple publications in the past.…”

Section: Related Workmentioning

confidence: 99%

Discovering OLAP dimensions in semi-structured data

Mansmann

Rehman

Weiler

et al. 2014

Information Systems

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OLAP cubes enable aggregation centric analysis of transactional data by shaping data records into measurable facts with dimensional characteristics. A multidimensional view is obtained from the available data fields and explicit relationships between them. This classical modeling approach is not feasible for scenarios dealing with semi structured or poorly structured data. We propose to the data warehouse design methodology with a content driven discovery of measures and dimensions in the original dataset. Our approach is based on introducing a data enrichment layer responsible for detecting new structural elements in the data using data mining and other techniques. Discovered elements can be of type measure, dimension, or hierarchy level and may represent static or even dynamic properties of the data. This paper focuses on the challenge of generating, maintaining, and querying discovered elements in OLAP cubes. We demonstrate the power of our approach by providing OLAP to the public stream of user generated content on the Twitter platform. We have been able to enrich the original set with dynamic characteristics, such as user activity, popularity, messaging behavior, as well as to classify messages by topic, impact, origin, method of generation, etc. Knowledge discovery techniques coupled with human expertise enable structural enrichment of the original data beyond the scope of the existing methods for obtaining multidimensional models from relational or semi structured data.

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“…The latter contain classifications obtained by applying clustering or other algorithms on the original cube and can be materialized and used (with some limitations) just like ordinary dimensions for OLAP. Usman et al review the research literature on coupling OLAP and data mining in [17] and propose a conceptual model for combining enhanced OLAP with data mining systems. The urge to enhance the analysis by integrating OLAP and data mining was expressed in multiple publications in the past.…”

Section: Related Workmentioning

confidence: 99%

Discovering Dynamic Classification Hierarchies in OLAP Dimensions

Rehman

Mansmann

Weiler

et al. 2012

Lecture Notes in Computer Science

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The standard approach to OLAP requires measures and dimensions of a cube to be known at the design stage. Besides, dimensions are required to be non-volatile, balanced and normalized. These constraints appear too rigid for many data sets, especially semi-structured ones, such as user-generated content in social networks and other web applications. We enrich the multidimensional analysis of such data via content-driven discovery of dimensions and classification hierarchies. Discovered elements are dynamic by nature and evolve along with the underlying data set.We demonstrate the benefits of our approach by building a data warehouse for the public stream of the popular social network and microblogging service Twitter. Our approach allows to classify users by their activity, popularity, behavior as well as to organize messages by topic, impact, origin, method of generation, etc. Such capturing of the dynamic characteristic of the data adds more intelligence to the analysis and extends the limits of OLAP.

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A Conceptual Model for Combining Enhanced OLAP and Data Mining Systems

Cited by 11 publications

References 22 publications

Integration of Data Mining and Data Warehousing: A Practical Methodology

Integration of Data Mining and Data Warehousing: A Practical Methodology

Discovering OLAP dimensions in semi-structured data

Discovering Dynamic Classification Hierarchies in OLAP Dimensions

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