The CUBIST Project: Combining and Uniting Business Intelligence with Semantic Technologies

The CUBIST Project: Combining and Uniting Business Intelligence with Semantic Technologies

Simon Andrews
Copyright: © 2013 |Pages: 15
DOI: 10.4018/ijiit.2013100101
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As a preface to this Special 'CUBIST' Edition of the International Journal of Intelligent Information Technologies (IJIIT), this article describes the European Framework Seven Combining and Unifying Business Intelligence with Semantic Technologies (CUBIST) project, which ran from October 2010 to September 2013. The project aimed to combine the best elements of traditional BI with the newer, semantic, technologies of the Sematic Web, in the form of the Resource Description Framework (RDF), and Formal Concept Analysis (FCA). CUBIST's purpose was to provide end-users with “conceptually relevant and user friendly visual analytics” to allow them to explore their data in new ways, discovering hidden meaning and solving hitherto difficult problems. To this end, three of the partners in CUBIST were use-cases: recruitment consultancy, computational biology and the space industry. Each use-case provided their own requirements and problems that were finally addressed by the prototype CUBIST visual-analytics developed in the project.
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There have been three conference workshops dedicated to the CUBIST project (Andrews & Dau, 2012; Andrews & Dau, 2013; Dau, 2011). Each has involved a number of papers produced by members of the project consortium and by people external to CUBIST with an interest in its aims and approaches. In this special edition of IJIIT, selected extended versions of some of these papers are presented.

In Browsing Large Concept Lattices through Tree Extraction and Reduction Methods, Melo, Le-Grand and Aufare describe an approach used in CUBIST to simplify concept lattices by extracting and visualising trees derived from them. Browsing concept lattices from Formal Concept Analysis becomes a problem as the number of concepts can grow significantly with the number of objects and attributes. Alternative, browse-able trees, combined with reduction methods such as fault-tolerance and concept clustering provide a much more manageable approach.

McLeod, Iskandar and Burger take the semantic approach to exploring biological images in Towards the Semantic Representation of Biological Images: From Pixels To Regions. Biomedical images and models contain vast amounts of information, only accessible by domain experts. Although a semantic representation in which every image pixel is featured is expensive, more abstract renditions, such as those made possible through Region Connection Calculus and the W3C Geospatial Vocabulary, provide a basic description that enables a non-expert end-user to perform a number of queries.

In Gene Co-Expression in Mouse Embryo Tissues, Andrews and McLeod develop some existing ideas in Formal Concept Analysis to provide an analysis of a large data set of gene expressions in mouse embryo tissues. In biology, there is a mapping between a protein and the gene that helped create it. Working together in groups, genes are responsible for the production of tissues and organs, thus the identification of such groups is of interest to biologists exploring the development and specialisation of tissues. This paper describes a new technique for managing complexity based on 'fault tolerance' and the identification of disjoint sets in data. Using this technique, distinct groups of co-expressed genes are identified.

Polovina explores the possibilities of using the semantics of transactions to give enterprises new insight into their business processes. He describes A Transaction-Oriented Architecture for Enterprise Systems. Many enterprises risk business transactions based on information systems that are incomplete or misleading, given that 80-85% of all corporate information remains outside of the processing scope of such systems. Enterprise architectures are needed that captures more of this 'soft' information. Such architectures can be achieved through modelling more holistically the transactions that collectively describe the business and its processes. Such an architecture captures the real-world meaning of the transactions, something not possible using traditional, data driven, IT approaches.

In this first article (an 'extended preface' to this special edition), the CUBIST project itself is described, covering its background, the real-world use-cases involved in the project, the semantic technologies developed and employed and some of the results obtained. Finally, some key areas of further work and exploitation are described with a conclusion to summarise the overall success of the project.

It is anticipated that the reader of this CUBIST edition of IJIIT will be motivated to explore semantic technologies, and, in particular, the new possibilities of applying Formal Concept Analysis (FCA) and the Resource Description Framework (RDF) to their data, perhaps initially by exploring the contents of the original CUBIST conference workshops. Creating an ontology of their information, storing their data as RDF triples and employing new visual analytics may give the reader new ways to be productive with their own data and find hidden insights into their area of business or research domain.

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