CUBISTProject reference: 257403
Funded under: FP7-ICT
Combining and Uniting Business Intelligence and Semantic Technologies
Total cost:EUR 4 357 196
EU contribution:EUR 3 029 834
Subprogramme:ICT-2009.4.3 - Intelligent Information Management
Call for proposal:FP7-ICT-2009-5
Funding scheme:CP - Collaborative project (generic)
CUBIST - Your Business Intelligence<p>The constantly growing amounts of data and an emerging trend of incorporating unstructured data into analytics is bringing new challenges to Business Intelligence (BI). Contemporary BI solutions fall short in the following aspects: </p><p>Firstly, they focus only on structured data and disregard the increasing amount of information hidden in unstructured data.</p><p>Secondly, BI users are dealing with increasingly complex analyses, but the complexity of BI tools becomes the<br />biggest barrier for their success.</p><p>CUBIST is an EU funded research project with a visionary approach that leverages BI to a new level of precise, meaningful and user-friendly analytics of data by following a best-of-breed approach that combines essential features of Semantic Technologies, Business Intelligence and Visual Analytics. CUBIST aims to <br />• support federation of data from unstructured and structured sources <br />• persist the federated data in an Information Warehouse; an approach based on a BI enabled triple store<br />• provide novel ways of applying Visual Analytics based on meaningful diagrammatic representations.<br /></p>
Constantly growing amounts of data, complicated and rapidly changing economic interactions, and an emerging trend of incorporating unstructured data into analytics, brings new challenges to Business Intelligence (BI). Contemporary solutions involve BI users dealing with increasingly complex analyses. According to a 2008 study, the complexity of BI tools is the biggest barrier for success of these systems. Moreover, classical BI solutions have, so far, neglected the meaning of data.
Semantic Technologies (STs), deal with this meaning and are capable of dealing with both unstructured and structured data. Having the meaning of data in place, a user can be guided during his work with data. In particular, we foresee FCA (Formal Concept Analysis), which is a well known ST, to be a key element of new hybrid BI system. However, STs have, traditionally, operated on data sets a magnitude smaller than classical BI solutions. They also lack standard BI functionalities such as Online Analytical Processing queries, making it difficult to perform analysis over semantic data. On the other hand, 'understanding data' will improve classical methods in BI-CUBIST combines essential features of ST and BI. We envision a system with the following core features:
* It supports federation of data from a variety of unstructured and structured sources,
* The persistency layer is an Information Warehouse; having a BI enabled triple store in its center,
* Semantic information is used to improve BI best practices,
* CUBIST enables a user to perform BI operations over semantic data,
* Semantic data warehouse is used to realize advance mining techniques known from, in particular FCA.
* Novel ways of applying visual analytics in which meaningful diagrammatic representations of the data will be used for representing, navigating through and visually querying the data.
CUBIST has three use cases in the fields of market intelligence, computational biology and control centre operations.
CARDIFF, United Kingdom
SHEFFIELD, United Kingdom
EDINBURGH, United Kingdom