OLAP for Multidimensional Semantic Web Databases

  • Adriana MateiEmail author
  • Kuo-Ming Chao
  • Nick Godwin
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 206)


Semantic Web (SW) and web data have become increasingly important sources to support Business Intelligence (BI), but it is difficult to manage due to its scalability in their volumes, inconsistency in semantics and complexity in representations. On-Line Analytical Processing (OLAP) is an important tool in analysing large and complex BI data, but it lacks the capability of processing disperse SW data due to the nature of its design. A new concept with a richer vocabulary than the existing ones for OLAP is needed to model distributed multidimensional semantic web databases. In this paper we proposed a new OLAP framework with multiple layers including additional vocabulary, extended OLAP operators, and SPARSQL to model heterogeneous semantic web data, unify multidimensional structures, and provide new enabling functions for interoperability. We present the framework with examples to demonstrate its capability to unify RDF Data Cube (QB) [2] and QB4OLAP [1] with additional vocabulary elements to handle both informational and topological data [3] in Graph OLAP. It is also able to compose multiple databases (e.g. energy consumptions and property market values etc.) to generate observations through semantic pipe-like operators.


On-Line Analytical Processing Business Intelligence Semantic web Data management RDF vocabulary 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Faculty of Engineering and ComputingCoventry UniversityCoventryUK

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