Leveraging Semantics to Represent and Compute Quantitative Indexes: The RDFIndex Approach

Abstract

The compilation of key performance indicators (KPIs) in just one value is becoming a challenging task in certain domains to summarize data and information. In this context, policymakers are continuously gathering and analyzing statistical data with the aim of providing objective measures about a specific policy, activity, product or service and making some kind of decision. Nevertheless existing tools and techniques based on traditional processes are preventing a proper use of the new dynamic and data environment avoiding more timely, adaptable and flexible (on-demand) quantitative index creation. On the other hand, semantic-based technologies emerge to provide the adequate building blocks to represent domain-knowledge and process data in a flexible fashion using a common and shared data model. That is why a RDF vocabulary designed on the top of the RDF Data Cube Vocabulary to model quantitative indexes is introduced in this paper. Moreover a Java and SPARQL based processor of this vocabulary is also presented as a tool to exploit the index meta-data structure and automatically perform the computation process to populate new values. Finally some discussion, conclusions and future work are also outlined.

This work is part of the FP7 Marie Curie Initial Training Network “RELATE” (cod. 264840) and developed in the context of the Workpackage 4 and more specifically under the project “Quality Management in Service-based Systems and Cloud Applications”. It is also supported by the ROCAS project with code TIN2011-27871, a research project partially funded by the Spanish Ministry of Science and Innovation.