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.