SBI

Abstract

Despite the importance of analytical tools to organizations, they still lack the inference power needed to solve the requests of decision makers in a flexible and smarter way. We present a framework called SBI -- Semantic Business Intelligence - in which we apply ontologies on the description of business rules and concepts in order to support semantic-analytical functionalities that extend traditional OLAP operations. Such approach enables developers to customize BI solutions according to organizations' specific analytical requirements and allows developers align BI solutions to the latest business analytic requirements. We present how semantic inference is supported using batch and on-the-fly based strategies. In addition, we show how such semantic infrastructure makes the access to heterogeneous data sources transparent. Finally, we illustrate the benefits of our approach through Extracta, an analytical tool that relies on SBI ontologies and modules. Extracta enables an easy access to information and provides novel exploratory funcionalities based on semantics towards faster and smarter decisions.