IOS Press A Five-Star Rating Scheme to Assess

Abstract. Analytics is a widespread phenomenon that often requires analysts to coordinate operations across a variety of incom-patible tools. When incompatibilities occur, analysts are forced to configure tools and munge data, distracting them from their ultimate task objective. This additional burden is a barrier to our vision of seamless analytics, i.e. the use and transition of con-tent across tools without incurring significant costs. Our premise is that standardized semantic web technologies (e.g., RDF and OWL) can enable analysts to more easily munge data to satisfy tools ’ input requirements and better inform subsequent analytical steps. However, although the semantic web has shown some promise for interconnecting disparate data, more needs to be done to interlink user- and task-centric, analytic applications. We present five contributions towards this goal. First, we introduce an extension of the W3C PROV Ontology to model analytic applications regardless of the type of data, tool, or objective involved. Next, we exercise the ontology to model a series of applications performed in a hypothetical but realistic and fully-implemented scenario. We then introduce a measure of seamlessness for any ecosystem described in our Application Ontology. Next, we extend the ontology to distinguish five types of applications based on the structure of data involved and the behavior of the tools used. By combining our 5-star application rating scheme and our seamlessness measure, we propose a simple Five-Star Theory of Seamless Analytics that embodies tenets of the semantic web in a form which emits falsifiable predictions and which can be revised to better reflect and thus reduce the costs embedded within analytical environments