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Visual analytics is a costly endeavor in which analysts must coordinate the execution of incompatible visualization tools to derive coherent presentations from complex information. Distributed environments such as the Web pose additional costs since analysts must also establish logical connections among shared results, decode unfamiliar data formats, and engage with broader sets of tools that support the heterogeneity of different information sources. These ancillary activities are often limiting factors to our vision of seamless analytics, which we define as the low-cost generation and reuse of analytical resources. In this paper, we offer a theory of analytics that formally explains how analysts can employ Linked Data to maintain and leverage explicit connections across shared results as well as manage different representations of information required by visualization tools. Our theory builds on the well-known benefits of interconnected data and provides new metrics that quantify the utility of interconnected user-and task-centric, analytical applications. To describe our theory, we first 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 chain of applications described in our Application Ontology. Finally, we extend the ontology to distinguish five types of applications based on the structure of data involved and the behavior of the tools used. Together, our seamlessness measure and application ontology compose our Five-Star Theory of Seamless Analytics that embodies tenets of Linked Data in a form that emits falsifiable predictions and which can be revised to better reflect and thus reduce the costs embedded within analytical environments.

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The Inference Web infrastructure for web explanations together with its underlying Proof Markup Language (PML) for encoding justification and provenance information has been used in multiple projects varying from explaining the behavior of cognitive agents to explaining how knowledge is extracted from multiple sources of information in natural language. The PML specification has increased significantly since its inception in 2002 in order to accommodate a rich set of requirements derived from multiple projects, including the ones mentioned above. In this paper, we have a very different goal than the other PML documents: to demonstrate that PML may be effectively used by simple systems (as well as complex systems) and to describe lightweight use of language and its associated Inference Web tools. We show how an exemplar scientific application can use lightweight PML descriptions within the context of an NSF-funded cyberinfrastructure project. The scientific application is used throughout the paper as a use case for the lightweight use of PML and the Inference Web and is meant to be an operational prototype for a class of cyberinfrastructure applications.

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