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Webarch star analys12/12/2023 ![]() ![]() We then introduce a measure of seamlessness for any chain of applications described in our Application Ontology. Next, we exercise the ontology to model a series of applications performed in a hypothetical but realistic and fully-implemented scenario. 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. 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. 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. 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. 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. Visual analytics is a costly endeavor in which analysts must coordinate the execution of incompatible visualization tools to derive coherent presentations from complex information.
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