Investigation design: The structural elements of knowledge-seeking efforts

Abstract

Abstract Knowledge about human systems usually comes from deliberate, organized efforts. These efforts are increasingly collaborative with partnering among diverse teams of experts. This has spawned research in the means for data integration and data lineage, or provenance, to better enable sharing of data and workflows. Such research has focused on specific problems in domain representation, such as ontology domain modeling, provenance standards and methodologies, and automated workflow management. While all these contributions are highly relevant for knowledge seeking efforts, what is missing is a meta model that accounts for all elements of investigation. This work introduces the concept of investigation as a means to formalize knowledge seeking efforts involving collaborative human action. We model the investigation concept as a set of seven elements common to all knowledge-seeking efforts. Incorporated into the proposed investigation design are the concepts from emerging sensor-observation standards and W3C provenance standards. This design differs from other approaches for knowledge acquisition modeling in its focus on reifying the management of effort – here referred to as an initiative, and the abstraction and reification of analytic results – here referred to as judgments. Further, we provide and initial attempt to identify specific workflow and data model design patterns within each of the seven investigation elements. An example illustrates the various aspects of our approach.