Towards the Explanation of Workflows

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Across many fields involving complex computing, software systems are being augmented with workflow logging functionality. The log data can be effectively organized using declarative structured languages such as OWL; however, such declarative encodings alone are not enough to facilitate understandable workflow systems with high quality explanation. In this paper, we present our approach for visually explaining OWL-encoded workflow logs for complex systems, which includes the following steps: (i) identifying and normalizing provenance in workflow logs using the provenance interlingua PML2, (ii) using this provenance information, as well as supplemental log data, building an abstracted workflow representation known as a RITE network (capable of storing workflow state Relationships, Identifiers, Types, and Explanations), and (iii) visualizing the workflow log by displaying its provenance information as a directed acyclic graph and presenting supplemental explanations for individual workflow states and relationships. To demonstrate these techniques, we describe the design of a workflow explainer for the Generalized Integrated Learning Architecture (GILA) -- a multi-agent platform designed to use multiple learners to solve problems such as resolving airspace allocation conflicts. We also comment on how our approach can be generalized to explain other complex workflow systems.


DateCreated ByLink
July 17, 2011
Patrick WestDownload

Related Projects:

Generalized Integrated Learning Architecture (GILA)
Principal Investigator: Jim Hendler and Deborah L. McGuinness
Description: The Generalized Integrated Learning Architecture [GILA] is a general-purpose integrated multi-agent platform that solves domain problems by learning from a problem-solution pair submitted by a human expert. One of the key purposes of GILA is to learn how humans solve complex problems and apply this knowledge to future problems. A complex problem domain known as the Airspace Control Scenario has been chosen to drive the development of GILA and evaluate its performance. The objective of this problem domain is to resolve conflicts in a collection of airspace allocations for aircrafts.
Inference Web Project LogoInference Web
Principal Investigator: Deborah L. McGuinness
Description: The Inference Web is a Semantic Web based knowledge provenance infrastructure that supports interoperable explanations of sources, assumptions, learned information, and answers as an enabler for trust. Provenance - if users (humans and agents) are to use and integrate data from unknown, uncertain, or multiple sources, they need provenance metadata for evaluation Interoperability - more systems are using varied sources and multiple information manipulation engines, thus increasing interoperability requirements Explanation/Justification - if information has been manipulated (i.e., by sound deduction or by heuristic processes), information manipulation trace information should be available Trust - if some sources are more trustworthy than others, trust ratings are desired The Inference Web consists of two important components: Proof Markup Language (PML) Ontology - Semantic Web based representation for exchanging explanations including provenance information - annotating the sources of knowledge justification information - annotating the steps for deriving the conclusions or executing workflows trust information - annotating trustworthiness assertions about knowledge and sources IW Toolkit - Web-based and standalone tools that facilitate human users to browse, debug, explain, and abstract the knowledge encoded in PML.

Related Research Areas:

Data Frameworks
Lead Professor: Peter Fox
Description: None.
Future Web
Lead Professor: Jim Hendler
Description: Since its inception the World Wide Web has changed the ways people work, play, communicate, collaborate, and educate. There is, however, a growing realization among researchers across a number of disciplines that without new research aimed at understanding the current, evolving and potential Web, we may be missing or delaying opportunities for new and revolutionary capabilities. To model the Web, it is necessary to understand the architectural principles that have provided for its growth. Looking into the future, to be sure that it supports the basic social values of trustworthiness, personal control over information, and respect for social boundaries, a research agenda must be pursued that targets the Web and its use as a primary focus of attention. This research requires powerful scientific and mathematical techniques from many disciplines to explore the modeling of the Web from network- and information- centric views.
Knowledge Provenance
Lead Professor: Deborah L. McGuinness
Description: Knowledge Provenance
Concepts: Provenance,