Experiences Developing A User-centric Presentation of A Domain-enhanced Provenance Data Model

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Presented at the AGU Fall Meeting 2010

Abstract:

Web-based science analysis and processing tools allow users to access, analyze, and generate visualizations of data without requiring the user manage data processing. These tools streamline science analysis activities by significantly reducing the data processing overhead for the user. The benefits of these tools come with a cost - the increased need for transparency in what data processing the tool performed on behalf of the user. By providing a clear explanation of what processing was performed and what domain-knowledge (assumptions, caveats, etc) modulated that processing we can increase user trust, understanding, and accountability and reduce misinterpretation or generation of inconsistent results.

We will describe our knowledge provenance solution infrastructure in action. A demonstration will include presentation capabilities using an integrated semantic data model, supporting provenance and science domain models, applied to an existing web-based Earth science data analysis tool (e.g. Giovanni from NASA/GSFC). We will explain how interactions with tool users lead us to the conclusion that user accessible visual presentations of the integrated semantic data model, that is exposing data provenance and how it is connected with and enhanced by domain-specific knowledge, are key to building a meaningful presentation of processing provenance and describe how this belief guided our visualization development.

History

DateCreated ByLink
April 15, 2012
18:15:23
Patrick WestDownload

Related Projects:

MDSA LogoMulti-Sensor Data Synergy Advisor (MDSA)
Principal Investigator: Peter Fox
Description: Augment Giovanni, the Goddard online tool for data access, visualization and analysis, with semantic web technologies and ontologies to support data inter-comparisons from different sensors or models. Data provenance (i.e. the essential data parameter details, quality and production caveats) will be added to enable researchers to make valid data comparisons and draw quantitative conclusions on specific analysis (e.g. ocean fertilization due to acid rain). In the resulting Giovanni framework, the dataset variable characteristics and related quality can be encoded so that inter-comparison rules can be derived.

Related Research Areas:

Data Science
Lead Professor: Peter Fox
Description: Science has fully entered a new mode of operation. Data science is advancing inductive conduct of science driven by the greater volumes, complexity and heterogeneity of data being made available over the Internet. Data science combines of aspects of data management, library science, computer science, and physical science using supporting cyberinfrastructure and information technology. As such it is changing the way all of these disciplines do both their individual and collaborative work.

Data science is helping scienists face new global problems of a magnitude, complexity and interdisciplinary nature whose progress is presently limited by lack of available tools and a fully trained and agile workforce.

At present, there is a lack formal training in the key cognitive and skill areas that would enable graduates to become key participants in escience collaborations. The need is to teach key methodologies in application areas based on real research experience and build a skill-set.

At the heart of this new way of doing science, especially experimental and observational science but also increasingly computational science, is the generation of data.

Concepts: eScience
Knowledge Provenance
Lead Professor: Deborah L. McGuinness
Description: Knowledge Provenance
Concepts: Provenance,
Semantic eScience
Lead Professor: Peter Fox
Description:
Science has fully entered a new mode of operation. E-science, defined as a combination of science, informatics, computer science, cyberinfrastructure and information technology is changing the way all of these disciplines do both their individual and collaborative work.
As semantic technologies have been gaining momentum in various e-Science areas (for example, W3C's new interest group for semantic web health care and life science), it is important to offer semantic-based methodologies, tools, middleware to facilitate scientific knowledge modeling, logical-based hypothesis checking, semantic data integration and application composition, integrated knowledge discovery and data analyzing for different e-Science applications.
Partially influenced by the Artificial Intelligence community, the Semantic Web researchers have largely focused on formal aspects of semantic representation languages or general-purpose semantic application development, with inadequate consideration of requirements from specific science areas. On the other hand, general science researchers are growing ever more dependent on the web, but they have no coherent agenda for exploring the emerging trends on the semantic web technologies. It urgently requires the development of a multi-disciplinary field to foster the growth and development of e-Science applications based on the semantic technologies and related knowledge-based approaches.

Concepts: eScience
X-informatics
Lead Professor: Peter Fox
Description: In the last 2-3 years, Informatics has attained greater visibility across a broad range of disciplines, especially in light of great successes in bio- and biomedical-informatics and significant challenges in the explosion of data and information resources. Xinformatics is intended to provide both the common informatics knowledge as well as how it is implemented in specific disciplines, e.g. X=astro, geo, chem, etc. Informatics' theoretical basis arises from information science, cognitive science, social science, library science as well as computer science. As such, it aggregates these studies and adds both the practice of information processing, and the engineering of information systems.
Concepts: , eScience