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High-probability grants
According to our matching algorithm, David Leake is the likely recipient of the following grants.
| Years |
Recipients |
Code |
Title / Keywords |
Matching
score |
| 1994 — 1997 |
Leake, David |
N/AActivity Code Description:
No activity code was retrieved: click on the grant title for more information |
Ria: Learning Case Adaptation For Case-Base Reasoning
9409348 Leake Case-based reasoning (CBR) systems reason from experience: They solve new problems by retrieving relevant prior cases and performing case adaptation knowledge is normally provided by the system developer, hand-coded for a single task. The difficulty of encoding effective adaptation rules is widely recognized as a serious impediment to the development of case-based reasoning systems. This research addresses that problem by developing a model of automatic learning to improve case adaptation. The model learns memory search procedures to operationalize general adaptation rules. The model starts results in learning memory search cases tracing the memory search processes used and their results. Those memory search cases reflect the applicability of particular memory search strategies to particular adaption, learned memory search cases are retrieved to provide specific guidance for memory search. The effects of the learning process on adaptation performance are evaluated by a series of experiments including "ablation" studies and direct assessment of the quality of the adaptations by human subjects.
|
1 |
| 2007 — 2011 |
Simmhan, Yogesh (co-PI) [⬀]
Leake, David
Plale, Beth [⬀] |
N/AActivity Code Description:
No activity code was retrieved: click on the grant title for more information |
Sdci Data: New Toolkit For Provenance Collection, Publishing, and Experience Reuse
OCI - SDCI Data: New Toolkit for Provenance Collection, Publishing, and Experience Reuse
As research digital data collections created through computational science experiments proliferate, it becomes increasingly important to address the provenance issues of the data validity and quality: to record and manage information about where each data object originated, the processes applied to the data products, and by whom. The first outcome of this work is a provenance collection and experience reuse tool that makes minimal assumptions about the software environment and imposes minimal burden on the application writer. It stores and produces results in a form suitable for publication to a digital library. The provenance collection system is a standalone system that imposes a minimal burden on users to integrate it into their application framework and it exhibits good performance.
A second outcome of the work is a recommender system for workflow completion that employs case-based reasoning to provenance collections in order to make suggestions to users about future workflow-driven investigations. The workflow completion tool builds on computer models of case-based reasoning to develop a support system that leverages the collective experience of the users of the provenance system to provide suggestions. As a key part of effectively evaluating aspects of the tool, this work builds a gigabyte benchmark database of real and synthetic provenance information. Real workflows are sought from the community, with synthetic extensions to the data set for completeness for purposes of testing. The software and database are available to the research community.
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1 |
