Recommender systems of problem-solving environments
Abstract
It has been predicted that, by the beginning of the next century, the available computational power will enable anyone with access to a computer to find an answer to any scientific problem that has a known or effectively computable solution. The concept of problem solving environments (PSEs) promises to contribute toward the realization of this prediction for multidisciplinary physical modeling. It provides students, scientists and engineers with systems that allow them to spend more time doing science and engineering rather than computing. The first goal of this thesis is to support programming in-the-large where the user specifies his problem in a natural, high level form along with computational objectives (on performance criteria such as accuracy, time, cost, etc.,) and the domain specific PSE selects the resources (algorithm, parameters, platform) necessary to compute the problem solution. The methodology proposed to realize this 'recommender' functionality consists of a knowledge discovery approach and case based reasoning mechanisms. A kernel to aid in the rapid prototyping of recommender systems is designed and the effectiveness of this methodology in two domains of scientific computing--elliptic partial differential equations and numerical quadrature--is demonstrated. The second goal of this thesis is to extend and implement the above methodology in the context of networked computing, where the libraries and machine resources are assumed geographically distributed over the world and connected through a global infrastructure such as the Internet. In this scenario, we demonstrate a collaborative methodology--a multi-agent approach that tracks the relative efficacies of recommender systems, using a notion of reasonableness. Finally, the developed recommender systems are interfaced with a well known mathematical software repository system--the Guide to Available Mathematical Software (GAMS)--to facilitate intelligent search and retrieval of scientific software.
Degree
Ph.D.
Advisors
Houstis, Purdue University.
Subject Area
Computer science|Artificial intelligence
Off-Campus Purdue Users:
To access this dissertation, please log in to our
proxy server.