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Tech Report kmi-05-11 Abstract


A Generic Library of Problem Solving Methods for Scheduling Applications
Techreport ID: kmi-05-11
Date: 2005
Author(s): Dnyanesh Rajpathak, Enrico Motta, Zdenek Zdrahal, and Rajkumar Roy
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In this report we propose a generic library of problem-solving methods for solving scheduling applications. Some attempts have been made in the past at developing a library scheduling problem-solvers but in some cases these earlier proposals subscribe to a specific application domain of scheduling, which restricted their reusability, while in some other cases they subscribe to the specific problem-solving technique which may be applicable only to a subset of the space of scheduling problems. Finally all the existing scheduling libraries fail to provide an adequate degree of detail and precision. In our approach we subscribe to the Task-Method-Domain-Application knowledge modeling framework which provides a structured organization for the different components of the library. At the task level, we constructed generic scheduling task ontology to formalize the space of scheduling problems. At the method level, we constructed a generic problem-solving model of scheduling that generalizes from the variety of approaches to scheduling problem-solving, which can be found in the literature. And, then seven knowledge-intensive methods are developed as a specialization of generic problem-solving model of scheduling. Finally, we validated our library on a number of applications to demonstrate its generic nature and effective support for developing scheduling applications.

Publication(s):

Submitted to IEEE Transactions on Knowledge and Data Engineering.
 
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Multimedia and Information Systems
Our research is centred around the theme of Multimedia Information Retrieval, ie, Video Search Engines, Image Databases, Spoken Document Retrieval, Music Retrieval, Query Languages and Query Mediation.

We focus on content-based information retrieval over a wide range of data spanning form unstructured text and unlabelled images over spoken documents and music to videos. This encompasses the modelling of human perception of relevance and similarity, the learning from user actions and the up-to-date presentation of information. Currently we are building a research version of an integrated multimedia information retrieval system MIR to be used as a research prototype. We aim for a system that understands the user's information need and successfully links it to the appropriate information sources, be it a report or a TV news clip. This work is guided by the vision that an automated knowledge extraction system ultimately empowers people making efficient use of information sources without the burden of filing data into specialised databases.

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