Paper Info
Title
Tuning a Blackboard-based Applicatio: A case study using GBB
Abstract
THE RUN-TIME PERFORMANCE OF A BLACKBOARD-BASED APPLICATION CAN BE SIGNIFICANTLY IMPROVED BY SELECTING AN APPROPRIATE BLACKBOARD DATABASE REPRESENTATION. WE PRESENT EMPIRICAL VALIDATION OF THIS STATEMENT BY TUNING A LARGE, BLACKBOARD-BASED AI APPLICATION. DRAMATIC PERFORMANCE GAINS WERE OBTAINED WITHOUT CHANGING PROBLEM SOLVING OR CONTROL ACTIVITIES. THE RESULTS UNDERSCORE THE IMPORTANCE OF EFFICIENT BLACKBOARD DATABASE OPERATIONS AND THE BENEFITS OF USING A FLEXIBLE, INSTRUMENTED BLACKBOARD DEVELOPMENT ENVIRONMENT TO MODIFY THE DATABASE REPRESENTATION AS DESIGN INTUITION EVOLVES INTO APPLICATION EXPERIENCE. THIS INVESTIGATION WAS FACILITATED BY THE USE OF THE GENERIC BLACKBOARD DEVELOPMENT SYSTEM (GBB) TO CONSTRUCT THE AI APPLICATION. GBB PROVIDED THE FLEXIBILITY TO EASILY CHANGE THE DATABASE IMPLEMENTATION WITHOUT RECODING. SIMILAR PERFORMANCE TUNING POSSIBILITIES ARE AVAILABLE TO ANY APPLICATION WRITTEN USING GBB.
Year
Venue
Keywords
1988
AAAI
blackboard-based application,application experience,application written using gbb,generic blackboard development system,efficient blackboard database operations,database representation,blackboard-based applicatio,ai application,database implementation without recoding,appropriate blackboard database representation,blackboard-based ai application,case study
Field
DocType
Citations 
Software engineering,Computer science,Development environment,Blackboard system,Artificial intelligence,Performance tuning,Machine learning
Conference
0
PageRank 
References 
Authors
0.34
5
2
Name
Order
Citations
PageRank
Daniel D. Corkill1722467.03
Kevin Q. Gallagher24927.11