Increasing and Automating Adaptivity of LogicBlox Datalog Platform

Declarative programming techniques have been used to make programming available to common users through introducing query languages such as SQL and spreadsheet programs such as Excel. The proposed industrial partner, LogicBlox, has developed software that enables highly-complex data analysis through the flexible and familiar form of spreadsheet computing. The main goal of this proposal is to increase and automate adaptivity of LogicBlox platform, and to evaluate proposed methodology through applications in local industry. More specifically, we propose to extend LogicBlox Datalog with novel features that allow programmers to augment purely declarative specifications with problem-specific “advice” that directs the underlying solver to the solution. The problem-specific “advice” would automatically produce reasoning procedures that will be embedded into the generalpurpose LogicBlox’s Datalog engine to obtain a special-purpose solver for the problem that is being solved. The problem-specific fine-tuning would greatly increase the solving performance.

Faculty Supervisor:

Eugenia Ternovska

Student:

Partner:

LogicBlox;Simon Fraser University (Burnaby Campus)

Discipline:

Computer science

Sector:

Information and Communications Technology; Technology

University:

Simon Fraser University

Program: