Rank-based Heuristics for Optimizing the Execution of Product Data Models

TL;DR

This paper introduces new rank-based heuristics for optimizing Product Data Model executions, adapting data-intensive workflow techniques to improve process efficiency and cost-effectiveness.

Contribution

It proposes novel heuristics for PDM optimization and provides an extensive evaluation of existing solutions to demonstrate their effectiveness.

Findings

New heuristics improve execution time and cost.

Existing heuristics have varying effectiveness.

Proposed heuristics outperform some traditional methods.

Abstract

The Product Data Model (PDM) is an example of a data-centric approach to modelling information-intensive business processes, which offers exibility and facilitates process optimization. Because the approach is declarative in nature, there may be multiple, alternative execution plans that can produce the desired end product. To generate such plans, several heuristics have been proposed in the literature. The contributions of this work are twofold: (i) we propose new heuristics that capitalize on established techniques for optimizing data-intensive work ows in terms of execution time and cost and transfer them to business processes; and (ii) we extensively evaluate the existing solutions. Our results shed light on the merits of each heuristic and show that our new heuristics can yield significant benefits.