Timed Partial Order Inference Algorithm

TL;DR

This paper introduces timed partial orders (TPOs) for modeling workflow schedules with timing constraints, and presents an efficient algorithm to mine TPOs from workflow logs, demonstrating its effectiveness on synthetic and real-world datasets.

Contribution

The paper proposes a novel TPO model combining partial orders with timing constraints and develops a graph-coloring based algorithm for inferring TPOs from logs with correctness guarantees.

Findings

TPOs effectively capture event dependencies and timing constraints.

The mining algorithm infers TPOs with hundreds of events from thousands of data points within seconds.

Results provide insights into workflow dependencies and timing.

Abstract

In this work, we propose the model of timed partial orders (TPOs) for specifying workflow schedules, especially for modeling manufacturing processes. TPOs integrate partial orders over events in a workflow, specifying ``happens-before'' relations, with timing constraints specified using guards and resets on clocks -- an idea borrowed from timed-automata specifications. TPOs naturally allow us to capture event ordering, along with a restricted but useful class of timing relationships. Next, we consider the problem of mining TPO schedules from workflow logs, which include events along with their time stamps. We demonstrate a relationship between formulating TPOs and the graph-coloring problem, and present an algorithm for learning TPOs with correctness guarantees. We demonstrate our approach on synthetic datasets, including two datasets inspired by real-life applications of aircraft turnaround and gameplay videos of the Overcooked computer game. Our TPO mining algorithm can infer TPOs involving hundreds of events from thousands of data-points within a few seconds. We show that the resulting TPOs provide useful insights into the dependencies and timing constraints for workflows.