English

Hyper Temporal Networks

Data Structures and Algorithms 2017-03-23 v3 Artificial Intelligence

Abstract

Simple Temporal Networks (STNs) provide a powerful and general tool for representing conjunctions of maximum delay constraints over ordered pairs of temporal variables. In this paper we introduce Hyper Temporal Networks (HyTNs), a strict generalization of STNs, to overcome the limitation of considering only conjunctions of constraints but maintaining a practical efficiency in the consistency check of the instances. In a Hyper Temporal Network a single temporal hyperarc constraint may be defined as a set of two or more maximum delay constraints which is satisfied when at least one of these delay constraints is satisfied. HyTNs are meant as a light generalization of STNs offering an interesting compromise. On one side, there exist practical pseudo-polynomial time algorithms for checking consistency and computing feasible schedules for HyTNs. On the other side, HyTNs offer a more powerful model accommodating natural constraints that cannot be expressed by STNs like Trigger off exactly delta min before (after) the occurrence of the first (last) event in a set., which are used to represent synchronization events in some process aware information systems/workflow models proposed in the literature.

Keywords

Cite

@article{arxiv.1503.03974,
  title  = {Hyper Temporal Networks},
  author = {Carlo Comin and Roberto Posenato and Romeo Rizzi},
  journal= {arXiv preprint arXiv:1503.03974},
  year   = {2017}
}
R2 v1 2026-06-22T08:51:59.893Z