首页    期刊浏览 2024年11月27日 星期三
登录注册

文章基本信息

  • 标题:Mining Significant Temporal Networks Is Polynomial
  • 本地全文:下载
  • 作者:Guido Sciavicco ; Matteo Zavatteri ; Tiziano Villa
  • 期刊名称:LIPIcs : Leibniz International Proceedings in Informatics
  • 电子版ISSN:1868-8969
  • 出版年度:2020
  • 卷号:178
  • 页码:11:1-11:12
  • DOI:10.4230/LIPIcs.TIME.2020.11
  • 出版社:Schloss Dagstuhl -- Leibniz-Zentrum fuer Informatik
  • 摘要:A Conditional Simple Temporal Network with Uncertainty and Decisions (CSTNUD) is a formalism that tackles controllable and uncontrollable durations as well as controllable and uncontrollable choices simultaneously. In the classic top-down model-based engineering approach, a designer builds a CSTNUD to model, validate and execute some temporal plan of interest. Instead, in this paper, we investigate the bottom-up approach by providing a deterministic polynomial time algorithm to mine a CSTNUD from a set of execution traces (i.e., a log). This paper paves the way for the design of controllable temporal networks mined from traces that also contain information on uncontrollable events.
  • 关键词:Mining temporal constraints; cstnud; uncertainty; significant temporal network
国家哲学社会科学文献中心版权所有