• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International Journal of Innovative Computing Information and Control >A MORE EFFICIENT DETERMINISTIC ALGORITHM IN PROCESS MODEL DISCOVERY
【24h】

A MORE EFFICIENT DETERMINISTIC ALGORITHM IN PROCESS MODEL DISCOVERY

机译：过程模型发现中更有效的确定性算法

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Alpha is a basic deterministic discovery algorithm that has been enhanced by Alpha*, Alpha++ and Alpha#. Alpha does an analysis of place, transition, and firing locally on each trace in the event log, which causes the time complexity of Alpha to be high for large event logs. In this paper, the Alpha-Tree (Alpha-T) algorithm is proposed to enhance Alpha's time complexity performance and quality of discovery. Based on generalized tuple pattern recognition (GTPR) inside the adjacency list tree (ALT) data structure, Alpha-T is able to simplify the tuple pattern analysis, resulting in a more efficient time complexity (O (t~3)) compared to Alpha (O (t~4)). Alpha-T reduces the time complexity by localizing the effect of the event log size to the preprocessing stage, which diminishes the number of steps in the discovery processing stage. Then within processing, Alpha-T does execution pattern of logic directly and induction in the places gateway, and make it have more dynamically pattern that produces more completeness and correctness model than other algorithms. Finally, in the post-processing stage, Alpha-T has a single graph structure, which reduces the complexity and memory space needed for workflow firing between place and transition.
机译：Alpha是一种基本的确定性发现算法，已通过Alpha *，Alpha ++和Alpha＃进行了增强。 Alpha对事件日志中每个迹线的局部放置，过渡和触发进行分析，这导致大型事件日志的Alpha时间复杂度很高。本文提出了一种Alpha-Tree（Alpha-T）算法，以提高Alpha的时间复杂度性能和发现质量。基于邻接表树（ALT）数据结构内的广义元组模式识别（GTPR），Alpha-T能够简化元组模式分析，与Alpha相比，其时间复杂度更高（O（t〜3）） （O（t〜4））。 Alpha-T通过将事件日志大小的影响集中到预处理阶段来减少时间复杂度，从而减少了发现处理阶段的步骤数。然后在处理过程中，Alpha-T直接在场所网关中执行逻辑和归纳的执行模式，并使其具有比其他算法更具动态性的模式，该模式可产生更多的完整性和正确性模型。最后，在后期处理阶段，Alpha-T具有单个图形结构，从而降低了工作流在放置和过渡之间触发所需的复杂性和内存空间。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号