Data-Driven Performance Analysis of Scheduled Processes
Data-Driven Performance Analysis of Scheduled Processes
复制标题
数据驱动的预定流程性能分析
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
C. Bunnell
中科院分区：
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作者：
Arik Senderovich;Andreas Rogge;A. Gal;J. Mendling;A. Mandelbaum;S. Kadish;C. Bunnell
调度业务流程的性能对于服务和制造系统至关重要。然而，目前的技术性能分析不考虑语义和过程的角度来看。在这项工作中，我们通过开发一种新的方法，利用丰富的进程日志来分析调度进程的性能来解决这个差距。所提出的方法结合了模拟，验证分析和统计方法。在我们的方法的核心是从数据中发现一个单独的情况下的模型，基于扩展的有色Petri网形式主义。所得到的模型可以模拟回答性能查询，但它是计算效率低下。为了降低计算成本，发现的模型被投影到网络中，这是一种能够实现高效性能分析的形式主义。的投影是由一系列的折叠操作，改变了结构和动态的Petri网模型。我们使用来自美国大型门诊癌症医院Dana-Farber癌症研究所的真实数据集评估该方法。
The performance of scheduled business processes is of central importance for services and manufacturing systems. However, current techniques for performance analysis do not take both queueing semantics and the process perspective into account. In this work, we address this gap by developing a novel method for utilizing rich process logs to analyze performance of scheduled processes. The proposed method combines simulation, queueing analytics, and statistical methods. At the heart of our approach is the discovery of an individual-case model from data, based on an extension of the Colored Petri Nets formalism. The resulting model can be simulated to answer performance queries, yet it is computational inefficient. To reduce the computational cost, the discovered model is projected into Queueing Networks, a formalism that enables efficient performance analytics. The projection is facilitated by a sequence of folding operations that alter the structure and dynamics of the Petri Net model. We evaluate the approach with a real-world dataset from Dana-Farber Cancer Institute, a large outpatient cancer hospital in the United States.