Discovering Simulation Models (original) (raw)
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A Python Extension to Simulate Petri nets in Process Mining
The capability of process mining techniques in providing extensive knowledge and insights into business processes has been widely acknowledged. Process mining techniques support discovering process models as well as analyzing process performance and bottlenecks in the past executions of processes. However, process mining tends to be "backward-looking" rather than "forward-looking" techniques like simulation. For example, process improvement also requires "what-if" analyses. In this paper, we present a Python library which uses an event log to directly generate a simulated event log, with additional options for end-users to specify duration of activities and the arrival rate. Since the generated simulation model is supported by historical data (event data) and it is based on the Discrete Event Simulation (DES) technique, the generated event data is similar to the behavior of the real process.
PROCESS MINING AND SIMULATION: A MATCH MADE IN HEAVEN
Event data are collected everywhere: in logistics, manufacturing, finance, healthcare, e-learning, egovernment, and many other domains. The events found in these domains typically refer to activities executed by resources at particular times and for particular cases. Process mining provides the means to discover the real processes, to detect deviations from normative processes, and to analyze bottlenecks and waste from such events. However, process mining tends to be backward-looking. Fortunately, simulation can be used to explore different design alternatives and to anticipate future performance problems. This keynote paper discusses the link between both types of analysis and elaborates on the challenges process discovery techniques are facing. Quality notions such as recall, precision, and generalization are discussed. Rather than introducing a specific process discovery or conformance checking algorithm, the paper provides a comprehensive set of conformance propositions. These conformance propositions serve two purposes: (1) introducing the essence of process mining by discussing the relation between event logs and process models, and (2) discussing possible requirements for the quantification of quality notions related to recall, precision, and generalization.
Decomposing Petri Nets for Process Mining -A Generic Approach
The practical relevance of process mining is increasing as more and more event data become available. Process mining techniques aim to discover, monitor and improve real processes by extracting knowledge from event logs. The two most prominent process mining tasks are: (i) process discovery: learning a process model from example behavior recorded in an event log, and (ii) conformance checking: diagnosing and quantifying discrepancies between observed behavior and modeled behavior. The increasing volume of event data provides both opportunities and challenges for process mining. Existing process mining techniques have problems dealing with large event logs referring to many different activities. Therefore, we propose a generic approach to decompose process mining problems. The decomposition approach is generic and can be combined with different existing process discovery and conformance checking techniques. It is possible to split computationally challenging process mining problems into many smaller problems that can be analyzed easily and whose results can be combined into solutions for the original problems.
Lecture Notes in Business Information Processing, 2013
Organizations of all sizes are currently supporting their performance on information systems that record the real execution of their business processes in event logs. Process mining tools analyze the log to provide insight on the real problems of the process, as part of the diagnostic phase. Nonetheless, to complete the lifecycle of a process, the latter has to be redesigned, a task for which simulation techniques can be used in combination with process mining, in order to evaluate different improvement alternatives before they are put in practice. In this context, the current work presents a methodological approach to the integration of process mining and simulation techniques in a process redesign project.
A Tour in Process Mining: From Practice to Algorithmic Challenges
Process mining seeks the confrontation between modeled behavior and observed behavior. In recent years, process mining techniques managed to bridge the gap between traditional model-based process analysis (e.g., simulation and other business process management techniques) and data-centric analysis techniques such as machine learning and data mining. Process mining is used by many data-driven organizations as a means to improve performance or to ensure compliance. Traditionally, the focus was on the discovery of process models from event logs describing real process executions. However, process mining is not limited to process discovery and also includes conformance checking. Process models (discovered or handmade) may deviate from reality. Therefore, we need powerful means to analyze discrepancies between models and logs. These are provided by conformance checking techniques that first align modeled and observed behavior, and then compare both. The resulting alignments are also used to enrich process models with performance related information extracted from the event log. This tutorial paper focuses on the control-flow perspective and describes a range of process discovery and conformance checking techniques. The goal of the paper is to show the algorithmic challenges in process mining. We will show that process mining provides a wealth of opportunities for people doing research on Petri nets and related models of concurrency.
Discovering Petri Nets From Event Logs
As information systems are becoming more and more intertwined with the operational processes they support, multitudes of events are recorded by todays information systems. The goal of process mining is to use such event data to extract process related information, e.g., to automatically discover a process model by observing events recorded by some system or to check the conformance of a given model by comparing it with reality. In this article, we focus on process discovery, i.e., extracting a process model from an event log. We focus on Petri nets as a representation language, because of the concurrent and unstructured nature of real-life processes. The goal is to introduce several approaches to discover Petri nets from event data (notably the α-algorithm, state-based regions, and language-based regions). Moreover, important requirements for process discovery are discussed. For example, process mining is only meaningful if one can deal with incompleteness (only a fraction of all possible behavior is observed) and noise (one would like to abstract from infrequent random behavior). These requirements reveal significant challenges for future research in this domain.
Top-Down Process Mining From Multi-Source Running Logs Based on Refinement of Petri Nets
IEEE Access, 2020
Today's information systems of enterprises are incredibly complex and typically composed of a large number of participants. Running logs are a valuable source of information about the actual execution of the distributed information systems. In this paper, a top-down process mining approach is proposed to construct the structural model for a complex workflow from its multi-source and heterogeneous logs collected from its distributed environment. The discovered top-level process model is represented by an extended Petri net with abstract transitions while the obtained bottom-level process models are represented using classical Petri nets. The Petri net refinement operation is used to integrate these models (both top-level and bottom-level ones) to an integrated one for the whole complex workflow. A multi-modal transportation business process is used as a typical case to display the proposed approach. By evaluating the discovered process model in terms of different quality metrics, we argue that the proposed approach is readily applicable for real-life business scenario. INDEX TERMS Workflow models, multi-source running log, distributed process mining, petri nets, refinement operation.
Discovering process models from empirical data
Effective information systems require the existence of explicit process models; a completely specified process design needs to be developed in order to enact a given business process. This development is time consuming and often subjective and incomplete. We propose a method that discovers the process model from process logs where process events are recorded as they have been executed over time. We induce a rule-set that predict causal, exclusive, and parallel relations between process events. The rule-set is induced from simulated process log data that are generated by varying process characteristics (e.g. noise, log size). Tests reveal that the induced rule-set has a high performance on new data. Knowing the causal, exclusive and parallel relations we can build the process model expressed in the Petri net formalism. We also evaluate the results using a real-world case study.
Workflow simulation for operational decision support using event graph through process mining
Decision Support Systems, 2012
Simulation is widely used as a tool for analyzing business processes but is mostly focused on examining abstract steady-state situations. Such analyses are helpful for the initial design of a business process but are less suitable for operational decision making and continuous improvement. Here we describe a simulation system for operational decision support in the context of workflow management. To do this we exploit not only the workflow's design, but also use logged data describing the system's observed historic behavior, and incorporate information extracted about the current state of the workflow. Making use of actual data capturing the current state and historic information allows our simulations to accurately predict potential near-future behaviors for different scenarios. The approach is supported by a practical toolset which combines and extends the workflow management system YAWL and the process mining framework ProM. Workflow & organizational model Event logs Workflow system records supports / controls Current state information models Simulation model specifies configures Simulation logs Simulation engine records simulates models Historic information Design information analyze Simulated process Real-world process specifies configures : Overview of our integrated workflow management (right) and simulation (left) system ganizational model. Note that the workflow and organizational models have been designed before enactment and are used for the configuration of the workflow system. During the enactment of the process, the performed activities are recorded in event logs. An event log records events related to the offering, start, and completion of work items, e.g., an event may be 'Mary completes the approval activity for insurance claim XY160598 at 16.05 on Monday 21-1-2008'. The right-hand side of is concerned with enactment using a workflow system while the left-hand side focuses on analysis using simulation. In order to link enactment and simulation we use three types of information readily available in workflow systems to create and initialize the simulation model.
Workflow mining: Discovering process models from event logs
IEEE Transactions on Knowledge and Data Engineering, 2003
Contemporary workflow management systems are driven by explicit process models, i.e., a completely specified workflow design is required in order to enact a given workflow process. Creating a workflow design is a complicated time-consuming process and, typically, there are discrepancies between the actual workflow processes and the processes as perceived by the management. Therefore, we have developed techniques for discovering workflow models. The starting point for such techniques is a so-called "workflow log" containing information about the workflow process as it is actually being executed. We present a new algorithm to extract a process model from such a log and represent it in terms of a Petri net. However, we will also demonstrate that it is not possible to discover arbitrary workflow processes. In this paper, we explore a class of workflow processes that can be discovered. We show that the -algorithm can successfully mine any workflow represented by a so-called SWF-net.