Model repair — aligning process models to reality

Process mining techniques relate observed behavior (i.e., event logs) to modeled behavior (e.g., a BPMN model or a Petri net). Process models can be discovered from event logs and conformance checking techniques can be used to detect and diagnose differences between observed and modeled behavior. Existing process mining techniques can only uncover these differences, but the actual repair of the model is left to the user and is not supported. In this paper we investigate the problem of repairing a process model w.r.t. a log such that the resulting model can replay the log (i.e., conforms to it) and is as similar as possible to the original model. To solve the problem, we use an existing conformance checker that aligns the runs of the given process model to the traces in the log. Based on this information, we decompose the log into several sublogs of non-fitting subtraces. For each sublog, either a loop is discovered that can replay the sublog or a subprocess is derived that is then added to the original model at the appropriate location. The approach is implemented in the process mining toolkit ProM and has been validated on logs and models from several Dutch municipalities.     

What makes a good process model?

There seems to be a never ending stream of new process modeling notations. Some of these notations are foundational and have been around for decades (e.g., Petri nets). Other notations are vendor specific, incremental, or are only popular for a short while. Discussions on the various competing notations concealed the more important question “What makes a good process model?”. Fortunately, large scale experiences with process mining allow us to address this question. Process mining techniques can be used to extract knowledge from event data, discover models, align logs and models, measure conformance, diagnose bottlenecks, and predict future events. Today’s processes leave many trails in data bases, audit trails, message logs, transaction logs, etc. Therefore, it makes sense to relate these event data to process models independent of their particular notation. Process models discovered based on the actual behavior tend to be very different from the process models made by humans. Moreover, conformance checking techniques often reveal important deviations between models and reality. The lessons that can be learned from process mining shed a new light on process model quality. This paper discusses the role of process models and lists seven problems related to process modeling. Based on our experiences in over 100 process mining projects, we discuss these problems. Moreover, we show that these problems can be addressed by exposing process models and modelers to event data.     

Conformance checking based on multi-perspective declarative process models

Process mining is a family of techniques that aim at analyzing business process execution data recorded in event logs. Conformance checking is a branch of this discipline embracing approaches for verifying whether the behavior of a process, as recorded in a log, is in line with some expected behavior provided in the form of a process model. Recently, techniques for conformance checking based on declarative specifications have been developed. Such specifications are suitable to describe processes characterized by high variability. However, an open challenge in the context of conformance checking with declarative models is the capability of supporting multi-perspective specifications. This means that declarative models used for conformance checking should not only describe the process behavior from the control flow point of view, but also from other perspectives like data or time. In this paper, we close this gap by presenting an approach for conformance checking based on MP-Declare, a multi-perspective version of the declarative process modeling language Declare. The approach has been implemented in the process mining tool ProM and has been experimented using artificial and real-life event logs.     

Formal Modeling and Discovery of Multi-instance Business Processes: A Cloud Resource Management Case Study

Process discovery, as one of the most challenging process analysis techniques, aims to uncover business process models from event logs. Many process discovery approaches were invented in the past twenty years; however, most of them have difficulties in handling multi-instance sub-processes. To address this challenge, we first introduce a multi-instance business process model (MBPM) to support the modeling of processes with multiple sub-process instantiations. Formal semantics of MBPMs are precisely defined by using multi-instance Petri nets (MPNs) that are an extension of Petri nets with distinguishable tokens. Then, a novel process discovery technique is developed to support the discovery of MBPMs from event logs with sub-process multi-instantiation information. In addition, we propose to measure the quality of the discovered MBPMs against the input event logs by transforming an MBPM to a classical Petri net such that existing quality metrics, e.g., fitness and precision, can be used. The proposed discovery approach is properly implemented as plugins in the ProM toolkit. Based on a cloud resource management case study, we compare our approach with the state-of-the-art process discovery techniques. The results demonstrate that our approach outperforms existing approaches to discover process models with multi-instance sub-processes.      

Single-Entry Single-Exit decomposed conformance checking

An exponential growth of event data can be witnessed across all industries. Devices connected to the internet (internet of things), social interaction, mobile computing, and cloud computing provide new sources of event data and this trend will continue. The omnipresence of large amounts of event data is an important enabler for process mining. Process mining techniques can be used to discover, monitor and improve real processes by extracting knowledge from observed behavior. However, unprecedented volumes of event data also provide new challenges and often state-of-the-art process mining techniques cannot cope. This paper focuses on “conformance checking in the large” and presents a novel decomposition technique that partitions larger process models and event logs into smaller parts that can be analyzed independently. The so-called Single-Entry Single-Exit (SESE) decomposition not only helps to speed up conformance checking, but also provides improved diagnostics. The analyst can zoom in on the problematic parts of the process. Importantly, the conditions under which the conformance of the whole can be assessed by verifying the conformance of the SESE parts are described, which enables the decomposition and distribution of large conformance checking problems. All the techniques have been implemented in ProM, and experimental results are provided.     

Exploring the CSCW spectrum using process mining

Process mining techniques allow for extracting information from event logs. For example, the audit trails of a workflow management system or the transaction logs of an enterprise resource planning system can be used to discover models describing processes, organizations, and products. Traditionally, process mining has been applied to structured processes. In this paper, we argue that process mining can also be applied to less structured processes supported by computer supported cooperative work (CSCW) systems. In addition, the ProM framework is described. Using ProM a wide variety of process mining activities are supported ranging from process discovery and verification to conformance checking and social network analysis.     

基于接口变迁的交互流程模型挖掘方法

流程模型挖掘是基于系统运行记录下的事件日志来还原特征对应流程模型的技术。目前已有的挖掘方法多是基于由系统分解出的不同模块之间交互频繁且模块包含特征较少的场景。在挖掘包含较多特征、交互不频繁的流程模型方面,目前的方法存在一定的局限性。鉴于此,文中提出了基于接口变迁的交互流程模型挖掘方法。首先,利用现有的挖掘方法来挖掘模块内部的特征序,确定初始模块网;其次,遍历事件日志以查找疑似接口变迁;然后,通过挖掘特征网来确定接口变迁,并对接口变迁增加接口库所;最后,基于开放Petri网,利用合成网的观点将交互模块合成为一个完善的流程模型Petri网。通过实例分析,验证了该挖掘方法的有效性。     

一种基于多视角数据属性的决策表Petri网挖掘方法

当处理高度可变的流程时,已有的自动过程挖掘技术产生的模型可能并不能真实反映流程运行中不同决策点之间规则的变化情况。从声明性过程挖掘的角度出发,提出了一种具备可视化规则的决策表Petri网挖掘方法,实现真实日志到声明性过程决策表Petri网模型的映射。首先,形式化了决策表Petri网模型及其携带的规则分析决策表,并对模型的静态语义和动态语义进行定义;其次,通过扩展属性的添加,分析流程内部属性和事件属性是否会对决策产生影响,并通过规则分析决策表的异常值属性,判断规则的异常程度;最后,在一组人工日志和真实事件日志的基础上进行实验仿真,并与数据Petri网的挖掘技术进行分析对比。实验结果表明所提方法在反映流程运行中规则的变化情况具有一定优势,并为数据流异常检测提供数值可解释性;同时,所设计的决策表Petri网挖掘方法可以将决策信息与模型结构整合在一起,为过程模型的可变性建模提供形式化基础。     

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.     

Process Discovery and Refinement of an Enterprise Management System

The need for the analysis of modern businesses is rapidly increasing as the supporting enterprise systems generate more and more data. This data can be extremely valuable for executing organizations because the data allows constant monitoring, analyzing, and improving the underlying processes, which leads to the reduction of cost and the improvement of the quality. Process mining is a useful technique for analyzing enterprise systems by using an event log that contains behaviours. This research focuses on the process discovery and refinement using real-life event log data collected from a large multinational organization that deals with coatings and paints. By investigating and analyzing their order handling processes, this study aims at learning a model that gives insight inspection of the processes and performance analysis. Furthermore, the animation is also performed for the better inspection, diagnostics, and compliance-related questions to specify the system. The configuration of the system and the conformance checking for further enhancement is also addressed in this research. To achieve the objectives, this research uses process mining techniques, i.e. process discovery in the form of formal Petri nets models with the help of process maps, and process refinement through conformance checking and enhancement. Initially, the identified executed process is reconstructed by using the process discovery techniques. Following the reconstruction, we perform a deep analysis for the underlying process to ensure the process improvement and redesigning. Finally, some recommendations are made to improve the enterprise management system processes.     

BPMN 2.0过程模型的语义和分析

BPMN 2.0已成为了建模业务过程事实上的标准。BPMN 2.0过程模型中建模元素的混用会产生控制流方面的语义错误。首先,建立了BPMN 2.0过程模型到工作流网的映射,并使用Petri网来形式定义过程模型的语义;其次,借助Petri网的分析技术,使用这种定义的语义对BPMN 2.0过程模型进行了合理性分析。实验结果表明,这种形式化可以识别BPMN 2.0过程模型中的语义错误。     

BPMN Miner: Automated discovery of BPMN process models with hierarchical structure

Existing techniques for automated discovery of process models from event logs generally produce flat process models. Thus, they fail to exploit the notion of subprocess as well as error handling and repetition constructs provided by contemporary process modeling notations, such as the Business Process Model and Notation (BPMN). This paper presents a technique, namely BPMN Miner, for automated discovery of hierarchical BPMN models containing interrupting and non-interrupting boundary events and activity markers. The technique employs approximate functional and inclusion dependency discovery techniques in order to elicit a process–subprocess hierarchy from the event log. Given this hierarchy and the projected logs associated to each node in the hierarchy, parent process and subprocess models are discovered using existing techniques for flat process model discovery. Finally, the resulting models and logs are heuristically analyzed in order to identify boundary events and markers. By employing approximate dependency discovery techniques, BPMN Miner is able to detect and filter out noise in the event log arising for example from data entry errors, missing event records or infrequent behavior. Noise is detected during the construction of the subprocess hierarchy and filtered out via heuristics at the lowest possible level of granularity in the hierarchy. A validation with one synthetic and two real-life logs shows that process models derived by the proposed technique are more accurate and less complex than those derived with flat process discovery techniques. Meanwhile, a validation on a family of synthetically generated logs shows that the technique is resilient to varying levels of noise.     

Conformance checking of processes based on monitoring real behavior

Many companies have adopted Process-aware Information Systems (PAIS) to support their business processes in some form. On the one hand these systems typically log events (e.g., in transaction logs or audit trails) related to the actual business process executions. On the other hand explicit process models describing how the business process should (or is expected to) be executed are frequently available. Together with the data recorded in the log, this situation raises the interesting question “Do the model and the log conform to each other?”. Conformance checking, also referred to as conformance analysis, aims at the detection of inconsistencies between a process model and its corresponding execution log, and their quantification by the formation of metrics. This paper proposes an incremental approach to check the conformance of a process model and an event log. First of all, the fitness between the log and the model is measured (i.e., “Does the observed process comply with the control flow specified by the process model?”). Second, the appropriateness of the model can be analyzed with respect to the log (i.e., “Does the model describe the observed process in a suitable way?”). Appropriateness can be evaluated from both a structural and a behavioral perspective. To operationalize the ideas presented in this paper a Conformance Checker has been implemented within the ProM framework, and it has been evaluated using artificial and real-life event logs.     

Process diagnostics using trace alignment: Opportunities, issues, and challenges

Business processes leave trails in a variety of data sources (e.g., audit trails, databases, and transaction logs). Hence, every process instance can be described by a trace, i.e., a sequence of events. Process mining techniques are able to extract knowledge from such traces and provide a welcome extension to the repertoire of business process analysis techniques. Recently, process mining techniques have been adopted in various commercial BPM systems (e.g., BPM|one, Futura Reflect, ARIS PPM, Fujitsu Interstage, Businesscape, Iontas PDF, and QPR PA). Unfortunately, traditional process discovery algorithms have problems dealing with less structured processes. The resulting models are difficult to comprehend or even misleading. Therefore, we propose a new approach based on trace alignment. The goal is to align traces in such a way that event logs can be explored easily. Trace alignment can be used to explore the process in the early stages of analysis and to answer specific questions in later stages of analysis. Hence, it complements existing process mining techniques focusing on discovery and conformance checking. The proposed techniques have been implemented as plugins in the ProM framework. We report the results of trace alignment on one synthetic and two real-life event logs, and show that trace alignment has significant promise in process diagnostic efforts.     

Matching events and activities by integrating behavioral aspects and label analysis

Nowadays, business processes are increasingly supported by IT services that produce massive amounts of event data during the execution of a process. These event data can be used to analyze the process using process mining techniques to discover the real process, measure conformance to a given process model, or to enhance existing models with performance information. Mapping the produced events to activities of a given process model is essential for conformance checking, annotation and understanding of process mining results. In order to accomplish this mapping with low manual effort, we developed a semi-automatic approach that maps events to activities using insights from behavioral analysis and label analysis. The approach extracts Declare constraints from both the log and the model to build matching constraints to efficiently reduce the number of possible mappings. These mappings are further reduced using techniques from natural language processing, which allow for a matching based on labels and external knowledge sources. The evaluation with synthetic and real-life data demonstrates the effectiveness of the approach and its robustness toward non-conforming execution logs.     

Semantics and analysis of business process models in BPMN

The Business Process Modelling Notation (BPMN) is a standard for capturing business processes in the early phases of systems development. The mix of constructs found in BPMN makes it possible to create models with semantic errors. Such errors are especially serious, because errors in the early phases of systems development are among the most costly and hardest to correct. The ability to statically check the semantic correctness of models is thus a desirable feature for modelling tools based on BPMN. Accordingly, this paper proposes a mapping from BPMN to a formal language, namely Petri nets, for which efficient analysis techniques are available. The proposed mapping has been implemented as a tool that, in conjunction with existing Petri net-based tools, enables the static analysis of BPMN models. The formalisation also led to the identification of deficiencies in the BPMN standard specification.     

A Survey of Petri Net Methods for Controlled Discrete Event Systems

This paper surveys recent research on the application of Petri net models to the analysis and synthesis of controllers for discrete event systems. Petri nets have been used extensively in applications such as automated manufacturing, and there exists a large body of tools for qualitative and quantitative analysis of Petri nets. The goal of Petri net research in discrete event systems is to exploit the structural properties of Petri net models in computationally efficient algorithms for computing controls. We present an overview of the various models and problems formulated in the literature focusing on two particular models, the controlled Petri nets and the labeled nets. We describe two basic approaches for controller synthesis, based on state feedback and event feedback. We also discuss two efficient techniques for the on-line computation of the control law, namely the linear integer programming approach which takes advantage of the linear structure of the Petri net state transition equation, and path-based algorithms which take advantage of the graphical structure of Petri net models. Extensions to timed models are briefly described. The paper concludes with a discussion of directions for future research.     

An alignment-based framework to check the conformance of declarative process models and to preprocess event-log data

Process mining can be seen as the “missing link” between data mining and business process management. The lion's share of process mining research has been devoted to the discovery of procedural process models from event logs. However, often there are predefined constraints that (partially) describe the normative or expected process, e.g., “activity A should be followed by B” or “activities A and B should never be both executed”. A collection of such constraints is called a declarative process model. Although it is possible to discover such models based on event data, this paper focuses on aligning event logs and predefined declarative process models. Discrepancies between log and model are mediated such that observed log traces are related to paths in the model. The resulting alignments provide sophisticated diagnostics that pinpoint where deviations occur and how severe they are. Moreover, selected parts of the declarative process model can be used to clean and repair the event log before applying other process mining techniques. Our alignment-based approach for preprocessing and conformance checking using declarative process models has been implemented in ProM and has been evaluated using both synthetic logs and real-life logs from a Dutch hospital.