Merging event logs for process mining: A rule based merging method and rule suggestion algorithm

In an inter-organizational setting the manual construction of process models is challenging because the different people involved have to put together their partial knowledge about the overall process. Process mining, an automated technique to discover and analyze process models, can facilitate the construction of inter-organizational process models. This paper presents a technique to merge the input data of the different partners of an inter-organizational process in order to serve as input for process mining algorithms. The technique consists of a method for configuring and executing the merge and an algorithm that searches for links between the data of the different partners and that suggests rules to the user on how to merge the data. Tool support is provided in the open source process mining framework ProM. The method and the algorithm are tested using two artificial and three real life datasets that confirm their effectiveness and efficiency.     

Bridging abstraction layers in process mining

While the maturity of process mining algorithms increases and more process mining tools enter the market, process mining projects still face the problem of different levels of abstraction when comparing events with modeled business activities. Current approaches for event log abstraction try to abstract from the events in an automated way that does not capture the required domain knowledge to fit business activities. This can lead to misinterpretation of discovered process models. We developed an approach that aims to abstract an event log to the same abstraction level that is needed by the business. We use domain knowledge extracted from existing process documentation to semi-automatically match events and activities. Our abstraction approach is able to deal with n:m relations between events and activities and also supports concurrency. We evaluated our approach in two case studies with a German IT outsourcing company.     

CPL+: An improved approach for evaluating the local completeness of event logs

Process mining aims at deriving order relations between tasks recorded by event logs in order to construct their corresponding process models. The quality of the results is not only determined by the mining algorithm being used, but also by the quality of the provided event logs. As a criterion of log quality, completeness measures the magnitude of information for process mining covered by an event log. In this paper, we focus on the evaluation of the local completeness of an event log. In particular, we consider the direct succession (DS) relations between the tasks of a business process. Based on our previous work, an improved approach called CPL+ is proposed in this paper. Experiments show that the proposed CPL+ works better than other approaches, on event logs that contain a small amount of traces. Finally, by further investigating CPL+, we also found that the more distinct DSs observed in an event log, the lower the local completeness of the log is.     

Mining business process variants: Challenges, scenarios, algorithms

During the last years a new generation of process-aware information systems has emerged, which enables process model configurations at buildtime as well as process instance changes during runtime. Respective model adaptations result in a large number of model variants that are derived from the same process model, but slightly differ in structure. Generally, such model variants are expensive to configure and maintain. In this paper we address two scenarios for learning from process model adaptations and for discovering a reference model out of which the variants can be configured with minimum efforts. The first one is characterized by a reference process model and a collection of related process variants. The goal is to improve the original reference process model such that it fits better to the variant models. The second scenario comprises a collection of process variants, while the original reference model is unknown; i.e., the goal is to “merge” these variants into a new reference process model. We suggest two algorithms that are applicable in both scenarios, but have their pros and cons. We provide a systematic comparison of the two algorithms and further contrast them with conventional process mining techniques. Comparison results indicate good performance of our algorithms and also show that specific techniques are needed for learning from process configurations and adaptations. Finally, we provide results from a case study in automotive industry in which we successfully applied our algorithms.     

基于拟间接依赖的过程模型挖掘方法

过程挖掘旨在从信息系统所记录的事件日志中挖掘出人们需要的且合理的过程模型,从而有助于改善或重建业务流程。以往的方法大多是根据任务间的直接依赖关系构建过程模型,具有很大的局限性。现存的过程挖掘方法中,虽然有能挖掘间接依赖的方法,其却没有从过程行为的角度进行分析。基于拟间接依赖的过程模型挖掘方法,把行为轮廓融入其中,依据行为轮廓建立初始模型;然后基于增量日志和拟间接依赖关系调整模型;最后根据评价标准选出最优模型。此方法特别适用于挖掘含有间接依赖的过程模型。     

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.     

Improving process models by discovering decision points

Workflow management systems (WfMS) are widely used by business enterprises as tools for administrating, automating and scheduling the business process activities with the available resources. Since the control flow specifications of workflows are manually designed, they entail assumptions and errors, leading to inaccurate workflow models. Decision points, the XOR nodes in a workflow graph model, determine the path chosen toward completion of any process invocation. In this work, we show that positioning the decision points at their earliest points can improve process efficiency by decreasing their uncertainties and identifying redundant activities. We present novel techniques to discover the earliest positions by analyzing workflow logs and to transform the model graph. The experimental results show that the transformed model is more efficient with respect to its average execution time and uncertainty, when compared to the original model.     

Controlled automated discovery of collections of business process models

Automated process discovery techniques aim at extracting process models from information system logs. Existing techniques in this space are effective when applied to relatively small or regular logs, but generate spaghetti-like and sometimes inaccurate models when confronted to logs with high variability. In previous work, trace clustering has been applied in an attempt to reduce the size and complexity of automatically discovered process models. The idea is to split the log into clusters and to discover one model per cluster. This leads to a collection of process models – each one representing a variant of the business process – as opposed to an all-encompassing model. Still, models produced in this way may exhibit unacceptably high complexity and low fitness. In this setting, this paper presents a two-way divide-and-conquer process discovery technique, wherein the discovered process models are split on the one hand by variants and on the other hand hierarchically using subprocess extraction. Splitting is performed in a controlled manner in order to achieve user-defined complexity or fitness thresholds. Experiments on real-life logs show that the technique produces collections of models substantially smaller than those extracted by applying existing trace clustering techniques, while allowing the user to control the fitness of the resulting models.     

A novel approach for process mining based on event types

Despite the omnipresence of event logs in transactional information systems (cf. WFM, ERP, CRM, SCM, and B2B systems), historic information is rarely used to analyze the underlying processes. Process mining aims at improving this by providing techniques and tools for discovering process, control, data, organizational, and social structures from event logs, i.e., the basic idea of process mining is to diagnose business processes by mining event logs for knowledge. Given its potential and challenges it is no surprise that recently process mining has become a vivid research area. In this paper, a novel approach for process mining based on two event types, i.e., START and COMPLETE, is proposed. Information about the start and completion of tasks can be used to explicitly detect parallelism. The algorithm presented in this paper overcomes some of the limitations of existing algorithms such as the α-algorithm (e.g., short-loops) and therefore enhances the applicability of process mining.

Web日志挖掘及其在校园网中的应用

如何有效地分析用户的需求,帮助用户从因特网的信息海洋中发现他们感兴趣的信息和资源,已经成为一项迫切而重要的课题。解决这些问题的一个途径,就是将传统的数据挖掘技术与Web结合起来,进行Web数据挖掘。其中的Web日志挖掘可以掌握用户在浏览站点时的行为,并且将挖掘出的用户访问模式应用于网站上,在改善Web站点的结构以及页面间的超链接结构,提高站点的服务质量等方面有重要的意义。     

Web日志挖掘及其在校园网中的应用

如何有效地分析用户的需求,帮助用户从因特网的信息海洋中发现他们感兴趣的信息和资源．已经成为一项迫切而重要的课题。解决这些问题的一个途径,就是将传统的数据挖掘技术与Web结合起来,进行Web数据挖掘。其中的Web日志挖掘可以掌握用户在浏览站点时的行为,并且将挖掘出的用户访问模式应用于网站上,在改善Web站点的结构以及页面间的超链接结构,提高站点的服务质量等方面有重要的意义。     

Stage-based discovery of business process models from event logs

An automated process discovery technique generates a process model from an event log recording the execution of a business process. For it to be useful, the generated process model should be as simple as possible, while accurately capturing the behavior recorded in, and implied by, the event log. Most existing automated process discovery techniques generate flat process models. When confronted to large event logs, these approaches lead to overly complex or inaccurate process models. An alternative is to apply a divide-and-conquer approach by decomposing the process into stages and discovering one model per stage. It turns out, however, that existing divide-and-conquer process discovery approaches often produce less accurate models than flat discovery techniques, when applied to real-life event logs. This article proposes an automated method to identify business process stages from an event log and an automated technique to discover process models based on a given stage-based process decomposition. An experimental evaluation shows that: (i) relative to existing automated process decomposition methods in the field of process mining, the proposed method leads to stage-based decompositions that are closer to decompositions derived by human experts; and (ii) the proposed stage-based process discovery technique outperforms existing flat and divide-and-conquer discovery techniques with respect to well-accepted measures of accuracy and achieves comparable results in terms of model complexity.     

Retrieving the resource availability calendars of a process from an event log

Knowing the availability of human resources for a business process is required, e.g., when allocating resources to work items, or when analyzing the process using a simulation model. In this respect, it should be taken into account that staff members are not permanently available and that they can be involved in multiple processes within the company. Consequently, it is far from trivial to specify their availability for the single process from, e.g., generic timetables. To this end, this paper presents a new method to automatically retrieve resource availability calendars from event logs containing process execution information. The retrieved resource availability calendars are the first to take into account (i) the temporal dimension of availability, i.e. the time of day at which a resource is available, and (ii) intermediate availability interruptions (e.g. due to a break). Empirical evaluation using synthetic data shows that the method’s key outputs closely resemble their equivalents in reality.     

流程增量挖掘中的模型更新方法

正确发现流程实际运作情况对工作流管理有着重要的意义.流程挖掘抽取系统日志信息,挖掘流程的真实运作模型.目前很多该方面的研究,着重于从一份日志中挖掘出工作流模型.然而,这些挖掘方法只关注日志信息,忽略了流程设计者的先验知识.而且,日志所包含信息量较大,进行一次挖掘耗费较大.因此,希望能结合已有工作流模型及新增日志信息,更新工作流模型.已有研究给出对模型及日志的增量挖掘算法.但是,业务流程会随着时间推移变更,可能已有的任务被取消了,因此在新增的一段日志中该任务没被记录.但由于该任务曾经在已有日志中记录下来,故应用已有挖掘算法或增量挖掘算法,在更新模型中,该任务也会被挖掘出来.提出了一种增量挖掘模型更新的改进算法.通过流程设计者的先验知识及统计任务出现的频率,判断该任务是否被取消.最后给出一个实验,验证算法的可行性.     

Workflow simulation for operational decision support using event graph through process mining

It is increasingly common to see computer-based simulation being used as a vehicle to model and analyze business processes in relation to process management and improvement. While there are a number of business process management (BPM) and business process simulation (BPS) methodologies, approaches and tools available, it is more desirable to have a systemic BPS approach for operational decision support, from constructing process models based on historical data to simulating processes for typical and common problems. In this paper, we have proposed a generic approach of BPS for operational decision support which includes business processes modeling and workflow simulation with the models generated. Processes are modeled with event graphs through process mining from workflow logs that have integrated comprehensive information about the control-flow, data and resource aspects of a business process. A case study of a credit card application is presented to illustrate the steps involved in constructing an event graph. The evaluation detail is also given in terms of precision, generalization and robustness. Based on the event graph model constructed, we simulate the process under different scenarios and analyze the simulation logs for three generic problems in the case study: 1) suitable resource allocation plan for different case arrival rates; 2) teamwork performance under different case arrival rates; and 3) evaluation and prediction for personal performances. Our experimental results show that the proposed approach is able to model business processes using event graphs and simulate the processes for common operational decision support which collectively play an important role in process management and improvement.     

Simplifying discovered process models in a controlled manner

Process models discovered from a process log using process mining tend to be complex and have problems balancing between overfitting and underfitting. An overfitting model allows for too little behavior as it just permits the traces in the log and no other trace. An underfitting model allows for too much behavior as it permits traces that are significantly different from the behavior seen in the log. This paper presents a post-processing approach to simplify discovered process models while controlling the balance between overfitting and underfitting. The discovered process model, expressed in terms of a Petri net, is unfolded into a branching process using the event log. Subsequently, the resulting branching process is folded into a simpler process model capturing the desired behavior.     

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.     

基于数据块的关系数据库日志挖掘技术

在关系型数据库中数据库通过Redo日志来实现事物的快速提交,并记录事物的操作过程与操作内容.通过对Redo日志的分析与变化数据内容的捕获,将变化数据传送到灾备端,并在灾备端实现变化数据的写入,是目前数据库复制最主要实现原理.本文分析了oralce数据库Redo日志文件结构,阐述了日志文件头标志位信息.结合Redo 日志文件头定位分析技术,给出了一种基于数据块的数据库Redo日志挖掘算法.通过测试分析,验证了该Redo日志挖掘技术的可行性与可靠性.最后展望了下一步的研究方向.