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.     

Process mining using BPMN: relating event logs and process models

Process-aware information systems (PAIS) are systems relying on processes, which involve human and software resources to achieve concrete goals. There is a need to develop approaches for modeling, analysis, improvement and monitoring processes within PAIS. These approaches include process mining techniques used to discover process models from event logs, find log and model deviations, and analyze performance characteristics of processes. The representational bias (a way to model processes) plays an important role in process mining. The BPMN 2.0 (Business Process Model and Notation) standard is widely used and allows to build conventional and understandable process models. In addition to the flat control flow perspective, subprocesses, data flows, resources can be integrated within one BPMN diagram. This makes BPMN very attractive for both process miners and business users, since the control flow perspective can be integrated with data and resource perspectives discovered from event logs. In this paper, we describe and justify robust control flow conversion algorithms, which provide the basis for more advanced BPMN-based discovery and conformance checking algorithms. Thus, on the basis of these conversion algorithms low-level models (such as Petri nets, causal nets and process trees) discovered from event logs using existing approaches can be represented in terms of BPMN. Moreover, we establish behavioral relations between Petri nets and BPMN models and use them to adopt existing conformance checking and performance analysis techniques in order to visualize conformance and performance information within a BPMN diagram. We believe that the results presented in this paper can be used for a wide variety of BPMN mining and conformance checking algorithms. We also provide metrics for the processes discovered before and after the conversion to BPMN structures. Cases for which conversion algorithms produce more compact or more complicated BPMN models in comparison with the initial models are identified.     

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.     

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.     

Discovering Social Networks from Event Logs

Process mining techniques allow for the discovery of knowledge based on so-called “event logs”, i.e., a log recording the execution of activities in some business process. Many information systems provide such logs, e.g., most WFM, ERP, CRM, SCM, and B2B systems record transactions in a systematic way. Process mining techniques typically focus on performance and control-flow issues. However, event logs typically also log the performer, e.g., the person initiating or completing some activity. This paper focuses on mining social networks using this information. For example, it is possible to build a social network based on the hand-over of work from one performer to the next. By combining concepts from workflow management and social network analysis, it is possible to discover and analyze social networks. This paper defines metrics, presents a tool, and applies these to a real event log within the setting of a large Dutch organization.     

基于混合人工免疫算法的流程挖掘事件日志融合方法

流程挖掘是流程管理和数据挖掘交叉领域中的一个研究热点.在实际业务环境中,流程执行的数据往往分散记录到不同的事件日志中,需要将这些事件日志融合成为单一事件日志文件,才能应用当前基于单一事件日志的流程挖掘技术.然而,由于流程日志间存在着执行实例的多对多匹配关系、融合所需信息可能缺失等问题,导致事件日志融合问题具有较高挑战性.本文对事件日志融合问题进行了形式化定义,指出该问题是一个搜索优化问题,并提出了一种基于混合人工免疫算法的事件日志融合方法：以启发式方法生成初始种群,人工免疫系统的克隆选择理论基础,通过免疫进化获得“最佳”的融合解,从而支持包含多对多的实例匹配关系的日志融合;考虑两个实例级别的因素：流程执行路径出现的频次和流程实例间的时间匹配关系,分别从“量”匹配和“时间”匹配两个维度来评价进化中的个体;通过设置免疫记忆库、引入模拟退火机制,保证新一代种群的多样性,减少进化早熟几率.实验结果表明,本文的方法能够实现多对多的实例匹配关系的事件日志融合的目标,相比随机方法生成初始种群,启发式方法能加快免疫进化的速度.文中还针对利用分布式技术提高事件日志融合性能,探讨了大规模事件日志的分布式融合中的数据划问题.     

融合特征网与模块网的业务过程挖掘

过程挖掘目的是通过分析由信息系统记录的日志得出的过程模型,从而改善和维护业务流程。目前,许多业务流程都以模块化的方式进行交互。虽然很多过程挖掘算法已经被提出来,不过对于处理多模块还有一定的局限性。提出了基于特征网与模块网的挖掘算法,根据日志将特征分为不同模块;在此基础上,分别求出模块间特征交互的特征网与模块内的特征交互模块网;将两者根据提出的融合算法进行融合,得到完整的过程模型。通过一个用户网上购物的实例说明了该算法的可行性。     

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.     

Scalable process discovery and conformance checking

Considerable amounts of data, including process events, are collected and stored by organisations nowadays. Discovering a process model from such event data and verification of the quality of discovered models are important steps in process mining. Many discovery techniques have been proposed, but none of them combines scalability with strong quality guarantees. We would like such techniques to handle billions of events or thousands of activities, to produce sound models (without deadlocks and other anomalies), and to guarantee that the underlying process can be rediscovered when sufficient information is available. In this paper, we introduce a framework for process discovery that ensures these properties while passing over the log only once and introduce three algorithms using the framework. To measure the quality of discovered models for such large logs, we introduce a model–model and model–log comparison framework that applies a divide-and-conquer strategy to measure recall, fitness, and precision. We experimentally show that these discovery and measuring techniques sacrifice little compared to other algorithms, while gaining the ability to cope with event logs of 100,000,000 traces and processes of 10,000 activities on a standard computer.     

基于时间差约束的事件日志合并

在跨企业、跨系统的环境中,流程数据通常记录在单独的事件日志中,这使得无法挖掘完整的端到端的执行流程,因此本算法提出仅使用事件名称以及时间戳属性对日志进行合并。首先分别获取两个系统的过程模型以及根据活动的跨系统跟随依赖关系获得的合并模型,接着将两个系统的流程一对一进行合并并按照时间戳排序,留下与合并模型路径一致的合并流程,然后从这些流程中获得一对一的实例对,即唯一主流程仅与唯一子流程可以合并,再从这些实例对中挖掘活动间的时间约束用于剩余日志的合并,重复最后两步直到所有日志均合并或无法一对一合并日志。该算法在真实的事件日志上进行了实验,达到了满意的合并效果并获得较高的准确率与召回率。     

基于拟间接依赖的流程挖掘优化分析

业务流程挖掘旨在从记录的事件日志中挖掘出满足人们需求的流程模型。以往的方法多是根据事件之间的直接依赖关系建立流程模型,具有一定的局限性,提出了基于拟间接依赖的流程挖掘优化分析方法。依据事件日志,以行为轮廓为基础,构建初始模型。在执行日志下,通过基于整数线性规划流程发现算法的基本约束体查找出具有拟间接依赖关系的变迁对,并对模型进行完善,挖掘出优化模型。通过具体的实例分析验证了该方法的有效性。     

流程树切挖掘业务流程隐变迁的方法

隐变迁是指存在于事件日志中的不频繁行为,从流程模型中挖掘出隐变迁,提高流程运行效率和服务质量显得尤为重要。已有的方法大部分基于业务流程序列进行分析,但很少考虑跨序列间的关系,因此对挖掘业务流程隐变迁有一定的影响。提出流程树切挖掘业务流程隐变迁的方法,首先根据发生频数较高的日志序列得到初始模型,再根据流程树切预处理事件日志,把日志活动关系与初始模型关系进行对比,找到存在变化的区域,挖掘可能存在的隐变迁,通过评价指标判定带隐变迁的模型是最优模型,最后实例分析验证该方法的有效性。     

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。     

Business alignment: using process mining as a tool for Delta analysis and conformance testing

Increasingly, business processes are being controlled and/or monitored by information systems. As a result, many business processes leave their “footprints” in transactional information systems, i.e., business events are recorded in so-called event logs. 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. In this paper we focus on the potential use of process mining for measuring business alignment, i.e., comparing the real behavior of an information system or its users with the intended or expected behavior. We identify two ways to create and/or maintain the fit between business processes and supporting information systems: Delta analysis and conformance testing. Delta analysis compares the discovered model (i.e., an abstraction derived from the actual process) with some predefined processes model (e.g., the workflow model or reference model used to configure the system). Conformance testing attempts to quantify the “fit” between the event log and some predefined processes model. In this paper, we show that Delta analysis and conformance testing can be used to analyze business alignment as long as the actual events are logged and users have some control over the process.

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.     

On process model synthesis based on event logs with noise

Process mining is a new emerging discipline related to process management, formal process modelling, and data mining. One of the main tasks of process mining is model synthesis (discovery) based on event logs. A wide range of algorithms for process model discovery, analysis, and enhancement is developed. The real-life event logs often contain noise of different types. In this paper, we describe the main causes of noise in the event logs and study the effect of noise on the performance of process discovery algorithms. The experimental results of application of the main process discovery algorithms to artificial event logs with noise are provided. Specially generated event logs with noise of different types were processed using the four basic discovery techniques. Although modern algorithms can cope with some types of noise, in most cases, their use does not lead to obtaining a satisfactory result. Thus, there is a need for more sophisticated algorithms to deal with noise of different types.     

事件日志的批量迹与过程模型的多视角对齐方法

在业务过程发现的一致性检测中,现有事件日志与过程模型的多视角对齐方法一次只能获得一条迹与过程模型的最优对齐;并且最优对齐求解中的启发函数计算复杂,以致最优对齐的计算效率较低。为此,提出一种基于迹最小编辑距离的、事件日志的批量迹与过程模型的多视角对齐方法。首先选取事件日志中的多条迹组成批量迹,使用过程挖掘算法得到批量迹的日志模型;进而获取日志模型与过程模型的乘积模型及其变迁系统,即为批量迹的搜索空间;然后设计基于Petri网变迁序列集合与剩余迹的最小编辑距离的启发函数来加快A*算法;最后设计可调节数据和资源视角所占权重的多视角代价函数,在乘积模型的变迁系统上提出批量迹中每条迹与过程模型的多视角最优对齐方法。仿真实验结果表明,相比已有工作,在计算批量迹与过程模型间的多视角对齐时,所提方法占用更少的内存空间和使用更少的运行时间。该方法提高了最优对齐的启发函数计算速度,可以一次获得批量迹的所有最优对齐,进而提高了事件日志与过程模型的多视角对齐效率。     

数据挖掘在安全管理中心的应用

提出了一个基于日志挖掘的、分布式、多协议支持的企业安全管理中心框架，介绍了其组成和实现。描述了利用数据挖掘技术产生检测模型的过程。通过对各种日志信息进行采集、规整和汇集，生成统一的通告事件，利用检测模型进行分析，从而发现系统中的潜在威胁和攻击，采取实时应对措施。