一种并行化的启发式流程挖掘算法

启发式流程挖掘算法在日志噪音与不完备日志的处理方面优势显著,但是现有算法对长距离依赖关系以及2-循环特殊结构的处理存在不足,而且算法未进行并行化处理.针对上述问题,基于执行任务集将流程模型划分为多个案例模型,结合改进的启发式算法并行挖掘各个案例模型所对应的C-net模型;再将上述模型集成得到完整流程对应的C-net.同时,将长距离依赖关系扩展为决策点处两个任务子集之间的非局部依赖关系,给出了更为准确的长距离依赖关系度量指标和挖掘算法.上述改进措施使得该算法更为精确、高效.     

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.

Genetic process mining: an experimental evaluation

One of the aims of process mining is to retrieve a process model from an event log. The discovered models can be used as objective starting points during the deployment of process-aware information systems (Dumas et al., eds., Process-Aware Information Systems: Bridging People and Software Through Process Technology. Wiley, New York, 2005) and/or as a feedback mechanism to check prescribed models against enacted ones. However, current techniques have problems when mining processes that contain non-trivial constructs and/or when dealing with the presence of noise in the logs. Most of the problems happen because many current techniques are based on local information in the event log. To overcome these problems, we try to use genetic algorithms to mine process models. The main motivation is to benefit from the global search performed by this kind of algorithms. The non-trivial constructs are tackled by choosing an internal representation that supports them. The problem of noise is naturally tackled by the genetic algorithm because, per definition, these algorithms are robust to noise. The main challenge in a genetic approach is the definition of a good fitness measure because it guides the global search performed by the genetic algorithm. This paper explains how the genetic algorithm works. Experiments with synthetic and real-life logs show that the fitness measure indeed leads to the mining of process models that are complete (can reproduce all the behavior in the log) and precise (do not allow for extra behavior that cannot be derived from the event log). The genetic algorithm is implemented as a plug-in in the ProM framework.     

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.     

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.

Manufacturing feature recognition toward integration with processplanning

Process planning plays a key role by linking CAD and CAM. Its front-end is feature recognition, but feature recognition research has not been in accord with the requirements of process planning. This paper presents an effort for integrating the two activities: feature-based machining sequence generation primarily based on tool capabilities. The system recognizes only manufacturable features by consulting the tool database, and simultaneously constructs dependencies among the features. Then, the A* algorithm is used to search for an optimal machining sequence by the aid of the feature dependencies and a manufacturing cost function.     

Mining functional dependencies from data

In this paper, we propose an efficient rule discovery algorithm, called FD_Mine, for mining functional dependencies from data. By exploiting Armstrong’s Axioms for functional dependencies, we identify equivalences among attributes, which can be used to reduce both the size of the dataset and the number of functional dependencies to be checked. We first describe four effective pruning rules that reduce the size of the search space. In particular, the number of functional dependencies to be checked is reduced by skipping the search for FDs that are logically implied by already discovered FDs. Then, we present the FD_Mine algorithm, which incorporates the four pruning rules into the mining process. We prove the correctness of FD_Mine, that is, we show that the pruning does not lead to the loss of useful information. We report the results of a series of experiments. These experiments show that the proposed algorithm is effective on 15 UCI datasets and synthetic data.     

From event logs to goals: a systematic literature review of goal-oriented process mining

Process mining helps infer valuable insights about business processes using event logs, whereas goal modeling focuses on the representation and analysis of competing goals of stakeholders and systems. Although there are clear benefits in mining the goals of existing processes, goal-oriented approaches that consider logs during model construction are still rare. Process mining techniques, when generalizing large instance-level data into process models, can be considered as a data-driven complement to use case/scenario elicitation. Requirements engineers can exploit process mining techniques to find new system or process requirements in order to align current practices and desired ones. This paper provides a systemic literature review, based on 24 papers rigorously selected from four popular search engines in 2018, to assess the state of goal-oriented process mining. Through two research questions, the review highlights that the use of process mining in association with goals does not yet have a coherent line of research, whereas intention mining (where goal models are mined) shows a meaningful trace of research. Research about performance indicators measuring goals associated with process mining is also sparse. Although the number of publications in process mining and goal modeling is trending up, goal mining and goal-oriented process mining remain modest research areas. Yet, synergetic effects achievable by combining goals and process mining can potentially augment the precision, rationality and interpretability of mined models and eventually improve opportunities to satisfy system stakeholders.

     

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。     

一种基于禁忌搜索算法的流程挖掘方法

为了满足高效率的自动化生产需要,支持流程控制的工作流管理系统 的应用越来越广泛。流程挖掘可以使用事件日志等历史数据生成抽象流程模型,为工作流系统的部署提供有利条件。首先总结归纳了一种较通用的基于启发式优化算法的流程挖掘框架;然后依照该流程挖掘框架将禁忌搜索算法用于流程挖掘领域,针对禁忌搜索中程序初始化、邻域构建方法和禁忌表构造等几个关键问题进行了详细阐述和论证;最后将算法实现为ProM的插件并进行了对比实验。实验验证了该流程挖掘框架的正确性,表明了禁忌搜索流程挖掘方法对不同流程结构具有良好支持,对数据噪声具有较强的鲁棒性和更少的时间消耗。     

Mining Loosely Structured Motifs from Biological Data

The discovery of information encoded in biological sequences is assuming a prominent role in identifying genetic diseases and in deciphering biological mechanisms. This information is usually encoded in patterns frequently occurring in the sequences, also called motifs. In fact, motif discovery has received much attention in the literature, and several algorithms have already been proposed, which are specifically tailored to deal with motifs exhibiting some kinds of "regular structure". Motivated by biological observations, this paper focuses on the mining of loosely structured motifs, i.e., of more general kinds of motif where several "exceptions" may be tolerated in pattern repetitions. To this end, an algorithm exploiting data structures conceived to efficiently handle pattern variabilities is presented and analyzed. Furthermore, a randomized variant with linear time and space complexity is introduced, and a theoretical guarantee on its performances is proven. Both algorithms have been implemented and tested on real data sets. Despite the ability of mining very complex kinds of pattern, performance results evidence a genome-wide applicability of the proposed techniques.     

Location-Oblivious Distributed Unit Disk Graph Coloring

We present the first location-oblivious distributed unit disk graph coloring algorithm having a provable performance ratio of three (i.e. the number of colors used by the algorithm is at most three times the chromatic number of the graph). This is an improvement over the standard sequential coloring algorithm that has a worst case lower bound on its performance ratio of 4−3/k (for any k>2, where k is the chromatic number of the unit disk graph achieving the lower bound) (Tsai et al., in Inf. Process. Lett. 84(4):195–199, 2002). We present a slightly better worst case lower bound on the performance ratio of the sequential coloring algorithm for unit disk graphs with chromatic number 4. Using simulation, we compare our algorithm with other existing unit disk graph coloring algorithms.     

Toward objective software process information: experiences from a case study

A critical problem in software development is the monitoring, control and improvement in the processes of software developers. Software processes are often not explicitly modeled, and manuals to support the development work contain abstract guidelines and procedures. Consequently, there are huge differences between ‘actual’ and ‘official’ processes: “the actual process is what you do, with all its omissions, mistakes, and oversights. The official process is what the book, i.e., a quality manual, says you are supposed to do” (Humphrey in A discipline for software engineering. Addison-Wesley, New York, 1995). Software developers lack support to identify, analyze and better understand their processes. Consequently, process improvements are often not based on an in-depth understanding of the ‘actual’ processes, but on organization-wide improvement programs or ad hoc initiatives of individual developers. In this paper, we show that, based on particular data from software development projects, the underlying software development processes can be extracted and that automatically more realistic process models can be constructed. This is called software process mining (Rubin et al. in Process mining framework for software processes. Software process dynamics and agility. Springer Berlin, Heidelberg, 2007). The goal of process mining is to better understand the development processes, to compare constructed process models with the ‘official’ guidelines and procedures in quality manuals and, subsequently, to improve development processes. This paper reports on process mining case studies in a large industrial company in The Netherlands. The subject of the process mining is a particular process: the change control board (CCB) process. The results of process mining are fed back to practice in order to subsequently improve the CCB process.     

基于重复任务的复杂结构挖掘

过程挖掘对于部署新的商业流程以及审计、分析和改进已有的流程是非常有帮助的。在商业流程系统日志中,同名任务和重复任务是大量存在的。现有的挖掘算法都不能很好地区分,这导致在过程挖掘的结果中往往会产生不准确的流程模型。为了提高过程挖掘的准确性,提出了一种改进方法,它不仅能够挖掘日志中的循环结构、非自由选择结构等复杂结构,还能够挖掘日志中的同名任务和重复任务。     

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。     

Process mining: a two-step approach to balance between underfitting and overfitting

Process mining includes the automated discovery of processes from event logs. Based on observed events (e.g., activities being executed or messages being exchanged) a process model is constructed. One of the essential problems in process mining is that one cannot assume to have seen all possible behavior. At best, one has seen a representative subset. Therefore, classical synthesis techniques are not suitable as they aim at finding a model that is able to exactly reproduce the log. Existing process mining techniques try to avoid such “overfitting” by generalizing the model to allow for more behavior. This generalization is often driven by the representation language and very crude assumptions about completeness. As a result, parts of the model are “overfitting” (allow only for what has actually been observed) while other parts may be “underfitting” (allow for much more behavior without strong support for it). None of the existing techniques enables the user to control the balance between “overfitting” and “underfitting”. To address this, we propose a two-step approach. First, using a configurable approach, a transition system is constructed. Then, using the “theory of regions”, the model is synthesized. The approach has been implemented in the context of ProM and overcomes many of the limitations of traditional approaches.     

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.     

Process Algebra Having Inherent Choice: Revised Semantics for Concurrent Systems

Process algebras are standard formalisms for compositionally describing systems by the dependencies of their observable synchronous communication. In concurrent systems, parallel composition introduces resolvable nondeterminism, i.e., nondeterminism that will be resolved in later design phases or by the operating system. Sometimes it is also important to express inherent nondeterminism for equal (communication) labels.Here, we give operational and axiomatic semantics to a process algebra having a parallel operator interpreted as concurrent and having a choice operator interpreted as inherent, not only w.r.t. different, but also w.r.t. equal next-step actions. In order to handle the different kinds of nondeterminism, the operational semantics uses μ-automata as underlying semantical model. Soundness and completeness of our axiom system w.r.t. the operational semantics is shown.     

韩明峰 李传林

本文在分析已有裁剪算法优、缺点的基础上提出了一个新算法。该算法通过定义多边形的顶点类型、交点类型、裁剪算法对各种情况都快速、正确地得到结束。     

业务流程的时延预测队列挖掘方法

过程挖掘是针对流程信息系统所记录下的日志进行分析,将业务流程真实过程还原的技术。目前已有的方法多是基于控制流与数据流的观点,针对任务运行状态的,无时延的业务过程进行挖掘。但在挖掘存在多任务的有时延的业务进程方面,目前的方法存在一定局限性。提出基于队列挖掘优化过程模型的方法,首先利用现有的基于过程挖掘的方法,挖掘业务流程的初始模型。再运用队列挖掘的观点对特定的顾客进行时延预测,挖掘出顾客的行为信息,以此对初始流程模型进行优化。最后通过实例验证了所提出的优化挖掘方法的有效性,优化后的流程模型不仅对事件日志有很好的重放效果,并且能够反应出多类别的,且存在时延的业务流程中任务的行为信息。