排序方式: 共有73条查询结果,搜索用时 156 毫秒
1.
Sometimes historic information about workflow execution is needed to analyze business processes. Process mining aims at extracting information from event logs for capturing a business process in execution. In this paper a process mining algorithm is proposed based on Synchro-Net which is a synchronization-based model of workflow logic and workflow semantics. With this mining algorithm based on the model, problems such as invisible tasks and short-loops can be dealt with at ease. A process mining example is presented to illustrate the algorithm, and the evaluation is also given. 相似文献
2.
Mining process models with non-free-choice constructs 总被引:5,自引:0,他引:5
Lijie Wen Wil M. P. van der Aalst Jianmin Wang Jiaguang Sun 《Data mining and knowledge discovery》2007,15(2):145-180
Process mining aims at extracting information from event logs to capture the business process as it is being executed. Process
mining is particularly useful in situations where events are recorded but there is no system enforcing people to work in a
particular way. Consider for example a hospital where the diagnosis and treatment activities are recorded in the hospital
information system, but where health-care professionals determine the “careflow.” Many process mining approaches have been
proposed in recent years. However, in spite of many researchers’ persistent efforts, there are still several challenging problems
to be solved. In this paper, we focus on mining non-free-choice constructs, i.e., situations where there is a mixture of choice
and synchronization. Although most real-life processes exhibit non-free-choice behavior, existing algorithms are unable to
adequately deal with such constructs. Using a Petri-net-based representation, we will show that there are two kinds of causal
dependencies between tasks, i.e., explicit and implicit ones. We propose an algorithm that is able to deal with both kinds
of dependencies. The algorithm has been implemented in the ProM framework and experimental results shows that the algorithm
indeed significantly improves existing process mining techniques. 相似文献
3.
Discovering Social Networks from Event Logs 总被引:4,自引:0,他引:4
Wil M. P. van der Aalst Hajo A. Reijers Minseok Song 《Computer Supported Cooperative Work (CSCW)》2005,14(6):549-593
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. 相似文献
4.
Genetic process mining: an experimental evaluation 总被引:4,自引:0,他引:4
A. K. A. de Medeiros A. J. M. M. Weijters W. M. P. van der Aalst 《Data mining and knowledge discovery》2007,14(2):245-304
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. 相似文献
5.
基于过程挖掘的工作流性能分析 总被引:3,自引:0,他引:3
介绍了工作流性能的分析基础和概念。针对复杂和具有非确定性的业务流程,通过基于工作流日志的工作流过程挖掘算法,得到反映系统基本性能的工作流性能分析网。并应用到具有动态、模糊控制流程的工作流系统的性能分析中。 相似文献
6.
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. 相似文献
7.
8.
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. 相似文献
9.
Workflow simulation for operational decision support 总被引:1,自引:0,他引:1
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. 相似文献
10.
A novel approach for process mining based on event types 总被引:1,自引:0,他引:1
Lijie Wen Jianmin Wang Wil M. P. van der Aalst Biqing Huang Jiaguang Sun 《Journal of Intelligent Information Systems》2009,32(2):163-190
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.
相似文献
Jiaguang SunEmail: |