ScalableMine: Scalable Hierarchical Process Mining in Event-Stream Systems

ScalableMine：事件流系统中的可扩展分层流程挖掘

• 批准号: 521430520
• 负责人: Professor Dr. Wilhelm Hasselbring
• 金额: --
• 依托单位: Institut für Informatik
• 依托单位国家: 德国
• 项目类别: Research Units
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/521430520?language=en
• 关键词: ScalableMine; Scalable; Hierarchical; Process; Mining

Process mining challenges include scalability, i.e., dealing with volume, velocity and variability of input data, especially in online settings using event streams. Online means in this context that the events are processed immediately as they arrive in a continuous stream. Scaling process mining is required to establish process mining as a continuous company-wide activity, not to be seen as a single project. Thus, scalable processing of continuous event streams and process fragments on cloud infrastructures is required for efficient and effective streaming process mining. Scalability is the ability of a software system to sustain increasing workloads with adequate performance provided that hardware resources are added. When considering continuous streams of events, often an integration of multiple such streams is required. With the traditional approach to process mining of first writing all events to a (relational) database and then querying this integrated database, processing the events is straightforward. Processing continuous event streams raises new challenges. This is in particular the case when requirements for scalability have to be considered, as we intend to address with ScalableMine. With ScalableMine, we contribute to the seminal work on streaming process mining by designing and benchmarking scalable event processing algorithms and architectures to aggregate events of multiple event streams online for process mining in (near) real time. Streaming process mining has to be scalable to cope with the high volume and velocity of events from distributed sources. With ScalableMine, new algorithms and architectures for scalable streaming process mining will be designed and investigated. Representative, specific benchmarks are missing for systematically evaluating the scalability of approaches to streaming process mining. With ScalableMine, such benchmarks will be designed, implemented, evaluated and published for the community. The synergies of SOURCED make it possible to address the challenges of scalability in the context of process mining appropriately. For example, abstraction models for distributed streaming process mining will support benchmarking online data aggregation. Resource-aware process mining algorithms on edge and cloud infrastructures will support edge vs. cloud scalability benchmarking. The TinyWorkPlace House will provide realistic load profiles for benchmarking.

流程挖掘的挑战包括可扩展性，即，处理输入数据的数量、速度和可变性，特别是在使用事件流的在线设置中。在线在此上下文中意味着事件在连续流中到达时立即处理。扩展流程挖掘需要将流程挖掘建立为持续的公司范围内的活动，而不是被视为一个单一的项目。因此，云基础设施上的连续事件流和流程片段的可扩展处理是高效且有效的流式流程挖掘所必需的。可伸缩性是软件系统在增加硬件资源的情况下以足够的性能承受不断增加的工作负载的能力。当考虑连续的事件流时，通常需要多个这样的流的集成。对于流程挖掘的传统方法，首先将所有事件写入（关系）数据库，然后查询此集成数据库，处理事件非常简单。处理连续事件流提出了新的挑战。当必须考虑可伸缩性的需求时，情况尤其如此，因为我们打算用ScalableMine来解决这个问题。通过ScalableMine，我们通过设计和基准测试可扩展的事件处理算法和架构，在线聚合多个事件流的事件，以便（接近）真实的时间进行流程挖掘，从而为流流程挖掘的开创性工作做出贡献。流过程挖掘必须是可扩展的，以应对来自分布式源的大量和高速事件。借助ScalableMine，将设计和研究可扩展流进程挖掘的新算法和架构。缺乏代表性的，具体的基准来系统地评估流进程挖掘方法的可扩展性。通过ScalableMine，这些基准将为社区设计，实施，评估和发布。SOURCED的协同作用使得在流程挖掘的上下文中适当地解决可扩展性的挑战成为可能。例如，分布式流流程挖掘的抽象模型将支持基准在线数据聚合。边缘和云基础设施上的资源感知流程挖掘算法将支持边缘与云的可扩展性基准测试。TinyWorkPlace House将为基准测试提供现实的负载配置文件。