Social Process Mining

社会过程挖掘（SPM）研究项目旨在开发、实现和评估一种新的过程挖掘方法，该方法适用于企业协作系统（ECS）中基于日志的用户行为检测，并有助于自动检测使用模式由于ECS的解释灵活性，在它们中可能的工作流比，例如，在ERP系统中。由于在环境通信系统中没有重复相同的活动序列，导致流程模型比例如企业资源规划系统中的流程模型复杂得多，因此几乎不可能仅通过人工检查模型来确定典型的行为模式。现有的过程挖掘方法不提供自动支持，以确定行为模式，因为它们发生在ECS的过程到目前为止。即使是针对弱结构化过程定制的方法（例如，SPM方法旨在通过自动识别生成的过程模型中的典型协作场景来纠正这种情况。因此，我们将开发和实现一个基于频繁子图挖掘的模式识别算法作为SPM的一部分。该算法使用真实世界的ECS日志数据和已知的使用模式进行训练。近年来，研究小组通过初步研究，对识别和提供这种已知使用模式（领域知识）所需的过程有了深入的了解。SPM的一般（技术）可行性也已在几项初步研究中得到证明。来自UniConnect协作平台的大量日志数据可用于该项目，该平台由科布伦茨-兰道大学为DACH地区的3，000多名注册用户托管了10多年。SPM开发完成后，新算法将在探索性现场研究中进行测试，使用该领域真实公司的大规模协作系统的实际日志数据。其中一些系统拥有数十万用户，这意味着可以预期有意义的分析结果。计划中的SPM项目因此增加了理论和设计知识。设计目标是开发、实施和评估SPM方法和补充软件工具。对理论的贡献是更好地理解ECS中的用户行为，一方面以识别和测试的协作场景的形式，另一方面通过在真实的公司的背景下发现其使用模式的发生。所获得的知识可以用来更好地了解计算机支持的协作过程中的工作场所，并在此基础上，制定更明智的措施，供用户采用。

The research project "Social Process Mining" (SPM) aims to develop, implement and evaluate a novel process mining approach, which is suitable for log-based detection of user behaviour in Enterprise Collaboration Systems (ECS) and which facilitates the automatic detection of usage patterns (collaboration scenarios).Due to the interpretive flexibility of ECS, the workflows that are possible in them are much more variable and unstructured than, for example, in ERP systems. The lack of repetition of the same sequences of activities in ECS leads to far more complex process models than, for example, in ERP systems, so that it is hardly possible to identify typical behaviour patterns through merely examining the models manually. Existing process mining approaches do not provide automatic support to identify behavioural patterns as they occur in ECS processes so far. Even approaches that are tailored towards weakly structured processes (e.g., declarative process mining or case management approaches) are stretched to their limits here.The SPM approach is intended to remedy this situation by automatically identifying typical collaboration scenarios in the generated process models. Therefore, we will develop and implement a pattern recognition algorithm based on Frequent Subgraph Mining as part of the SPM. The algorithm is trained using real-world ECS log data and known usage patterns. The in-depth understanding of the processes necessary for the identification and provision of such known usage patterns (domain knowledge) has been established by the research team in preliminary studies in recent years. The general (technical) feasibility of SPM has also been demonstrated in several preliminary studies.Extensive log data from the UniConnect collaboration platform, which the University of Koblenz-Landau has been hosting for over 3,000 registered users in the DACH area for more than 10 years, is available for the project. After completion of the SPM development, the new algorithm will be tested in an explorative field study using the actual log data of highly scaled collaboration systems of real-world companies in the field. Some of these systems have hundreds of thousands of users, which means that meaningful analysis results can be expected.The planned SPM project thus adds to theory as well as to the design body of knowledge. The design goal to develop, implement and evaluate the SPM approach and a complementary software tool. The contribution to theory is the better understanding of user behaviour in ECS, on the one hand in the form of identified and tested collaboration scenarios and on the other hand by uncovering the occurrences of their usage patterns in the context of real companies. The acquired knowledge can be used to better understand computer-supported collaboration processes in the workplace and, based on this, to develop better-informed measures for user adoption.