BPMDS Session 1: Improving event data quality in coherence with business require скачать в хорошем качестве

BPMDS Session 1: Improving event data quality in coherence with business require

From Network Traffic Data to Business Activities: A Process Mining Driven Conceptualization (Gal Engelberg, Moshe Hadad, Pnina Soffer) Event logs are the main source for business process mining techniques. However, they are produced by part of the systems and are not always available. Furthermore, logs that are created by a given information system may not span the full process, which may entail actions performed outside the system. We suggest that data generated by communication network traffic associated with the process can fill this gap, both in availability and in span. However, traffic data is technically oriented and noisy, and there is a huge conceptual gap between this data and business meaningful event logs. Addressing this gap, this work develops a conceptual model of traffic behavior in a business activity. To develop the model, we use simulated traffic data annotated by the originating activity and perform an iterative process of abstracting and filtering the data, along with application of process discovery. The results include distinct process models for each activity type and a generic higher-level model of traffic behavior in a business activity. Conformance checking used for evaluating the models shows high fitness and generalization across different organizational domains. A NLP-oriented Methodology to Enhance Event Log Quality (Belén Ramos Gutiérrez, Belén Ramos Gutiérrez, Javier Ortega, Maria Teresa Gómez López, Moe Thandar Wynn) The quality of event logs is a crucial cornerstone for the feasibility of the application of later process mining techniques. The wide variety of data that can be included in an event log refer to information about the activity, such as what, who or where. In this paper, we focus on event logs that include textual information written in a natural language which contains exhaustive descriptions of activity executions. In this context, a pre-processing step is necessary since textual information is unstructured and it can contain inaccuracies that will provoke the impracticability of process mining techniques. For this reason, we propose a methodology that applies Natural Language Processing (NLP) to raw event log by relabelling activities. The approach let the customised description of the measurement and assessment of the event log quality depending on expert requirements. Additionally, it guides the selection of the most suitable NLP techniques for use. The methodology has been evaluated using a real-life event log that includes detailed textual descriptions to capture the management of incidents in the aircraft assembly process in aerospace manufacturing.