Enhancing Declare Maps Based on Event Correlations

Abstract

Abstract. Traditionally, most process mining techniques aim at discovering procedural process models (e.g., Petri nets, BPMN, and EPCs) from event data. However, the variability present in less-structured flexible processes complicates the discovery of such procedural models. The "open world" assumption used by declarative models makes it easier to handle this variability. However, initial attempts to automatically discover declarative process models result in cluttered diagrams showing misleading constraints. Moreover, additional data attributes in event logs are not used to discover meaningful causalities. In this paper, we use correlations to prune constraints and to disambiguate event associations. As a result, the discovered process maps only show the more meaningful constraints. Moreover, the data attributes used for correlation and disambiguation are also used to find discriminatory patterns, identify outliers, and analyze bottlenecks (e.g., when do people violate constraints or miss deadlines). The approach has been implemented in ProM and experiments demonstrate the improved quality of process maps and diagnostics.

Performance Analysis of Business Processes using Process Mining

“…[21]. in addition, there are post-processing approaches that aim at simplifying the ensuing Declare models in terms of redundancy elimination [22] and illumination [23]. The approach proposed in ref.…”

Mining processes with multi-instantiation

“…In this context, various techniques are used for deleting insignificant behaviour, e.g. in terms of event classes [13] and event relationships [3]. Clustering criteria are extended based on statistics [14], temporal proximity [23], attribute values [20], and text content [1].…”

Process mining in oncology: A literature review

“…Data limitations were identified in ten papers [20], [22], [33], [36], [37], [44], [48], [57], [60] and were mostly related to limited access to the data, data quality problems, attributes not available in the data being extracted, or the dataset available in inappropriate level of details. Technique limitations were identified in 13 papers [3], [21], [28], [31], [35], [38]- [40], [48], [50], [52], [63], [64] specifically if the study undertaken was done by implementing plug-ins/ functionalities available in tools. Team limitations were identified in two papers [11], [23] because the authors realize that they need medical domain experts to be included in the research team.…”

“…Some papers identified data improvements [28]- [30], [36]- [38], [48], [57], [64] in terms of improving data quality, dimensionality, and complexity. A larger number of important technical improvements were identified in 20 papers [3], [21]- [23], [29]- [31], [35], [39]- [41], [44], [48]- [52], [58], [60], [64].…”