Efficient Process Model Discovery Using Maximal Pattern Mining

In recent years, process mining has become one of the most important and promising areas of research in the field of business process management as it helps businesses understand, analyze, and improve their business processes. In particular, several proposed techniques and algorithms have been proposed to discover and construct process models from workflow execution logs (i.e., event logs). With the existing techniques, mined models can be built based on analyzing the relationship between any two events seen in event logs. Being restricted by that, they can only handle special cases of routing constructs and often produce unsound models that do not cover all of the traces seen in the log. In this paper, we propose a novel technique for process discovery using Maximal Pattern Mining (MPM) where we construct patterns based on the whole sequence of events seen on the traces—ensuring the soundness of the mined models. Our MPM technique can handle loops (of any length), duplicate tasks, non-free choice constructs, and long distance dependencies. Our evaluation shows that it consistently achieves better precision, replay fitness and efficiency than the existing techniques

Process Mining Concepts for Discovering User Behavioral Patterns in Instrumented Software

Process Mining is a technique for discovering “in-use” processes from traces emitted to event logs. Researchers have recently explored applying this technique to documenting processes discovered in software applications. However, the requirements for emitting events to support Process Mining against software applications have not been well documented. Furthermore, the linking of end-user intentional behavior to software quality as demonstrated in the discovered processes has not been well articulated. After evaluating the literature, this thesis suggested focusing on user goals and actual, in-use processes as an input to an Agile software development life cycle in order to improve software quality. It also provided suggestions for instrumenting software applications to support Process Mining techniques

Algorithms for Process Conformance and Process Refinement

Process Conformance is a crucial step in the area of Process Mining: the adequacy of a model derived from applying a discovery algorithm to a log must be certified before making further decisions that affect the system under consideration. In the first part of this thesis, among the different conformance dimensions, we propose a novel measure for precision, based on the simple idea of counting these situations were the model deviates from the log. Moreover, a log-based traversal of the model that avoids inspecting its whole behavior is presented. Experimental results show a significant improvement when compared to current approaches for the same task. Finally, the detection of the shortest traces in the model that lead to discrepancies is presented. In the second part of the thesis, two different approaches are proposed in order to use the precision analysis information for refining the model, improving its accuracy. The first one is based on the idea of break concurrencies reflected in the model but not in the log. The second one presents the Supervisory Control Theory as the mechanism to improve the accuracy of the models building supervisors for controlling the precision issues

IDEAS-1997-2021-Final-Programs

This document records the final program for each of the 26 meetings of the International Database and Engineering Application Symposium from 1997 through 2021. These meetings were organized in various locations on three continents. Most of the papers published during these years are in the digital libraries of IEEE(1997-2007) or ACM(2008-2021)