We apply business process mining tools and techniques to analyze the event log data (bug report history) generated by an issue tracking system with the objective of discovering runtime process maps, inefficiencies and inconsistencies. However, the runtime process (reality) may not conform to the design time model and can have imperfections or inefficiencies. Project teams define a workflow or a business process (design time process model and guidelines) to streamline and structure the issue management activities. The process of issue reporting to resolution consists of several steps or activities performed by various roles (bug reporter, bug triager, bug fixer, developers, and quality assurance manager) within the software maintenance team. Issue tracking systems such as Bugzilla, Mantis and JIRA are Process Aware Information Systems to support business process of issue (defect and feature enhancement) reporting and resolution. The study, specifically intended for on-body activity recognition systems, further provides designers with a set of guidelines devised to facilitate the system definition and configuration according to the particular application requirements and target activities. From the evaluation, the interval 1-2 s proves to provide the best trade-off between recognition speed and accuracy. To that end, some of the most widely used activity recognition procedures are evaluated for a wide range of window sizes and activities. In this work, we present an extensive study to fairly characterize the windowing procedure, to determine its impact within the activity recognition process and to help clarify some of the habitual assumptions made during the recognition system design. On the contrary, large data windows are normally considered for the recognition of complex activities. Intuitively, decreasing the window size allows for a faster activity detection, as well as reduced resources and energy needs. In fact, most designs normally rely on figures used in previous works, but with no strict studies that support them. Windowing approaches are normally used for segmentation, but no clear consensus exists on which window size should be preferably employed. Signal segmentation is a crucial stage in the activity recognition process however, this has been rarely and vaguely characterized so far. We conclude this paper by demonstrating the usefulness of supervised event abstraction for obtaining more structured and/or more comprehensible process models using both real life event data and synthetic event data. Furthermore, we propose a sequence-focused metric to evaluate supervised event abstraction results that fits closely to the tasks of process discovery and conformance checking. We present a method to generate feature vector representations of events based on XES extensions, and describe an approach to abstract events in an event log with Condition Random Fields using these event features. We show that supervised learning can be leveraged for the event abstraction task when annotations with high-level interpretations of the low-level events are available for a subset of the sequences (i.e., traces). This gives rise to the challenge to bridge the gap between an original low-level event log and a desired high-level perspective on this log, such that a more structured or more comprehensible process model can be discovered. We show that when process discovery algorithms are only able to discover an unrepresentative process model from a low-level event log, structure in the process can in some cases still be discovered by first abstracting the event log to a higher level of granularity. In many cases, events recorded in the event log are too fine-grained, causing process discovery algorithms to discover incomprehensible process models or process models that are not representative of the event log. Process mining techniques focus on extracting insight in processes from event logs.
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