Computing and Information Systems - Research Publications
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ItemDetection of statistically significant differences between process variants through declarative rules(Springer, 2020)Services and products are often offered via the execution of processes that vary according to the context, requirements, or customisation needs. The analysis of such process variants can highlight differences in the service outcome or quality, leading to process adjustments and improvement. Research in the area of process mining has provided several methods for process variant analysis. However, very few of those account for a statistical significance analysis of their output. Moreover, those techniques detect differences at the level of process traces, single activities, or performance. In this paper, we aim at describing the distinctive behavioural characteristics between variants expressed in the form of declarative process rules. The contribution to the research area is two-pronged: the use of declarative rules for the explanation of the process variants and the statistical significance analysis of the outcome. We assess the proposed method by comparing its results to the most recent process variant analysis methods. Our results demonstrate not only that declarative rules reveal differences at an unprecedented level of expressiveness, but also that our method outperforms the state of the art in terms of execution time.
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ItemAutomated Discovery of Process Models with True Concurrency and Inclusive Choices(Springer International Publishing, 2021-01-01)Enterprise information systems allow companies to maintain detailed records of their business process executions. These records can be extracted in the form of event logs, which capture the execution of activities across multiple instances of a business process. Event logs may be used to analyze business processes at a fine level of detail using process mining techniques. Among other things, process mining techniques allow us to discover a process model from an event log – an operation known as automated process discovery. Despite a rich body of research in the field, existing automated process discovery techniques do not fully capture the concurrency inherent in a business process. Specifically, the bulk of these techniques treat two activities A and B as concurrent if sometimes A completes before B and other times B completes before A. Typically though, activities in a business process are executed in a true concurrency setting, meaning that two or more activity executions overlap temporally. This paper addresses this gap by presenting a refined version of an automated process discovery technique, namely Split Miner, that discovers true concurrency relations from event logs containing start and end timestamps for each activity. The proposed technique is also able to differentiate between exclusive and inclusive choices. We evaluate the proposed technique relative to existing baselines using 11 real-life logs drawn from different industries.
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ItemIdentifying candidate routines for Robotic Process Automation from unsegmented UI logs(IEEE, 2020-10-22)Robotic Process Automation (RPA) is a technology to develop software bots that automate repetitive sequences of interactions between users and software applications (a.k. a. routines). To take full advantage of this technology, organizations need to identify and to scope their routines. This is a challenging endeavor in large organizations, as routines are usually not concentrated in a handful of processes, but rather scattered across the process landscape. Accordingly, the identification of routines from User Interaction (UI) logs has received significant attention. Existing approaches to this problem assume that the UI log is segmented, meaning that it consists of traces of a task that is presupposed to contain one or more routines. However, a UI log usually takes the form of a single unsegmented sequence of events. This paper presents an approach to discover candidate routines from unsegmented UI logs in the presence of noise, i.e. events within or between routine instances that do not belong to any routine. The approach is implemented as an open-source tool and evaluated using synthetic and real-life UI logs.
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ItemAutomatic Repair of Same-Timestamp Errors in Business Process Event Logs(Springer, 2020)This paper contributes an approach for automatically correcting “same timestamp” errors in business process event logs. These errors consist in multiple events exhibiting the same timestamp within a given process instance. Such errors are common in practice and can be due to the logging granularity or the performance load of the logging system. Analyzing logs that have not been properly screened for such problems is likely to lead to wrong or misleading process insights. The proposed approach revolves around two techniques: one to reorder events with same-timestamp errors, the other to assign an estimated timestamp to each such event. The approach has been implemented in a software prototype and extensively evaluated in different settings, using both artificial and real-life logs. The experiments show that the approach significantly reduces the number of inaccurate timestamps, while the reordering of events scales well to large and complex datasets. The evaluation is complemented by a case study in the meat & livestock domain showing the usefulness of the approach in practice.