Computing and Information Systems - Research Publications
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ItemMulti-Perspective process model discovery for robotic process automation(CEUR Workshop Proceedings, 2018-01-01)Robotic Process Automation (RPA) is a novel approach for immediate cost reduction and gaining operational efficiency. RPA tools can automate repeatable tasks, thus reducing the error rates and increasing overall process performance. Even more, RPA improves the quality of the data (data completeness, data consistency/correctness, etc.). Although, being widely used in many organizations, RPA suffers from high time consumption allocated to the training of software robots (bots for short). Moreover, the models used for training are often inaccurate, which leads to increase of time spent on testing the bots. One of the possible solutions is to apply process mining in order to extract the information about the processes from UI logs such as clickstreams and keylogs, which can then be used to train the bots. However, traditional process discovery techniques are not suitable for the purpose of RPA, as they discover only control-flow perspective of the process and cannot deal well with the UI logs, producing huge and complex models. The proposed research project aims at shifting process mining techniques from working on event logs to working on UI logs as well as developing multi-perspective automated discovery technique, which can then be applied to train the RPA bots.
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ItemPredictive Process Monitoring in Apromore(Springer-Verlag, Journals, 2018-06-15)This paper discusses the integration of Nirdizati, a tool for predictive process monitoring, into the Web-based process analytics platform Apromore. Through this integration, Apromore’s users can use event logs stored in the Apromore repository to train a range of predictive models, and later use the trained models to predict various performance indicators of running process cases from a live event stream. For example, one can predict the remaining time or the next events until case completion, the case outcome, or the violation of compliance rules or internal policies. The predictions can be presented graphically via a dashboard that offers multiple visualization options, including a range of summary statistics about ongoing and past process cases. They can also be exported into CSV for periodic reporting or to be visualized in third-parties business intelligence tools. Based on these predictions, operations managers may identify potential issues early on, and take remedial actions in a timely fashion, e.g. reallocating resources from one case onto another to avoid that the case runs overtime.
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ItemTimestamp Repair for Business Process Event Logs( 2018-04-05)This paper contributes an approach for automatically correcting timestamp errors in business process execution logs. These errors are quite common in practice 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 erroneous timestamps, the other to assign an estimated timestamp to each such event. The approach has been implemented in a software tool and extensively evaluated in different settings, using both synthetic and real-life logs. The experiments show that the approach significantly reduces the amount of incorrect 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.
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ItemAbstract and Compare: A Framework for Defining Precision Measures for Automated Process Discovery(SpringerLink, 2018-03-19)Automated process discovery techniques allow us to extract business process models from event logs. The quality of process models discovered by these techniques can be assessed with respect to various quality criteria related to simplicity and accuracy. One of these criteria, namely precision, captures the extent to which the behavior allowed by a discovered process model is observed in the log. While numerous measures of precision have been proposed in the literature, a recent study has shown that none of them fulfils a set of five axioms that capture intuitive properties behind the concept of precision. In addition, several existing precision measures suffer from scalability issues when applied to models discovered from real-life event logs. This paper presents a versatile framework for defining precision measures based on behavior abstractions. The key idea is that a precision measure can be defined by three ingredients: a function that abstracts a process model (e.g. as a transition system), a function that does the same for an event log, and a function that compares the behavior abstraction of the model with that of the log. We show empirically that different instances of this framework allow us to strike different tradeoffs between scalability and sensitivity. We also show that two instances of the framework based on lossless abstraction functions yield a precision measure that fulfils all the above-mentioned axioms.
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ItemFiltering Spurious Events from Event Streams of Business Processes(Springer, 2018-06-04)Process mining aims at gaining insights into business processes by analysing event data recorded during process execution. The majority of existing process mining techniques works offline, i.e. using static, historical data stored in event logs. Recently, the notion of online process mining has emerged, whereby techniques are applied on live event streams, as process executions un- fold. Analysing event streams allows us to gain instant insights into business processes. However, current techniques assume the input stream to be completely free of noise and other anomalous behaviour. Hence, applying these techniques on real data leads to results of inferior quality. In this paper, we propose an event processor that enables us to effectively filter out spurious events from a live event stream. Our experiments show that we are able to effectively filter out spurious events from the input stream and, as such, enhance online process mining results.
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ItemAutomated Discovery of Structured Process Models From Event Logs: The Discover-and-Structure Approach(Elsevier, 2018)This article tackles the problem of discovering a process model from an event log recording the execution of tasks in a business process. Previous approaches to this reverse-engineering problem strike different tradeoffs between the accuracy of the discovered models and their structural complexity. With respect to the latter property, empirical studies have demonstrated that block-structured process models are gener- ally more understandable and less error-prone than unstructured ones. Accordingly, several methods for automated process model discovery generate block-structured models only. These methods however intertwine the objective of producing accurate models with that of ensuring their structuredness, and often sacrifice the former in favour of the latter. In this paper we propose an alternative approach that separates these concerns. Instead of directly discovering a structured process model, we first apply a well-known heuristic that discovers accurate but oftentimes unstructured (and even unsound) process models, and then we transform the resulting process model into a structured (and sound) one. An experimental evaluation on synthetic and real-life event logs shows that this discover-and-structure approach consistently outperforms previous approaches with respect to a range of accuracy and complexity measures.
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ItemNo Preview AvailableFundamentals of Business Process Management(Springer-Verlag, 2018)This textbook covers the entire Business Process Management (BPM) lifecycle, from process identification to process monitoring, covering along the way process modelling, analysis, redesign and automation. Concepts, methods and tools from business management, computer science and industrial engineering are blended into one comprehensive and inter-disciplinary approach. The presentation is illustrated using the BPMN industry standard defined by the Object Management Group and widely endorsed by practitioners and vendors worldwide. In addition to explaining the relevant conceptual background, the book provides dozens of examples, more than 230 exercises – many with solutions – and numerous suggestions for further reading. This second edition includes extended and completely revised chapters on process identification, process discovery, qualitative process analysis, process redesign, process automation and process monitoring. A new chapter on BPM as an enterprise capability has been added, which expands the scope of the book to encompass topics such as the strategic alignment and governance of BPM initiatives. The textbook is the result of many years of combined teaching experience of the authors, both at the undergraduate and graduate levels as well as in the context of professional training. Students and professionals from both business management and computer science will benefit from the step-by-step style of the textbook and its focus on fundamental concepts and proven methods. Lecturers will appreciate the class-tested format and the additional teaching material available on the accompanying website.