Computing and Information Systems - Research Publications
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ItemUncovering Students’ Learning Pathways: A Process Mining Perspective( 2023)This paper presents an approach to discovering students’ pathways when accessing a Learning Management System (LMS). These pathways reflect students’ compliance with the subject design and/or alternate ways of learning. Discovering such routines can enable the early detection of students at risk of not achieving the intended learning outcomes, as well as informing academics about students’ understanding of their progression in the subject. While LMSs report on aggregate data, they do not report on the order in which students follow the subject design. This information can reveal undesirable situations, such as students responding to the quizzes before completing the prerequisite activities (e.g., watching videos or completing the readings).
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ItemDusting for fingerprints: Revealing patterns of online students’ behaviour( 2023-09-01)Hybrid learning strategies combine face-to-face instruction with online components. These hybrid environments rely heavily on online Learning Management Systems (LMSs) that serve as central hubs for learning materials. Depending on the adopted instructional strategy, students may be expected to complete certain tasks in the LMS. For instance, when adopting a flipped classroom strategy, students must watch videos, read material or complete quizzes prior the classroom time. Then, during classroom time, students focus on performing hands-on exercises. Students consistently engaging in the adopted strategy is key, as failing to do so can decrease the effectiveness of the adopted strategy. This paper presents an approach to monitor the changes of students’ behaviour over time. Using variants analysis, the method analyses data captured in an LMS and finds significant differences between time windows (e.g., two academic weeks). This can inform educators about changes in students’ engagement in the instructional strategy (e.g., students not completing some tasks). To showcase our method, we analyse a semester’s worth of data for a master’s subject implementing flipped classroom strategy.
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ItemStatistical Modelling for Simulating and Interpreting an Egg Packaging Process for Giveaway Mitigation(AIS, 2022)Giveaway, the excess product being packed into orders, contributes to revenue loss that pre-packaged food manufacturers care about the most. In collaboration with an egg packaging company, this study aims to discover operation rules to mitigate the giveaway in egg orders. For that, two variables have been raised as potential controllable factors of the giveaway. One statistical model has been developed to better interpret the experimental results by understanding the underlying rules of the egg grading machine. The experiments have been accurately reproduced by a simulation using the estimated model parameters, indicating the model's success. Based on the experiment results, we claim that the number of accepted egg grades significantly influences the final giveaway ratio. Limitations and further potentials of the statistical model have also been discussed.
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ItemDiscovering Unseen Behaviour from Event Logs(SpringerLink, 2022)Process mining techniques aim to discover insights into the performance of a business process by analysing its event logs. These logs capture historical process executions as sequences of activity occurrences (events). Often, event logs capture only part of the possible process behaviour because the number of executions can be very large, particularly when many activities are executed con- currently. A highly incomplete event log is problematic because process mining techniques use the event log as a starting point. This paper proposes a technique to discover behaviour from an incomplete log. In order to do so, the presented technique builds distributive lattices from the executions captured in the log, which have well-defined notions of completeness and can be used to discover behaviour from few observations. The paper tests the presented approach in a set of real-life event logs and measures the amount of behaviour that can be discovered.
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ItemProcess Model Repair(Springer, 2019)
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ItemScalable alignment of process models and event logs: An approach based on automata and S-components(PERGAMON-ELSEVIER SCIENCE LTD, 2020-12)
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ItemOn the expressive power of behavioral profiles(SPRINGER, 2016-07-01)Behavioral profiles have been proposed as a behavioral abstraction of dynamic systems, specifically in the context of business process modeling. A behavioral profile can be seen as a complete graph over a set of task labels, where each edge is annotated with one relation from a given set of binary behavioral relations. Since their introduction, behavioral profiles were argued to provide a convenient way for comparing pairs of process models with respect to their behavior or computing behavioral similarity between process models. Still, as of today, there is little understanding of the expressive power of behavioral profiles. Via counter-examples, several authors have shown that behavioral profiles over various sets of behavioral relations cannot distinguish certain systems up to trace equivalence, even for restricted classes of systems represented as safe workflow nets. This paper studies the expressive power of behavioral profiles from two angles. Firstly, the paper investigates the expressive power of behavioral profiles and systems captured as acyclic workflow nets. It is shown that for unlabeled acyclic workflow net systems, behavioral profiles over a simple set of behavioral relations are expressive up to configuration equivalence. When systems are labeled, this result does not hold for any of several previously proposed sets of behavioral relations. Secondly, the paper compares the expressive power of behavioral profiles and regular languages. It is shown that for any set of behavioral relations, behavioral profiles are strictly less expressive than regular languages, entailing that behavioral profiles cannot be used to decide trace equivalence of finite automata and thus Petri nets.
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ItemOn the suitability of generalized behavioral profiles for process model comparison(Lecture Notes in Computer Science, 2016-01-01)© Springer International Publishing Switzerland 2016. Given two process models, the problem of behavioral comparison is that of determining if these models are behaviorally equivalent (e.g., by trace equivalence) and, if not, identifying how can the differences be presented in a compact manner? Behavioral profiles have been proposed as a convenient abstraction for this problem. A behavioral profile is a matrix, where each cell encodes a behavioral relation between a pair of tasks (e.g., causality or conflict). Thus, the problem of behavioral comparison can be reduced to matrix comparison. It has been observed that while behavioral profiles can be efficiently computed, they are not accurate insofar as behaviorally different process models may map to the same behavioral profile. This paper investigates the question of how accurate existing behavioral profiles are. The paper shows that behavioral profiles are fully behavior preserving for the class of acyclic unlabeled nets with respect to configuration equivalence. However, for the general class of acyclic nets, existing behavioral profiles are exponentially inaccurate, meaning that two acyclic nets with the same behavioral profile may differ in an exponential number of configurations.
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ItemMeasuring Fitness and Precision of Automatically Discovered Process Models: A Principled and Scalable Approach(IEEE COMPUTER SOC, 2022-04-01)
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ItemLocal Concurrency Detection in Business Process Event Logs(Association for Computing Machinery, 2019-03-01)Process mining techniques aim at analysing records generated during the execution of a business process in order to provide insights on the actual performance of the process. Detecting concurrency relations be- tween events is a fundamental primitive underpinning a range of process mining techniques. Existing approaches to this problem identify concur- rency relations at the level of event types under a global interpretation. If two event types are declared to be concurrent, every occurrence of one event type is deemed to be concurrent to one occurrence of the other. In practice, this interpretation is too coarse-grained and leads to over- generalization. This paper proposes a finer-grained approach, whereby two event types may be deemed to be in a concurrency relation relative to one state of the process, but not relative to other states. In other words, the detected concurrency relation holds locally, relative to a set of states. Experimental results both with artificial and real-life logs show that the proposed local concurrency detection approach improves the accuracy of existing concurrency detection techniques.