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Jochen De Weerdt

Jochen De Weerdt
KU Leuven | ku leuven · Research Centre for Management Informatics (LIRIS)

About

101
Publications
21,466
Reads
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1,703
Citations
Citations since 2016
73 Research Items
1425 Citations
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2016201720182019202020212022050100150200250300
2016201720182019202020212022050100150200250300
Introduction
Additional affiliations
January 2014 - present
KU Leuven
Position
  • Professor
November 2012 - December 2013
Queensland University of Technology
Position
  • PostDoc Position
September 2009 - November 2012
KU Leuven
Position
  • PhD Student

Publications

Publications (101)
Conference Paper
Full-text available
As machine and deep learning models are increasingly leveraged in predictive process monitoring, the focus has shifted towards making these models explainable. The successful adoption of a model is dependent on whether decision-makers can trust the predictions and explanations made. However, recent studies have shown that deep learning models are v...
Article
Advanced fraud detection systems leverage the digital traces from (credit-card) transactions to detect fraudulent activity in future transactions. Recent research in fraud detection has focused primarily on data analytics combined with manual feature engineering, which is tedious, expensive and requires considerable domain expertise. Furthermore, t...
Preprint
Full-text available
A growing number of universities worldwide use various forms of online and blended learning as part of their academic curricula. Furthermore, the recent changes caused by the COVID-19 pandemic have led to a drastic increase in importance and ubiquity of online education. Among the major advantages of e-learning is not only improving students' learn...
Article
Business failure prediction (BFP) is an important instrument in assessing the risk of corporate failure. While a large body of research has focused on BFP, recent research in operations research and analytics acknowledges the beneficial effect of incorporating textual data for predictive modelling. However, extant BFP research that incorporates tex...
Article
A growing number of universities worldwide use various forms of online and blended learning as part of their academic curricula. Furthermore, the recent changes caused by the COVID-19 pandemic have led to a drastic increase in importance and ubiquity of online education. Among the major advantages of e-learning is not only improving students’ learn...
Preprint
Full-text available
Reliable remaining time prediction of ongoing business processes is a highly relevant topic. One example is order delivery, a key competitive factor in e.g. retailing as it is a main driver of customer satisfaction. For realising timely delivery, an accurate prediction of the remaining time of the delivery process is crucial. Within the field of pr...
Preprint
Full-text available
Artificial neural networks' inability to assess the uncertainty of their predictions poses a major roadblock towards more widespread adoption. We distinguish two strains of uncertainty, which can both be learned: model uncertainty-caused by a paucity of training data-and noise-induced observational uncertainty. Based on sound mathematical foundatio...
Chapter
Currently, the state of a house is typically assessed by an expert, which is time and resource intensive. Therefore, an automatic assessment could have economic, social and ecological benefits. Hence, this study presents a binary classification model using transfer learning to classify Google Street View images of houses. For this purpose, a three-...
Chapter
Advances in AI, and especially machine learning, are increasingly drawing research interest and efforts towards predictive process monitoring, the subfield of process mining (PM) that concerns predicting next events, process outcomes and remaining execution times. Unfortunately, researchers use a variety of datasets and ways to split them into trai...
Article
Graphs can be seen as a universal language to describe and model a diverse set of complex systems and data structures. However, efficiently extracting topological information from dynamic graphs is not a straightforward task. Previous works have explored a variety of inductive graph representation learning frameworks, but despite the surge in devel...
Chapter
Full-text available
Predictive process monitoring concerns itself with the prediction of ongoing cases in (business) processes. Prediction tasks typically focus on remaining time, outcome, next event or full case suffix prediction. Various methods using machine and deep learning have been proposed for these tasks in recent years. Especially recurrent neural networks (...
Chapter
Full-text available
Contextualisation is an important challenge in process mining. While Internet of Things (IoT) devices are collecting more and more data on the physical context in which business processes are executed, the IoT and process mining fields are still considerably disintegrated. Important concepts, such as event or context , are not understood in the sam...
Article
Due to the unprecedented growth in available data collected by e-learning platforms, including platforms used by MOOC providers, important opportunities arise to structurally use these data for decision making and improvement of the educational offering. Student retention is a strategic task that can be supported by means of automated data-driven d...
Chapter
Process analytics is an umbrella of data-driven techniques which includes making predictions for individual process instances or overall process models. At the instance level, various novel techniques have been recently devised, tackling next activity, remaining time, and outcome prediction. At the model level, there is a notable void. It is the am...
Preprint
Full-text available
Within the field of process mining, several different trace clustering approaches exist for partitioning traces or process instances into similar groups. Typically, this partitioning is based on certain patterns or similarity between the traces, or driven by the discovery of a process model for each cluster. The main drawback of these techniques, h...
Preprint
Full-text available
Predictive process monitoring concerns itself with the prediction of ongoing cases in (business) processes. Prediction tasks typically focus on remaining time, outcome, next event or full case suffix prediction. Various methods using machine and deep learning have been proposed for these tasks in recent years. Especially recurrent neural networks (...
Chapter
Artificial neural networks will always make a prediction, even when completely uncertain and regardless of the consequences. This obliviousness of uncertainty is a major obstacle towards their adoption in practice. Techniques exist, however, to estimate the two major types of uncertainty: model uncertainty and observation noise in the data. Bayesia...
Preprint
Full-text available
Advances in AI, and especially machine learning, are increasingly drawing research interest and efforts towards predictive process monitoring, the subfield of process mining (PM) that concerns predicting next events, process outcomes and remaining execution times. Unfortunately , researchers use a variety of datasets and ways to split them into tra...
Chapter
Most data-aware process modelling approaches have been developed from a process perspective and lack a full-fledged data modelling approach. In addition, the evaluation of data-centric process approaches reveals that, even though their value is acknowledged, their usability is a point of concern. This paper presents a data-aware process modelling a...
Conference Paper
Full-text available
Artificial neural networks will always make a prediction, even when completely uncertain and regardless of the consequences. This obliviousness of uncertainty is a major obstacle towards their adoption in practice. Techniques exist, however, to estimate the two major types of uncertainty: model uncertainty and observation noise in the data. Bayesia...
Preprint
Full-text available
Artificial neural networks will always make a prediction, even when completely uncertain and regardless of the consequences. This obliviousness of uncertainty is a major obstacle towards their adoption in practice. Techniques exist, however, to estimate the two major types of uncertainty: model uncertainty and observation noise in the data. Bayesia...
Preprint
Full-text available
Process analytics is the field focusing on predictions for individual process instances or overall process models. At the instance level, various novel techniques have been recently devised, tackling next activity, remaining time, and outcome prediction. At the model level, there is a notable void. It is the ambition of this paper to fill this gap....
Article
Full-text available
Within the field of process mining, several different trace clustering approaches exist for partitioning traces or process instances into similar groups. Typically, this partitioning is based on certain patterns or similarity between the traces, or driven by the discovery of a process model for each cluster. The main drawback of these techniques, h...
Preprint
Full-text available
The early outcome prediction of ongoing or completed processes confers competitive advantage to organizations. The performance of classic machine learning and, more recently, deep learning techniques such as Long Short-Term Memory (LSTM) on this type of classification problem has been thorougly investigated. Recently, much research focused on apply...
Chapter
Full-text available
Conformance checking is concerned with the task of assessing the quality of process models describing actual behavior captured in an event log across different dimensions. In this paper, a novel approach for obtaining the degree of recall and precision between a process model and event log is introduced. The approach relies on the generation of a s...
Article
Teacher feedback provided to learners in real-time is a crucial factor for their knowledge and skills acquisition. However, providing real-time feedback at an individual level is often infeasible, considering limited teaching resources. Fortunately, recent technological advancements have allowed for developing of various computer tutoring systems,...
Preprint
Full-text available
The field of predictive process monitoring focuses on modelling future characteristics of running business process instances, typically by either predicting the outcome of particular objectives (e.g. completion (time), cost), or next-in-sequence prediction (e.g. what is the next activity to execute). This paper introduces Processes-As-Movies (PAM),...
Chapter
Full-text available
Conformance checking describes process mining techniques used to compare an event log and a corresponding process model. In this paper, we propose an entirely new approach to conformance checking based on neural network-based embeddings. These embeddings are vector representations of every activity/task present in the model and log, obtained via ac...
Article
Full-text available
Healthcare organizations are confronted with challenges including the contention between tightening budgets and increased care needs. In the light of these challenges, they are becoming increasingly aware of the need to improve their processes to ensure quality of care for patients. To identify process improvement opportunities, a thorough process...
Chapter
Representation Learning in dynamic networks has gained increasingly more attention due to its promising applicability. In the literature, we can find two popular approaches that have been adapted to dynamic networks: random-walk based techniques and graph-autoencoders. Despite the popularity, no work has compared them in well-know datasets. We fill...
Chapter
Community detection has recently received increased attention due to its wide range of applications in many fields. While at first most techniques were focused on discovering communities in static networks, lately the focus has shifted toward evolving networks because of their high relevance in real-life problems. Given the increasing number of the...
Chapter
Full-text available
Representation learning in graphs has proven useful for many predictive tasks. In this paper we assess the feasibility of representation learning in a credit card fraud setting. Data analytics has been successful in predicting fraud in previous research. However, the research field has focused on techniques which require tedious and expensive hand-...
Chapter
In a study of mobility and urban behaviour, we analyse a longitudinal mobility data set from a sequence mining perspective using a technique that discovers behavioural constraints in sequences of movements between venues. Our contribution is two-fold. First, we propose a methodology to convert aggregated mobility data into insightful patterns. Seco...
Chapter
Full-text available
The early outcome prediction of ongoing or completed processes confers competitive advantage to organizations. The performance of classic machine learning and, more recently, deep learning techniques such as Long Short-Term Memory (LSTM) on this type of classification problem has been thorougly investigated. Recently, much research focused on apply...
Poster
Full-text available
Introduction • Fraud prediction based on a network of transactions from a large, real-life credit card fraud dataset with ground-truth fraud labels. • Automatic feature generation with graph representation learning (RL) for node classification (instead of manual feature engineering). • We propose an inductive extension based on Nearest Neighbors to...
Article
Finding structural and efficient ways of leveraging available data is not an easy task, especially when dealing with network data, as is the case in telco churn prediction. Several previous works have made advancements in this direction both from the perspective of churn prediction, by proposing augmented call graph architectures, and from the pers...
Article
Sequence classification deals with the task of finding discriminative and concise sequential patterns. To this purpose, many techniques have been proposed, which mainly resort to the use of partial orders to capture the underlying sequences in a database according to the labels. Partial orders, however, pose many limitations, especially on expressi...
Article
Applying social network analytics for telco churn prediction has become indispensable for almost a decade. However, in the current literature, the uptake does not reflect in a significantly increased leverage of the available information that these networks convey. First, network featurization in general is a very cumbersome process due to the comp...
Chapter
Process mining, and in particular process discovery, provides useful tools for extracting process models from event-based data. Nevertheless, certain types of processes are too complex and unstructured to be able to be represented with a start-to-end process model. For such cases, instead of extracting a model from a complete event log, it is inter...
Chapter
The research area of process mining concerns itself with knowledge discovery from event logs, containing recorded traces of executions as stored by process aware information systems. Over the past decade, research in process mining has increasingly focused on predictive process monitoring to provide businesses with valuable information in order to...
Conference Paper
p>In many real-life applications it is crucial to be able to, given a collection of link states of a network in a certain time period, accurately predict the link state of the network at a future time. This is known as dynamic link prediction, which compared to its static counterpart is more complex, as capturing the temporal characteristics is a n...
Conference Paper
Full-text available
Applying social network analysis on call networks for churn prediction in telco is proven to result in increased predictive performance, and as such, is already a common practice in the literature. Nevertheless, current works have two major drawbacks. First, call networks are usually considered static, and second, network features are typically han...
Article
Full-text available
Flexible systems and services require a solid approach for modeling and enacting dynamic behavior. Declarative process models gained plenty of traction lately as they have proven to provide a good fit for the problem at hand, i.e. visualizing and executing flexible business processes. These models are based on constraints that impose behavioral res...
Article
Churn prediction in telco remains a very active research topic. Due to the uptake of social network analytics and the results of previous benchmarking studies showing a rather flat maximum performance effect of predictive modeling techniques, the focus has mainly shifted to expanding and exploring the relevant feature space. While previous studies...
Conference Paper
Full-text available
Most of the recent studies on churn prediction in telco utilize social networks built on top of the call (and/or SMS) graphs to derive informative features. However, extracting features from large graphs, especially structural features, is an intricate process both from a methodological and computational perspective. Due to the former, feature extr...
Chapter
Given the complexity of real-life event logs, several trace clustering techniques have been proposed to partition an event log into subsets with a lower degree of variation. In general, these techniques assume that the number of clusters is known in advance. However, this will rarely be the case in practice. Therefore, this paper presents approache...
Conference Paper
Full-text available
Current studies on churn prediction in telco apply network analytics to analyze and featurize call graphs. While the suggested approaches demonstrate a lot of creativity when it comes to deriving new features from the underlying networks, they also exhibit at least one of the following problems: they either do not account properly for dynamic aspec...
Conference Paper
Full-text available
In this paper we present the interesting Behavioral Constraint Miner (iBCM), a new approach towards classifying sequences. The prevalence of sequential data, i.e., a collection of ordered items such as text, website navigation patterns, traffic management, and so on, has incited a surge in research interest towards sequence classification. Existing...
Conference Paper
Trace clustering techniques are a set of approaches for partitioning traces or process instances into similar groups. Typically, this partitioning is based on certain patterns or similarity between the traces, or done by discovering a process model for each cluster of traces. In general, however, it is likely that clustering solutions obtained by t...
Article
Full-text available
This paper presents a technique that aims to increase human understanding of trace clustering solutions. The clustering techniques under scrutiny stem from the process mining domain, where the clustering of process instances is deemed a useful technique to analyse process data with a large variety of behaviour. Until now, the most often used method...
Article
In this paper, we present Fodina, a process discovery technique with a strong focus on robustness and flexibility. To do so, we improve upon and extend an existing process discovery algorithm, namely Heuristics Miner. We have identified several drawbacks which impact the reliability of existing heuristic-based process discovery techniques and there...
Conference Paper
Full-text available
An extensive amount of work has addressed the evaluation of process discovery techniques and the process models they discover based on concepts like fitness, precision, generalization and simplicity. In this paper, we claim that stability could be considered as an important supplementary evaluation dimension for process discovery next to accuracy a...
Conference Paper
Full-text available
In recent years, a multitude of techniques has been proposed for the task of clustering traces. In general, these techniques either focus on optimizing their solution based on a certain type of similarity between the traces, such as the number of insertions and deletions needed to transform one trace into another; by mapping the traces onto a vecto...
Article
Full-text available
Organisations are constantly seeking new ways to improve operational efficiencies. This study investigates a novel way to identify potential efficiency gains in business operations by observing how they were carried out in the past and then exploring better ways of executing them by taking into account trade-offs between time, cost and resource uti...
Conference Paper
Full-text available
Recently, a surge in the use of declarative process models has been witnessed. These constraint-driven models excel at representing and enacting flexible and adaptable decision processes in application areas such as scheduling and workflow management. This work examines the intricacies of the most widespread declarative process language, Declare, w...
Conference Paper
Full-text available
Given the complexity of real-life event logs, several trace clustering techniques have been proposed to partition an event log into subsets with a lower degree of variation. In general, these techniques assume that the number of clusters is known in advance. However, this will rarely be the case in practice. Therefore, this paper is the first to pr...