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Increasing the success rate of a process, i.e. the percentage of cases that end in a positive outcome, is a recurrent process improvement goal. At runtime, there are often certain actions (a.k.a. treatments) that workers may execute to lift the probability that a case ends in a positive outcome. For example, in a loan origination process, a possible treatment is to issue multiple loan offers to increase the probability that the customer takes a loan. Each treatment has a cost. Thus, when defining policies for prescribing treatments to cases, managers need to consider the net gain of the treatments. Also, the effect of a treatment varies over time: treating a case earlier may be more effective than later in a case. This paper presents a prescriptive monitoring method that automates this decision-making task. The method combines causal inference and reinforcement learning to learn treatment policies that maximize the net gain. The method leverages a conformal prediction technique to speed up the convergence of the reinforcement learning mechanism by separating cases that are likely to end up in a positive or negative outcome, from uncertain cases. An evaluation on two real-life datasets shows that the proposed method outperforms a state-of-the-art baseline.

State-of-the-art process discovery methods construct free-choice process models from event logs. Consequently, the constructed models do not take into account indirect dependencies between events. Whenever the input behaviour is not free-choice, these methods fail to provide a precise model. In this paper, we propose a novel approach for enhancing free-choice process models by adding non-free-choice constructs discovered a-posteriori via region-based techniques. This allows us to benefit from the performance of existing process discovery methods and the accuracy of the employed fundamental synthesis techniques. We prove that the proposed approach preserves fitness with respect to the event log while improving the precision when indirect dependencies exist. The approach has been implemented and tested on both synthetic and real-life datasets. The results show its effectiveness in repairing models discovered from event logs. This work was partly supported by the Australian Research Council Discovery Project DP180102839. This work was supported by MINECO and FEDER funds under grant TIN2017-86727-C2-1-R.

As the first phase in the Business Process Management (BPM) lifecycle, process identification addresses the problem of identifying which processes to prioritize for improvement. Process selection plays a critical role in this phase, but it is a step with known pitfalls. Decision makers rely frequently on subjective criteria, and their knowledge of the alternative processes put forward for selection is often inconsistent. This leads to poor quality decision-making and wastes resources. In recent years, a rejection of a one-size-fits-all approach to BPM in favor of a more context-aware approach has gained significant academic attention. In this study, the role of context in the process selection step is considered. The context is qualitative, subjective, sensitive to decision-making bias and politically charged. We applied a design-science approach and engaged industry decision makers through a combination of research methods to assess how different configurations of process inputs influence and ultimately improve the quality of the process selection step. The study highlights the impact of framing effects on context and provides five guidelines to improve effectiveness., Comment: 16 pages, 5 figures, 3 tables

State-of-the-art process discovery methods construct free-choice process models from event logs. Hence, the constructed models do not take into account indirect dependencies between events. Whenever the input behavior is not free-choice, these methods fail to provide a precise model. In this paper, we propose a novel approach for the enhancement of free-choice process models, by adding non-free-choice constructs discovered a-posteriori via region-based techniques. This allows us to benefit from both the performance of existing process discovery methods, and the accuracy of the employed fundamental synthesis techniques. We prove that the proposed approach preserves fitness with respect to the event log, while improving the precision when indirect dependencies exist. The approach has been implemented and tested on both synthetic and real-life datasets. The results show its effectiveness in repairing process models discovered from event logs.

The importance of quality measures in process mining has increased. One of the key quality aspects, generalization, is concerned with measuring the degree of overfitting of a process model w.r.t. an event log, since the recorded behavior is just an example of the true behavior of the underlying business process. Existing generalization measures exhibit several shortcomings that severely hinder their applicability in practice. For example, they assume the event log fully fits the discovered process model, and cannot deal with large real-life event logs and complex process models. More significantly, current measures neglect generalizations for clear patterns that demand a certain construct in the model. For example, a repeating sequence in an event log should be generalized with a loop structure in the model. We address these shortcomings by proposing a framework of measures that generalize a set of patterns discovered from an event log with representative traces and check the corresponding control-flow structures in the process model via their trace alignment. We instantiate the framework with a generalization measure that uses tandem repeats to identify repetitive patterns that are compared to the loop structures and a concurrency oracle to identify concurrent patterns that are compared to the parallel structures of the process model. In an extensive qualitative and quantitative evaluation using 74 log-model pairs using against two baseline generalization measures, we show that the proposed generalization measure consistently ranks process models that fulfil the observed patterns with generalizing control-flow structures higher than those which do not, while the baseline measures disregard those patterns. Further, we show that our measure can be efficiently computed for datasets two orders of magnitude larger than the largest dataset the baseline generalization measures can handle.

Banks play an intrinsic role in any modern economy, recycling capital from savers to borrowers. They are heavily regulated and there have been a significant number of well publicized compliance failings in recent years. This is despite Business Process Compliance (BPC) being both a well researched domain in academia and one where significant progress has been made. This study seeks to determine why Australian banks find BPC so challenging. We interviewed 22 senior managers from a range of functions within the four major Australian banks to identify the key challenges. Not every process in every bank is facing the same issues, but in processes where a bank is particularly challenged to meet its compliance requirements, the same themes emerge. The compliance requirement load they bear is excessive, dynamic and complex. Fulfilling these requirements relies on impenetrable spaghetti processes, and the case for sustainable change remains elusive, locking banks into a fail-fix cycle that increases the underlying complexity. This paper proposes a conceptual framework that identifies and aggregates the challenges, and a circuit-breaker approach as an "off ramp" to the fail-fix cycle.

Robotic Process Automation (RPA) is a technology to automate routine work such as copying data across applications or filling in document templates using data from multiple applications. RPA tools allow organizations to automate a wide range of routines. However, identifying and scoping routines that can be automated using RPA tools is time consuming. Manual identification of candidate routines via interviews, walk-throughs, or job shadowing allow analysts to identify the most visible routines, but these methods are not suitable when it comes to identifying the long tail of routines in an organization. This article proposes an approach to discover automatable routines from logs of user interactions with IT systems and to synthesize executable specifications for such routines. The approach starts by discovering frequent routines at a control-flow level (candidate routines). It then determines which of these candidate routines are automatable and it synthetizes an executable specification for each such routine. Finally, it identifies semantically equivalent routines so as to produce a set of non-redundant automatable routines. The article reports on an evaluation of the approach using a combination of synthetic and real-life logs. The evaluation results show that the approach can discover automatable routines that are known to be present in a UI log, and that it identifies automatable routines that users recognize as such in real-life logs., 41 pages, 6 figures, 10 tables. arXiv admin note: text overlap with arXiv:2008.05782

The aim of a process discovery algorithm is to construct from event data a process model that describes the underlying, real-world process well. Intuitively, the better the quality of the input event data, the better the quality of the resulting discovered model should be. However, existing process discovery algorithms do not guarantee this relationship. We demonstrate this by using a range of quality measures for both event data and discovered process models. This paper is a call to the community of IS engineers to complement their process discovery algorithms with properties that relate qualities of their inputs to those of their outputs. To this end, we distinguish four incremental stages for the development of such algorithms, along with concrete guidelines for the formulation of relevant properties and experimental validation. We use these stages to reflect on the state of the art, which shows the need to move forward in our thinking about algorithmic process discovery.

Event suffix and remaining time prediction are sequence to sequence learning tasks. They have wide applications in different areas such as economics, digital health, business process management and IT infrastructure monitoring. Timestamped event sequences contain ordered events which carry at least two attributes: the event's label and its timestamp. Suffix and remaining time prediction are about obtaining the most likely continuation of event labels and the remaining time until the sequence finishes, respectively. Recent deep learning-based works for such predictions are prone to potentially large prediction errors because of closed-loop training (i.e., the next event is conditioned on the ground truth of previous events) and open-loop inference (i.e., the next event is conditioned on previously predicted events). In this work, we propose an encoder-decoder architecture for open-loop training to advance the suffix and remaining time prediction of event sequences. To capture the joint temporal dynamics of events, we harness the power of adversarial learning techniques to boost prediction performance. We consider four real-life datasets and three baselines in our experiments. The results show improvements up to four times compared to the state of the art in suffix and remaining time prediction of event sequences, specifically in the realm of business process executions. We also show that the obtained improvements of adversarial training are superior compared to standard training under the same experimental setup.

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.

While requirements elicitation remains a key success factor for software projects, there is little empirical research on the elicitation methods. We focus on fit-gap analysis, a requirements elicitation technique that is common in practice, but hardly studied in requirements engineering research. Fit-gap analysis is a method for matching software products with the needs of customers, with the aim to identify needs that are supported as fits, and needs that are not as gaps. Through a grounded theory investigation of recording transcripts from fit-gap analysis sessions, we provide empirical knowledge about this elicitation technique. We determine and discuss the different categories of the topics contained in a fit-gap analysis. Additionally, as a first step toward assisting analysts in processing and exploring their analyses, we build and share a set of keywords and phrases that can help automatically identify those categories within the transcripts. We conduct an experiment for early validation, involving both students and practitioners, that determines the relative perceived importance of the identified fit-gap categories. Finally, we derive implications for research in the field that include our perspective on how tooling can assist analysts in fit-gap analysis.

Increasing customer demands and variability in today’s logistics networks force fleet operators to become more reliable and flexible in their operations. As modern-day fleets are well equipped with wireless sensing, processing, and communication devices, fleet operators could proactively respond to dynamic events. However, the use of real-time sensor data to achieve re-optimization is scarce. This observation raises the question of how logistics operators should incorporate the emerging track-and-trace services into their dynamic planning activities. In this paper, we propose a reference architecture that relies on both the Internet of Things and the Smart Logistics paradigms, and aims at enhancing the resilience of logistics networks. Since the decision of when to reschedule the network’s configurations remains nontrivial, we propose a hierarchical set of disruption handling systems to facilitate the trade-off between decision quality and response time. In our design, autonomous logistics agents can quickly anticipate on minor changes in their surroundings, while more severe disruptions require both more data and computational power in higher-level processing nodes (e.g., fog/cloud computing, machine learning, optimization algorithms). We illustrate the need of our architecture in the context of the dynamic vehicle routing problem.

Predictive process monitoring aims to predict future characteristics of an ongoing process case, such as case outcome or remaining timestamp. Recently, several predictive process monitoring methods based on deep learning such as Long Short-Term Memory or Convolutional Neural Network have been proposed to address the problem of next event prediction. However, due to insufficient training data or sub-optimal network configuration and architecture, these approaches do not generalize well the problem at hand. This paper proposes a novel adversarial training framework to address this shortcoming, based on an adaptation of Generative Adversarial Networks (GANs) to the realm of sequential temporal data. The training works by putting one neural network against the other in a two-player game (hence the adversarial nature) which leads to predictions that are indistinguishable from the ground truth. We formally show that the worst-case accuracy of the proposed approach is at least equal to the accuracy achieved in non-adversarial settings. From the experimental evaluation it emerges that the approach systematically outperforms all baselines both in terms of accuracy and earliness of the prediction, despite using a simple network architecture and a naive feature encoding. Moreover, the approach is more robust, as its accuracy is not affected by fluctuations over the case length.

Robotic Process Automation (RPA) is a technology for automating repetitive routines consisting of sequences of user interactions with one or more applications. In order to fully exploit the opportunities opened by RPA, companies need to discover which specific routines may be automated, and how. In this setting, this paper addresses the problem of analyzing User Interaction (UI) logs in order to discover routines where a user transfers data from one spreadsheet or (Web) form to another. The paper maps this problem to that of discovering data transformations by example - a problem for which several techniques are available. The paper shows that a naive application of a state-of-the-art technique for data transformation discovery is computationally inefficient. Accordingly, the paper proposes two optimizations that take advantage of the information in the UI log and the fact that data transfers across applications typically involve copying alphabetic and numeric tokens separately. The proposed approach and its optimizations are evaluated using UI logs that replicate a real-life repetitive data transfer routine., 8 pages, 5 figures. To be published in proceedings of AAAI-20 workshop on Intelligent Process Automation

This paper proposes an encoder-decoder architecture grounded on Generative Adversarial Networks (GANs), that generates a sequence of activities and their timestamps in an end-to-end way. GANs work well with differentiable data such as images. However, a suffix is a sequence of categorical items. To this end, we use the Gumbel-Softmax distribution to get a differentiable continuous approximation. The training works by putting one neural network against the other in a two-player game (hence the "adversarial" nature), which leads to generating suffixes close to the ground truth. From the experimental evaluation it emerges that the approach is superior to the baselines in terms of the accuracy of the predicted suffixes and corresponding remaining times, despite using a naive feature encoding and only engineering features based on control flow and events completion time., Comment: arXiv admin note: substantial text overlap with arXiv:2003.11268

Conformance checking encompasses a body of process mining techniques which aim to find and describe the differences between a process model capturing the expected process behavior and a corresponding event log recording the observed behavior. Alignments are an established technique to compute the distance between a trace in the event log and the closest execution trace of a corresponding process model. Given a cost function, an alignment is optimal when it contains the least number of mismatches between a log trace and a model trace. Determining optimal alignments, however, is computationally expensive, especially in light of the growing size and complexity of event logs from practice, which can easily exceed one million events with traces of several hundred activities. A common limitation of existing alignment techniques is the inability to exploit repetitions in the log. By exploiting a specific form of sequential pattern in traces, namely tandem repeats, we propose a novel approximate technique that uses pre- and post-processing steps to compress the length of a trace and recomputes the alignment cost while guaranteeing that the cost result never under-approximates the optimal cost. In an extensive empirical evaluation with 50 real-life model log pairs and against six state-of-the-art alignment techniques, we show that the proposed compression approach systematically outperforms the baselines by up to an order of magnitude in the presence of traces with repetitions, and that the cost over-approximation, when it occurs, is negligible.

This paper presents a tool, called Action Logger, for recording user interface (UI) logs, i.e., logs of user interactions with information systems. By generating output suitable for process mining, the tool aims to introduce process mining methods, techniques, and tools for supporting Robotic Process Automation (RPA) activities, e.g., robot discovery and implementation. Action Logger offers unique capabilities, including logging relevant user actions at a granularity level suitable for RPA, data-awareness, and context-independence.

Blockchain technology offers a sizable promise to rethink the way interorganizational business processes are managed because of its potential to realize execution without a central party serving as a single point of trust (and failure). To stimulate research on this promise and the limits thereof, in this article, we outline the challenges and opportunities of blockchain for business process management (BPM). We first reflect how blockchains could be used in the context of the established BPM lifecycle and second how they might become relevant beyond. We conclude our discourse with a summary of seven research directions for investigating the application of blockchain technology in the context of BPM.

An automated process discovery technique generates a process model from an event log recording the execution of a business process. For it to be useful, the generated process model should be as simple as possible, while accurately capturing the behavior recorded in and implied by the event log. Most existing automated process discovery techniques generate flat process models. When confronted to large and complex event logs, these approaches lead to overly complex or inaccurate process models. An alternative is to apply a divide-and-conquer approach by decomposing the process into stages and discovering one model per stage. It turns out however that existing divideand-conquer process discovery approaches often produce less accurate models than flat discovery techniques when applied to real-life event logs. This article investigates the hypothesis that the weaknesses of existing divide-andconquer approaches lies in the way stages are identified. The article contributes a technique to identify stages from an event log based on a modularity measure as well as a technique for discovering process models based on a given stage decomposition. An experimental evaluation shows that the proposed stage identification technique finds stages that are closer to those identified by human experts, while the proposed stage-based process discovery technique outperforms existing flat and divide-and-conquer discovery techniques with respect to well-accepted measures of accuracy and model complexity.

Predictive business process monitoring methods exploit historical process execution logs to provide predictions about running instances of a process. These predictions enable process workers and managers to preempt performance issues or compliance violations. A number of approaches have been proposed to predict quantitative process performance indicators for running instances of a process, including remaining cycle time, cost, or probability of deadline violation. However, these approaches adopt a black-box approach, insofar as they predict a single scalar value without decomposing this prediction into more elementary components. In this paper, we propose a white-box approach to predict performance indicators of running process instances. The key idea is to first predict the performance indicator at the level of activities, and then to aggregate these predictions at the level of a process instance by means of flow analysis techniques. The paper develops this idea in the context of predicting the remaining cycle time of ongoing process instances. The proposed approach has been evaluated on real-life event logs and compared against several baselines.

This thesis focuses on the development of luminescent semiconductor nanocrystals quantum dots (QDs) for photoinduced applications. QDs are promising nanomaterials with size-dependent optical properties and are attractive for applications in several fields.However, QDs are commonly hydrophobic and many interesting applications require their compatibility with water or at least with a polar environment, meaning a post-synthetic treatment is required to confer a different solubility.During these studies, a new method for transferring QDs from an apolar solvent to another one polar has been successfully developed, by exploiting lipoic acid, as a versatile surface capping agent. Moreover, lipoic acid is a chiral molecule so a possible induced dichroism effect has been also investigated, as well as its dependence on the size of nanocrystals.A major aim of this research was the development of QDs exhibiting reversible electronic energy transfer (REET). Such a process is a bidirectional energy transfer between the photoexcited QDs and suitable chromophoric units attached on their surface, whose most important consequence is the elongation of the luminescence lifetime of the QD. Strong experimental evidence for REET and accompanying modifications of the photophysical properties has been obtained. Such a process to our knowledge has never been observed in QD-based systems.Finally, a novel protocol for depositing charged QDs on a locally polarized glassy substrate has been developed in collaboration with Dr. Marc Dussauze of the University of Bordeaux., Cette thèse s’est focalisée sur le développement de nanocristaux semi-conducteurs luminescents, i.e. des boîtes quantiques (quantum dots, QDs), pour des applications photo-induites. Ces nanomatériaux possèdent des propriétés optiques très intéressantes qui dépendent de leur taille et sont prometteurs pour des applications dans divers domaines.Les QD sont généralement hydrophobes mais de nombreuses applications intéressantes requiert une compatibilité avec l'eau ou du moins avec un environnement polaire. Cela, nécessite donc un traitement post-synthétique afin de modifier leur solubilité.Au cours de cette thèse, une nouvelle méthode pour transférer les QDs d'un solvant apolaire vers un solvant polaire a été développée en les fonctionnalisant avec l'acide lipoïque, un tensioactif complexant.L'acide lipoïque est une molécule chirale générant donc un effet de dichroïsme induit qui a pu être étudier, ainsi que sa dépendance vis-à-vis la taille des nanocristaux.Un objectif clef de ces recherches était le développement de QDs présentant un transfert d'énergie électronique réversible (REET). Il s’agit d’un transfert d'énergie bidirectionnel entre les QDs photoexcités et des unités chromophores appropriées fixées sur leur surface, dont la conséquence la plus importante est l'allongement de la durée de vie de luminescence du QD.Enfin, un nouveau protocole pour le dépôt de QDs chargés sur un substrat vitreux localement polarisé a été développé en collaboration avec le Dr Marc Dussauze de l'Université de Bordeaux.

Process mining allows analysts to exploit logs of historical executions of business processes to extract insights regarding the actual performance of these processes. One of the most widely studied process mining operations is automated process discovery. An automated process discovery method takes as input an event log, and produces as output a business process model that captures the control-flow relations between tasks that are observed in or implied by the event log. Various automated process discovery methods have been proposed in the past two decades, striking different tradeoffs between scalability, accuracy and complexity of the resulting models. However, these methods have been evaluated in an ad-hoc manner, employing different datasets, experimental setups, evaluation measures and baselines, often leading to incomparable conclusions and sometimes unreproducible results due to the use of closed datasets. This article provides a systematic review and comparative evaluation of automated process discovery methods, using an open-source benchmark covering twelve publicly-available real-life event logs and eight quality metrics. The results highlight gaps and unexplored tradeoffs in the field, including the lack of scalability of several methods and a strong divergence in their performance with respect to the different quality metrics used.

This thesis focuses on the development of luminescent semiconductor nanocrystals quantum dots (QDs) for photoinduced applications. QDs are promising nanomaterials with size-dependent optical properties and are attractive for applications in several fields. However, QDs are commonly hydrophobic and many interesting applications require their compatibility with water or at least with a polar environment, meaning a post-synthetic treatment is required to confer a different solubility. During these studies, a new method for transferring QDs from an apolar solvent to another one polar has been successfully developed, by exploiting lipoic acid, as a versatile surface capping agent. Moreover lipoic acid is a chiral molecule so a possible induced dichroism effect, which has been investigated, as well as its dependence on the size of nanocrystals. A major aim of this research was the development of QDs exhibiting reversible electronic energy transfer (REET). Such a process is a bidirectional energy transfer between the photoexcited QDs and suitable chromophoric units attached on their surface, where the most important consequence is the elongation of the luminescence lifetime of the QD. Strong experimental evidence for REET and accompanying modifications of the photophysical properties has been obtained. Such a process to our knowledge has never been observed in QD-based systems. Finally, a novel protocol for depositing charged QDs on a locally polarized glassy substrate has been developed in collaboration with Dr. Marc Dussauze of the University of Bordeaux.

Business process performance may be affected by a range of factors, such as the volume and characteristics of ongoing cases or the performance and availability of individual resources. Event logs collected by modern information systems provide a wealth of data about the execution of business processes. However, extracting root causes for performance issues from these event logs is a major challenge. Processes may change continuously due to internal and external factors. Moreover, there may be many resources and case attributes influencing performance. This paper introduces a novel approach based on time series analysis to detect cause-effect relations between a range of business process characteristics and process performance indicators. The scalability and practical relevance of the approach has been validated by a case study involving a real-life insurance claims handling process.

Context - Healthcare processes such as patient flows in the emergency department, are notoriously complex because of their high degree of variability. Yet, hospital stakeholders need to understand differences between variants of these processes, e.g. between different patient cohorts or different times of the day, to offer a consistent and reliable service to their patients. Objective - Analysis of these processes through classic process mining techniques is hampered by the inherently high variability of these processes. Method - This paper contributes two novel visualisation and animation techniques, specifically catered for highly-varied business processes, which can distill both static and dynamic differences between variants of the same business process, using their execution logs. Results - We validate these techniques via two case studies with two Australian hospitals and obtained insights on the differences that underpin patient flows for various patient cohorts through the Emergency Department. Conclusion - The Configurable Model Visualisation technique provides a birds-eye view of similarities and differences be- tween process variants while the Log Animation tool addresses behavioural comparison and is unique in its ability to display concurrently the processing of multiple process variants through a single BMPN model.

Detecting concurrency relations between events is a fundamental primitive underpinning a range of process mining techniques. Existing approaches to this problem identify concurrency 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.

Business process deviance refers to the phenomenon whereby a subset of the executions of a business process deviate, in a negative or positive way, with respect to its expected or desirable outcomes. Deviant executions of a business process include those that violate compliance rules, or executions that undershoot or exceed performance targets. Deviance mining is concerned with uncovering the reasons for deviant executions by analyzing business process event logs. This article provides a systematic review and comparative evaluation of deviance mining approaches based on a family of data mining techniques known as sequence classification. Using real-life logs from multiple domains, we evaluate a range of feature types and classification methods in terms of their ability to accurately discriminate between normal and deviant executions of a process. We also analyze the interestingness of the rule sets extracted using different methods. We observe that feature sets extracted using pattern mining techniques only slightly outperform simpler feature sets based on counts of individual activity occurrences in a trace.

Predictive business process monitoring is a family of techniques to determine how running instances of a business process are likely to unfold in the future. Techniques in this space differ according to their object of prediction. Some predict whether or not a running process instance will fulfill a compliance rule, others predict whether or not a given activity will occur, while others predict the remaining execution time. These and other predictive process monitoring problems are subsumed by the problem of predicting the remaining sequence of activities of a given process instance. In this paper, we tackle this latter problem using two alternative approaches. In the first one, we statically construct a transition system from an event log of completed process instances and annotate each transition with a probability calculated using a k-nearest neighbors classifier. At runtime, we map the (incomplete) trace of a process instance to a state in the transition system. To predict the remaining activity sequence of a process instance, we calculate a highest-probability path starting from the current state. In the second approach, we treat the problem of activity sequence prediction as a structured output prediction problem and apply recurrent neural networks. The accuracy of the two proposed approaches is evaluated on real-life and synthetic datasets and compared against an existing baseline technique.

This paper presents a technique for the automated removal of noise from process execution logs. Noise is the result of data quality issues such as logging errors and manifests itself in the form of infrequent process behavior. The proposed technique generates an abstract representation of an event log as an automaton capturing the direct follows relations between event labels. This automaton is then pruned from arcs with low relative frequency and used to remove from the log those events not fitting the automaton, which are identified as outliers. The technique has been extensively evaluated on top of various auto- mated process discovery algorithms using both artificial logs with different levels of noise, as well as a variety of real-life logs. The results show that the technique significantly improves the quality of the discovered process model along fitness, appropriateness and simplicity, without negative effects on generalization. Further, the technique scales well to large and complex logs.

The management of risks in business processes has been a subject of active research in the past few years. Many benefits can potentially be obtained by integrating the two traditionally-separated fields of risk management and business process management, including the ability to minimize risks in business processes (by design) and to mitigate risks at run time. In the past few years, an increasing amount of research aimed at delivering such an integrated system has been proposed. However, these research efforts vary in terms of their scope, goals, and functionality. Through systematic collection and evaluation of relevant literature, this paper compares and classifies current approaches in the area of risk-aware business process management in order to identify and explain relevant research gaps. The process through which relevant literature is collected, filtered, and evaluated is also detailed.

Process-Aware Information Systems (PAISs) support executions of operational processes that involve people, resources, and software applications on the basis of process models. Process models describe vast, often infinite, amounts of process instances, i.e., workflows supported by the systems. With the increasing adoption of PAISs, large process model repositories emerged in companies and public organizations. These repositories constitute significant information resources. Accurate and efficient retrieval of process models and/or process instances from such repositories is interesting for multiple reasons, e.g., searching for similar models/instances, filtering, reuse, standardization, process compliance checking, verification of formal properties, etc. This paper proposes a technique for indexing process models that relies on their alternative representations, called untanglings. We show the use of untanglings for retrieval of process models based on process instances that they specify via a solution to the total executability problem. Experiments with industrial process models testify that the proposed retrieval approach is up to three orders of magnitude faster than the state of the art.

Encompasses the whole BPM lifecycle, including process identification, modelling, analysis, redesign, automation and monitoring Class-tested textbook complemented with additional teaching material on the accompanying website Covers both relevant conceptual background, industrial standards and actionable skills Business Process Management (BPM) is the art and science of how work should be performed in an organization in order to ensure consistent outputs and to take advantage of improvement opportunities, e.g. reducing costs, execution times or error rates. Importantly, BPM is not about improving the way individual activities are performed, but rather about managing entire chains of events, activities and decisions that ultimately produce added value for an organization and its customers. This textbook encompasses the entire 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 100 hands-on exercises – many with solutions – as well as numerous suggestions for further reading. 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 fundamentals-of-bpm.org.

As business process management technology matures, organisations acquire more and more business process models. The management of the resulting collections of process models poses real challenges. One of these challenges concerns model retrieval where support should be provided for the formulation and efficient execution of business process model queries. As queries based on only structural information cannot deal with all querying requirements in practice, there should be support for queries that require knowledge of process model semantics. In this paper we formally define a process model query language that is based on semantic relationships between tasks in process models and is independent of any particular process modelling notation.

This month, Jan Recker turns his attention to the technological side of BPM research and education. He engaged in a collaboration with two colleagues at Queensland University, Dr Marcello La Rosa and Eike Bernhard, on an initiative on the development of an advanced BPM technology - an Advanced Process Model Repository called Apromore. In this Column, they use the example of Apromore to showcase how BPM technologies are conceived, designed, developed and applied.

International audience; Organizations put considerable e_orts in describing their operations in the form of business process models. Such models can be applied, for communication purposes, as blueprints for performance improvement projects, to guide the development of IT systems, and to check compliance with relevant quality standards. As organizations mature in their uptake of business process management, this leads to increasing numbers of business process models within organizations. The availability of large process model collections forms both a challenge and an opportunity. The challenge is how to keep track of the various models within the same collection, as they may refer to each other, mutually overlap, supersede one another, and evolve. The opportunity lies in exploiting this potentially rich source of information to create new models and support application scenarios that were unforeseen at the time of their conception. In the light of this background, the APROMORE initiative(1) was started in 2009 with the aim to catalyze research on the management of process model collections. The APROMORE initiative kicked o_ as a collaboration among four academic institutions: Queensland University of Technology (Australia), Eindhoven University of Technology (Netherlands), University of Tartu (Estonia) and Humboldt University of Berlin (Germany). Later on, other universities joined the initiative, including Joseph Fourier University of Grenoble (France), University of Karshrule (Germany) and Vienna University of Economics (Austria). Moreover, Suncorp-Metway, one of the major insurance providers in Australia, sponsored the initiative within the context of a project co-funded with the Australian Research Council, which aims to achieve standardization and reuse in large process model collections. The main objective of the APROMORE initiative is to design and develop the open source platform APROMORE(2) [LRRvdA+11, FRS+10]. Beyond the typical features of a process model repository (e.g. model import and export), the APROMORE platform aims to provide various advanced features speci_cally designed for the management of large process model collections. These include features to _lter the repository and perform searches (e.g. by looking for similarities to an input model), to enhance the presentation of process model collections (e.g. by refactoring the process models), to design new process models (e.g by merging existing models that are similar to each other) and to evaluate existing ones (e.g. along their soundness property). At the time of writing, some of these features have already been implemented (e.g. similarity search and merging) while others are available as separate prototypes (e.g. querying and clone detection) which will be incorporated in the platform in the future. (1) : www.apromore.org (2) : http://code.google.com/p/apromore

Previous research has put forward a number of properties of business process models that have an impact on their understandability. Two such properties are compactness and(block-)structuredness. What has not been sufficiently appreciated at this point is that these desirable properties may be at odds with one another. This paper presents the results of a two-pronged study aimed at exploring the trade-off between compactness and structuredness of process models. The first prong of the study is a comparative analysis of the complexity of a set of unstructured process models from industrial practice and of their corresponding structured versions. The second prong is an experiment wherein a cohort of students was exposed to semantically equivalent unstructured and structured process models. The key finding is that structuredness is not an absolute desideratum vis-a-vis for process model understandability. Instead, subtle trade-offs between structuredness and other model properties are at play.

As the service-oriented architecture paradigm has become ever more popular, different standardization efforts have been proposed by various consortia to enable interaction among heterongeneous environments through this paradigm. This chapter will overview the most prevalent of these SOA approaches. It will first show how technical services can be described, how they can interact with each other and be discovered by users. Next, the chapter will present different standards to facilitate service composition and to design service-oriented environments in light of a universal understanding of service orientation. The chapter will conclude with a summary and a discussion on the limitations of the reviewed standards along their ability to describe service properties. This paves the way to the next chapters where the USDL standard will be presented, which aims to lift such limitations.

As business process management technology matures, organisations acquire more and more business process models. The resulting collections can consist of hundreds, even thousands of models and their management poses real challenges. One of these challenges concerns model retrieval where support should be provided for the formulation and efficient execution of business process model queries. As queries based on only structural information cannot deal with all querying requirements in practice, there should be support for queries that require knowledge of process model semantics. In this paper we formally define a process model query language that is based on semantic relationships between tasks. This query language is independent of the particular process modelling notation used, but we will demonstrate how it can be used in the context of Petri nets by showing how the semantic relationships can be determined for these nets in such a way that state space explosion is avoided as much as possible. An experiment with three large process model repositories shows that queries expressed in our language can be evaluated efficiently.

As the Service-oriented architecture paradigm has become ever more popular, different standardization efforts have been proposed by various consortia to enable interaction among heterogeneous environments through this paradigm. This chapter will overview the most prevalent of these SOA Efforts. It will first show how technical services can be described, how they can interact with each other and be discovered by users. Next, the chapter will present different standards to facilitate service composition and to design service-oriented environments in light of a universal understanding of service orientation. The chapter will conclude with a summary and a discussion on the limitations of the reviewed standards along their ability to describe service properties. This paves the way to the next chapters where the USDL standard will be presented, which aim to lift such limitations.

This paper proposes a novel approach for identifying risks in executable business processes and detecting them at run time. The approach considers risks in all phases of the business process management lifecycle, and is realized via a distributed, sensor-based architecture. At design-time, sensors are defined to specify risk conditions which when fulfilled, are a likely indicator of faults to occur. Both historical and current execution data can be used to compose such conditions. At run-time, each sensor independently notifies a sensor manager when a risk is detected. In turn, the sensor manager interacts with the monitoring component of a process automation suite to prompt the results to the user who may take remedial actions. The proposed architecture has been implemented in the YAWL system and its performance has been evaluated in practice.

As a result of the growing adoption of Business Process Management (BPM) technology different stakeholders need to understand and agree upon the process models that are used to configure BPM systems. However, BPM users have problems dealing with the complexity of such models. Therefore, the challenge is to improve the comprehension of process models. While a substantial amount of literature is devoted to this topic, there is no overview of the various mechanisms that exist to deal with managing complexity in (large) process models. It is thus hard to obtain comparative insight into the degree of support offered for various complexity reducing mechanisms by state-of-the-art languages and tools. This paper focuses on complexity reduction mechanisms that affect the abstract syntax of a process model, i.e. the structure of a process model. These mechanisms are captured as patterns, so that they can be described in their most general form and in a language- and tool-independent manner. The paper concludes with a comparative overview of the degree of support for these patterns offered by state-of-the-art languages and language implementations.

As order dependencies between process tasks can get complex, it is easy to make mistakes in process model design, especially behavioral ones such as deadlocks. Notions such as soundness formalize behavioral errors and tools exist that can identify such errors. However these tools do not provide assistance with the correction of the process models. Error correction can be very challenging as the intentions of the process modeler are not known and there may be many ways in which an error can be corrected. We present a novel technique for automatic error correction in process models based on simulated annealing. Via this technique a number of process model alternatives are identified that resolve one or more errors in the original model. The technique is implemented and validated on a sample of industrial process models. The tests show that at least one sound solution can be found for each input model and that the response times are short.

In recent years several scientific Workflow Management Systems (WfMSs) have been developed with the aim to automate large scale scientific experiments. As yet, many offerings have been developed, but none of them has been promoted as an accepted standard. In this paper we propose a pattern-based evaluation of three among the most widely used scientific WfMSs: Kepler, Taverna and Triana. The aim is to compare them with traditional business WfMSs, emphasizing the strengths and deficiencies of both systems. Moreover, a set of new patterns is defined from the analysis of the three considered systems.

Vendors provide reference process models as consolidated, off-the-shelf solutions to capture best practices in a given industry domain. Customers can then adapt these models to suit their specific requirements. Traditional process flexibility approaches facilitate this operation, but do not fully address it as they do not sufficiently take controlled change guided by vendors’ reference models into account. This tension between the customer’s freedom of adapting reference models, and the ability to incorporate with relatively low effort vendor-initiated reference model changes, thus needs to be carefully balanced. This paper introduces process extensibility as a new paradigm for customising reference processes and managing their evolution over time. Process extensibility mandates a clear recognition of the different responsibilities and interests of reference model vendors and consumers, and is concerned with keeping the effort of customer-side reference model adaptations low while allowing sufficient room for model change., Enterprise Modelling and Information Systems Architectures, Vol 5, No 3 (2010)

Recent years have seen an increased uptake of business process management technology in industries. This has resulted in organizations trying to manage large collections of business process models. One of the challenges facing these organizations concerns the retrieval of models from large business process model repositories. For example, in some cases new process models may be derived from existing models, thus finding these models and adapting them may be more effective and less error-prone than developing them from scratch. Since process model repositories may be large, query evaluation may be time consuming. Hence, we investigate the use of indexes to speed up this evaluation process. To make our approach more applicable, we consider the semantic similarity between labels. Experiments are conducted to demonstrate that our approach is efficient.

Recent years have seen an increased uptake of business process management technology in industries. This has resulted in organizations trying to manage large collections of business process models. One of the challenges facing these organizations concerns the retrieval of models from large business process model repositories. For example, in some cases new process models may be derived from existing models, thus finding these models and adapting them may be more effective than developing them from scratch. As process model repositories may be large, query evaluation may be time consuming. Hence, we investigate the use of indexes to speed up this evaluation process. Experiments are conducted to demonstrate that our proposal achieves a significant reduction in query evaluation time.

The YAWL system is structured as a service-oriented architecture. It is composed of an extensible set of YAWL Services [1], each of which is deployed at a certain endpoint and offers one or multiple interfaces. Some of these services are userfacing, meaning that they offer interfaces to end users, while others offer interfaces to applications or other services.

This chapter describes how the YAWL meta-model was extended to support the definition of variation points. These variation points can be used to describe different variants of a YAWL process model in a unified, configurable model. The model can then be configured to suit the needs of specific settings, e.g. for a new organization of project.

This appendix describes the Order Fulfillment process followed by a fictitious company named Genko Oil. The process is freely inspired by the VICS (Voluntary Inter-industry Commerce Solutions) reference model1 and provides a demonstration of YAWL’s capabilities in modelling complex control-flow, data and resourcing requirements.

A configurable process model describes a family of similar process models in a given domain. Such a model can be configured to obtain a specific process model that is subsequently used to handle individual cases, for instance, to process customer orders. Process configuration is notoriously difficult as there may be all kinds of interdependencies between configuration decisions.} In fact, an incorrect configuration may lead to behavioral issues such as deadlocks and livelocks. To address this problem, we present a novel verification approach inspired by the ``operating guidelines'' used for partner synthesis. We view the configuration process as an external service, and compute a characterization of all such services which meet particular requirements using the notion of configuration guideline. As a result, we can characterize all feasible configurations (i.\,e., configurations without behavioral problems) at design time, instead of repeatedly checking each individual configuration while configuring a process model.