Marcello La Rosa

University of Melbourne | MSD · School of Computing and Information Systems

Purpose Selecting which processes to improve plays a critical role in the first phase of the business process management lifecycle, but it is a step with known pitfalls. Decision-makers rely on subjective criteria and their knowledge of the alternative processes put forward for selection is often inconsistent. This leads to poor quality decision-ma...

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...

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 possibl...

ProLift is a Web-based tool that uses causal machine learning, specifically uplift trees, to discover rules for optimizing business processes based on execution data (event logs). ProLift allows users to upload an event log, to specify case treatments and case outcomes, and to visualize treatment rules that increase the probability of positive case...

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 progre...

User interaction logs allow us to analyze the execution of tasks in a business process at a finer level of granularity than event logs extracted from enterprise systems. The fine-grained nature of user interaction logs open up a number of use cases. For example, by analyzing such logs, we can identify best practices for executing a given task in a...

Process Mining is an active research domain and has been applied to understand and improve business processes. While significant research has been conducted on the development and improvement of algorithms, evidence on the application of Process Mining in organisations has been far more limited. In particular, there is limited understanding of the...

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 exh...

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 progre...

Augmented Business Process Management Systems (ABPMSs) are an emerging class of process-aware information systems that draws upon trustworthy AI technology. An ABPMS enhances the execution of business processes with the aim of making these processes more adaptable, proactive, explainable, and context-sensitive. This manifesto presents a vision for...

Reducing cycle time is a recurrent concern in the field of business process management. Depending on the process, various interventions may be triggered to reduce the cycle time of a case, for example, using a faster shipping service in an order-to-delivery process or calling a customer to obtain missing information rather than waiting passively. H...

Process models automatically discovered from event logs represent business process behavior in a compact graphical way. To compare process variants, e.g., to explore how the system’s behavior changes over time or between customer segments, analysts tend to visually compare conceptual process models discovered from different “slices” of the event lo...

Process mining is an active research domain and has been applied to understand and improve business processes. While significant research has been conducted on the development and improvement of algorithms, evidence on the application of process mining in organizations has been far more limited. In particular, there is limited understanding of the...

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 consumi...

The problem of automatically discovering business process models from event logs has been intensely investigated in the past two decades, leading to a wide range of approaches that strike various trade-offs between accuracy, model complexity, and execution time. A few studies have suggested that the accuracy of automated process discovery approache...

Die Prozessidentifikation (engl.: process identification) zielt darauf ab, die Geschäftsprozesse eines Unternehmens systematisch zu definieren und klare Kriterien für die Auswahl zu verbessernder Prozesse festzulegen. Das Ergebnis der Prozessidentifikation ist eine Prozessarchitektur, die die Prozesse und ihre Beziehungen darstellt. Diese Prozessar...

In den vorangegangenen Kapiteln haben wir gelernt, wie man konzeptionelle Prozessmodelle erstellt und diese für Dokumentations- und AnalysezweckeProzessimplementierung einsetzt. Diese Modelle sind aufgrund ihres Zwecks bewusst abstrakt, d. h. sie enthalten keine Details zur technischen Umsetzung. Das bedeutet, dass konzeptionelle Prozessmodelle sys...

In den letztenProzesserhebung beiden Kapiteln haben wir uns mit der Frage beschäftigt, wie man Prozessmodelle erstellt. Dabei sind wir meist von der Annahme ausgegangen, dass es eine textbasierte Beschreibung des Prozesses gibt. In der Praxis ist das aber nur selten der Fall, zumindest wenn ein Prozessmodell zum allerersten Mal erstellt wird. Es gi...

In den vorangegangenen Kapiteln haben wir gelernt, wie man qualitative und quantitative Analyseverfahren einsetzt, um Probleme in bestehenden Geschäftsprozessen zu identifizieren. Wir haben auch gesehen, dass viele Prozesse in der Praxis Probleme mit der Durchlaufzeiteffizienz haben. Verschiedene Heuristiken unterstreichen das Potenzial, das im Ein...

DieProzessanalyse qualitative AnalyseProzessanalyse ist ein wertvolles Instrument, um systematische Einblicke in einen Prozess zu gewinnen. Die Ergebnisse der qualitativen Analyse sind jedoch manchmal nicht präzise genug, um eine solide Entscheidungsgrundlage zu schaffen. Denken Sie an den Prozessverantwortlichen des Vermietungsprozesses von BuildI...

Geschäftsprozessmanagement (engl.: business process management (BPM)) ist die Kunst und Wissenschaft, die Arbeit in einer Organisation so zu gestalten, dass konsistente Ergebnisse sichergestellt und Verbesserungspotenziale genutzt werden. In diesem Zusammenhang kann der Begriff „Verbesserung“ in Abhängigkeit von den Zielen der Organisation untersch...

In diesem Kapitel werden wir weiter vertiefen, wie komplexe Geschäftsprozessemit BPMN modelliert werden können. Die hier vorgestellten Elemente bauen auf demerworbenen Wissen von Kap. 3 auf. Wir werden insbesondere näher auf Aktivitäten, Ereignisse und Gatter eingehen. Wir werden Aktivitäten erweitern, um komplexere Formen von Nacharbeit und Wieder...

Sobaldwir einen neu gestaltetenGeschäftsprozess implementiert und eingeführt haben, kann es passieren, dass der neue Prozess nicht unseren Erwartungen entspricht. Beispielsweise können bestimmte Arten von unvorhergesehenen Ausnahmen auftreten, die Bearbeitungszeit einiger Aufgaben kann aufgrund dieser Ausnahmen viel höher sein als erwartet, und War...

Modelle von Geschäftsprozessen sind in verschiedenen Phasen des BPM-Lebenszyklus von Bedeutung. Bevor wir mit der Modellierung eines Prozesses beginnen, ist es wichtig zu verstehen, warum wir diesen modellieren. Die Modelle, die wir erstellen, werden je nach dem Zweck, für den wir sie erstellen, recht unterschiedlich aussehen. Es gibt viele Gründe,...

Die Analyse von Geschäftsprozessen ist sowohl eine Kunst als auch eine Wissenschaft. In dieser Hinsicht ist die qualitative Analyse die künstlerische Seite der Prozessanalyse. Wie in der bildenden Kunst, zum Beispiel in der Malerei, gibt es nicht nur eine einzige Möglichkeit, eine gute Prozessanalyse zu erstellen, sondern eine Reihe von Prinzipien...

Die gründliche Analyse eines Geschäftsprozesses bringt eine ganze Reihe von Problemen zu Tage. Beispielsweise verlangsamen Engpässe den Prozess oder die Kosten für die Ausführung sind zu hoch. Diese Probleme deuten auf verschiedene Möglichkeiten zur Prozessverbesserung hin. Das Problem ist jedoch, dass die Verbesserung oft nicht systematisch angega...

wir eine Reihe vonMethoden und verwandten Techniken für die Identifizierung, Entdeckung, Analyse, Neugestaltung, Implementierung und Überwachung von Geschäftsprozessen vorgestellt. Entlang der sechs Phasen des BPM-Lebenszyklus haben wir auch Softwarewerkzeuge und -systeme besprochen, die uns bei der Anwendung dieser Methoden für das effektive Manag...

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...

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 consumi...

Robotic process automation (RPA) is an emerging technology that allows organizations automating repetitive clerical tasks by executing scripts that encode sequences of fine-grained interactions with Web and desktop applications. Examples of clerical tasks include opening a file, selecting a field in a Web form or a cell in a spreadsheet, and copy-p...

Reducing cycle time is a recurrent concern in the field of business process management. Depending on the process, various interventions may be triggered to reduce the cycle time of a case, for example, using a faster shipping service in an order-to-delivery process or giving a phone call to a customer to obtain missing information rather than waiti...

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 pr...

Purpose Capture, consumption and use of person-centred information presents challenges for hospitals when operating within the scope of limited resources and the push for organisational routines and efficiencies. This paper explores these challenges for patients with Acute Coronary Syndrome (ACS) and the examination of information that supports suc...

Business Process Management is a boundary-spanning discipline that aligns operational capabilities and technology to design and manage business processes. The Digital Transformation has enabled human actors, information systems, and smart products to interact with each other via multiple digital channels. The emergence of this hyper-connected world...

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 pr...

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 timestam...

It is common for business processes to exhibit a high degree of internal heterogeneity, in the sense that the executions of the process differ widely from each other due to contextual factors, human factors, or deliberate business decisions. For example, a quote-to-cash process in a multinational company is typically executed differently across dif...

This book constitutes the proceedings of the 33rd International Conference on Advanced Information Systems Engineering, CAiSE 2021, which was held online during June 28-July 2, 2021. The conference was planned to take place in Melbourne, Australia, and changed to an online format due to the COVID-19 pandemic. The papers included in these proceedin...

Dieses Lehrbuch umfasst den gesamten Lebenszyklus des Geschäftsprozessmanagements (BPM), von der Prozessidentifikation bis zur Prozessüberwachung, und die entsprechenden Schritte der Erhebung, Analyse, Verbesserung und Implementierung von Prozessen. Dabei werden Konzepte, Methoden und Werkzeuge der Betriebswirtschaftslehre, der Informatik und der I...

Robotic Process Automation (RPA) is a technology to develop software bots that automate repetitive sequences of interactions between users and software applications (a.k.a. routines). To take full advantage of this technology, organizations need to identify and to scope their routines. This is a challenging endeavor in large organizations, as routi...

This paper proposes an approach to analyze an event log of a business process in order to generate case-level recommendations of treatments that maximize the probability of a given outcome. Users classify the attributes in the event log into controllable and non-controllable, where the former correspond to attributes that can be altered during an e...

This paper proposes an approach to analyze an event log of a business process in order to generate case-level recommendations of treatments that maximize the probability of a given outcome. Users classify the attributes in the event log into controllable and non-controllable, where the former correspond to attributes that can be altered during an e...

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...

A plethora of algorithms for automatically discovering process models from event logs has emerged. The discovered models are used for analysis and come with a graphical flowchart-like representation that supports their comprehension by analysts. According to the Occam’s Razor principle, a model should encode the process behavior with as few constru...

This paper contributes an approach for automatically correcting “same-timestamp” errors in business process event logs. These errors consist in multiple events exhibiting the same timestamp within a given process instance. Such errors are common in practice and can be due to the logging granularity or the performance load of the logging system. Ana...

Robotic Process Automation (RPA) is a technology to develop software bots that automate repetitive sequences of interactions between users and software applications (a.k.a. routines). To take full advantage of this technology, organizations need to identify and to scope their routines. This is a challenging endeavor in large organizations, as routi...

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 t...

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...

Automated process discovery techniques allow us to generate a process model from an event log. The quality of automatically discovered process models can be assessed with respect to several criteria, including fitness, which captures the degree to which the process model is able to recognize the traces in the event log, and precision, which capture...

Comparing business process variants using event logs is a common use case in process mining. Existing techniques for process variant analysis detect statistically-significant differences between variants at the level of individual entities (such as process activities) and their relationships (e.g. directly-follows relations between activities). Thi...

Business processes are prone to unexpected changes, as process workers may suddenly or gradually start executing a process differently in order to adjust to changes in workload, season, or other external factors. Early detection of business process changes enables managers to identify and act upon changes that may otherwise affect process performan...

Given a model of the expected behavior of a business process and given an event log recording its observed behavior, the problem of business process conformance checking is that of identifying and describing the differences between the process model and the event log. A desirable feature of a conformance checking technique is that it should identif...

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...

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 cl...

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...

Process workers may vary the normal execution of a business process to adjust to changes in their operational environment, e.g., changes in workload, season, or regulations. Changes may be simple, such as skipping an individual activity, or complex, such as replacing an entire procedure with another. Over time, these changes may negatively affect p...

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...

This book constitutes the thoroughly refereed proceedings of the CAiSE Forum 2020 which was held as part of the 32nd International Conference on Advanced Information Systems Engineering, CAiSE 2020, in June 2020. The conference was held virtually due to the COVID-19 pandemic. The CAiSE Forum is a place within the CAiSE conference for presenting and...

This paper presents Robidium: a tool that discovers automatable routine tasks from User Interactions (UI) logs and generates Robotic Process Automation (RPA) scripts to automate such routines. Unlike record-and-replay features provided by commercial RPA tools, Robidium may take as input an UI log that is not specifically recorded to capture a pre-i...

Comparing business process variants using event logs is a common use case in process mining. Existing techniques for process variant analysis detect statistically-significant differences between variants at the level of individual entities (such as process activities) and their relationships (e.g. directly-follows relations between activities). Thi...

Automated process discovery techniques enable users to generate business process models from event logs extracted from enterprise information systems. Traditional techniques in this field generate procedural process models (e.g., in the BPMN notation). When dealing with highly variable processes, the resulting procedural models are often too comple...

Process variant analysis aims at identifying and addressing the differences existing in a set of process executions enacted by the same process model. A process model can be executed differently in different situations for various reasons, e.g., the process could run in different locations or seasons, which gives rise to different behaviors. Having...

Given a model of the expected behavior of a business process and an event log recording its observed behavior, the problem of business process conformance checking is that of identifying and describing the differences between the model and the log. A desirable feature of a conformance checking technique is to identify a minimal yet complete set of...

Process discovery aims at automatically creating process models on the basis of event data captured during the execution of business processes. Process discovery algorithms tend to use all of the event data to discover a process model. This attitude sometimes leads to discover imprecise and/or complex process models that may conceal important infor...

Process mining aims to understand the actual behavior and performance of business processes from event logs recorded by IT systems. A key requirement is that every event in the log must be associated with a unique case identifier (e.g., the order ID in an order-to-cash process). In reality, however, this case ID may not always be present, especiall...

Organizations can benefit from the use of practices, techniques, and tools from the area of business process management. Through the focus on processes, they create process models that require management, including support for versioning, refactoring and querying. Querying thus far has primarily focused on structural properties of models rather tha...

Organizations can benefit from the use of practices, techniques, and tools from the area of business process management. Through the focus on processes, they create process models that require management, including support for versioning, refactoring and querying. Querying thus far has primarily focused on structural properties of models rather tha...

Predictive business process monitoring methods exploit historical process execution logs to generate predictions about running instances (called cases) of a business process, such as the prediction of the outcome, next activity, or remaining cycle time of a given process case. These insights could be used to support operational managers in taking r...