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This open access book constitutes revised selected papers from the International Workshops held at the Third International Conference on Process Mining, ICPM 2021, which took place in Eindhoven, The Netherlands, during October 31–November 4, 2021. The conference focuses on the area of process mining research and practice, including theory, algorithmic challenges, and applications. The co-located workshops provided a forum for novel research ideas. The 28 papers included in this volume were carefully reviewed and selected from 65 submissions. They stem from the following workshops: 2nd International Workshop on Event Data and Behavioral Analytics (EDBA) 2nd International Workshop on Leveraging Machine Learning in Process Mining (ML4PM) 2nd International Workshop on Streaming Analytics for Process Mining (SA4PM) 6th International Workshop on Process Querying, Manipulation, and Intelligence (PQMI) 4th International Workshop on Process-Oriented Data Science for Healthcare (PODS4H) 2nd International Workshop on Trust, Privacy, and Security in Process Analytics (TPSA) One survey paper on the results of the XES 2.0 Workshop is included.

This open access book presents a number of case studies on digital transformation in Norway, one of the fore-runners in the digital progress index established by the European Commission in 2020. They explore the process of adoption, diffusion and value generation from digital technologies, and how the use of different digital solutions has enabled Norwegian enterprises to digitally transform their operations and business models. The book starts with an introductory chapter summarizing a vast body of literature in order to synthesize what is already known about digital transformation before exploring the Norwegian context in more detail. Then a series of case studies from the private and public sector in Norway is presented. They document a process perspective which describes the sequence of events during and after adoption of digital solutions, as well as the types of business value that were realized. Through these single studies, the process of digital transformation is illustrated, a number of key findings highlighted, and eventually theoretical and practical recommendations based on these cases emphasized. The book closes with a brief overview of some emerging technologies, and comments on how they are likely to change different sectors. Digital transformation has been one of the priority areas for the Norwegian government over the past years and puts Norwegian enterprises upfront in adopting novel technologies and utilizing them for achieving organizational goals. This experience accumulated over the years makes the Norwegian context a particularly interesting one in understanding how private and public organizations make use of new digital solutions, what lessons can be learnt during the process, and what are some of the key success and failure factors. This way the book is written for practitioners who are currently involved in digital transformation projects in their organizations, researchers of information systems and management, as well as master students in degrees of informatics and technology management.

This is an open access book. This book comprises all the single courses given as part of the First Summer School on Process Mining, PMSS 2022, which was held in Aachen, Germany, during July 4-8, 2022. This volume contains 17 chapters organized into the following topical sections: Introduction; process discovery; conformance checking; data preprocessing; process enhancement and monitoring; assorted process mining topics; industrial perspective and applications; and closing.

This open access book shows the breadth and various facets of e-Science, while also illustrating their shared core. Changes in scientific work are driven by the shift to grid-based worlds, the use of information and communication systems, and the existential infrastructure, which includes global collaboration. In this context, the book addresses emerging issues such as open access, collaboration and virtual communities and highlights the diverse range of developments associated with e-Science. As such, it will be of interest to researchers and scholars in the fields of information technology and knowledge management.

This open access book provides a broad range of insights on market engineering and information management. It covers topics like auctions, stock markets, electricity markets, the sharing economy, information and emotions in markets, smart decision-making in cities and other systems, and methodological approaches to conceptual modeling and taxonomy development. Overall, this book is a source of inspiration for everybody working on the vision of advancing the science of engineering markets and managing information for contributing to a bright, sustainable, digital world. Markets are powerful and extremely efficient mechanisms for coordinating individuals’ and organizations’ behavior in a complex, networked economy. Thus, designing, monitoring, and regulating markets is an essential task of today’s society. This task does not only derive from a purely economic point of view. Leveraging market forces can also help to tackle pressing social and environmental challenges. Moreover, markets process, generate, and reveal information. This information is a production factor and a valuable economic asset. In an increasingly digital world, it is more essential than ever to understand the life cycle of information from its creation and distribution to its use. Both markets and the flow of information should not arbitrarily emerge and develop based on individual, profit-driven actors. Instead, they should be engineered to serve best the whole society’s goals. This motivation drives the research fields of market engineering and information management. With this book, the editors and authors honor Professor Dr. Christof Weinhardt for his enormous and ongoing contribution to market engineering and information management research and practice. It was presented to him on the occasion of his sixtieth birthday in April 2021. Thank you very much, Christof, for so many years of cooperation, support, inspiration, and friendship.

This open access book covers the use of data science, including advanced machine learning, big data analytics, Semantic Web technologies, natural language processing, social media analysis, time series analysis, among others, for applications in economics and finance. In addition, it shows some successful applications of advanced data science solutions used to extract new knowledge from data in order to improve economic forecasting models. The book starts with an introduction on the use of data science technologies in economics and finance and is followed by thirteen chapters showing success stories of the application of specific data science methodologies, touching on particular topics related to novel big data sources and technologies for economic analysis (e.g. social media and news); big data models leveraging on supervised/unsupervised (deep) machine learning; natural language processing to build economic and financial indicators; and forecasting and nowcasting of economic variables through time series analysis. This book is relevant to all stakeholders involved in digital and data-intensive research in economics and finance, helping them to understand the main opportunities and challenges, become familiar with the latest methodological findings, and learn how to use and evaluate the performances of novel tools and frameworks. It primarily targets data scientists and business analysts exploiting data science technologies, and it will also be a useful resource to research students in disciplines and courses related to these topics. Overall, readers will learn modern and effective data science solutions to create tangible innovations for economic and financial applications.

This open access book presents an overview and step-by-step explanation of process management. It starts with the individual participants’ perspectives on their work in a process and its structuring and harmonization, and then moves on to its specification in a model and how it is embedded in the organizational and IT environment of the company. Lastly, the book examines the joint processing of instances in the resulting socio-technical systems. A corresponding illustration, which expands with the overview, enables readers to gain a comprehensive understanding of business process management. The book presents various facets of business process management from the perspective of the participants, and introduces a selection of models that have proved useful in practice. The design of such models supports the transition from a more-or-less unstructured or unsatisfactory way of working to a structured process that corresponds to the ideas of the company and its customers. The book is intended for professionals in industry as well as students in the field of business information systems who are looking for guidelines on how to discover, create and implement real-world processes.

This open access book is part of the LAMBDA Project (Learning, Applying, Multiplying Big Data Analytics), funded by the European Union, GA No. 809965. Data Analytics involves applying algorithmic processes to derive insights. Nowadays it is used in many industries to allow organizations and companies to make better decisions as well as to verify or disprove existing theories or models. The term data analytics is often used interchangeably with intelligence, statistics, reasoning, data mining, knowledge discovery, and others. The goal of this book is to introduce some of the definitions, methods, tools, frameworks, and solutions for big data processing, starting from the process of information extraction and knowledge representation, via knowledge processing and analytics to visualization, sense-making, and practical applications. Each chapter in this book addresses some pertinent aspect of the data processing chain, with a specific focus on understanding Enterprise Knowledge Graphs, Semantic Big Data Architectures, and Smart Data Analytics solutions. This book is addressed to graduate students from technical disciplines, to professional audiences following continuous education short courses, and to researchers from diverse areas following self-study courses. Basic skills in computer science, mathematics, and statistics are required.