Download Free Process Mining And Network Protocols Book in PDF and EPUB Free Download. You can read online Process Mining And Network Protocols and write the review.

Diploma Thesis from the year 2015 in the subject Computer Science - IT-Security, grade: 1, St. Pölten University of Applied Sciences (Informatik & Security), language: English, abstract: Process mining is the binding link between computational intelligence, data mining, process modeling and analysis. The thesis shows how this research discipline can be applied to network protocols and what the awards will be. Process mining is based on event data, logged by almost every information system. This event data is extracted, transformed and loaded into the process mining tool to discover, check conformance or enhance the underlying process based on observed behavior. Determining the significance of process mining in the field of network protocols and their control flow, finding the best possible algorithms and notation systems, clarifying the prerequisites and providing a proof of concept are the main achievements. Additionally other reasonable and beneficial applications, like mining an alternative protocol, dealing with a large amount of event data and estimations due to size of necessary event data, are investigated.
What are the possibilities for process mining in hospitals? In this book the authors provide an answer to this question by presenting a healthcare reference model that outlines all the different classes of data that are potentially available for process mining in healthcare and the relationships between them. Subsequently, based on this reference model, they explain the application opportunities for process mining in this domain and discuss the various kinds of analyses that can be performed. They focus on organizational healthcare processes rather than medical treatment processes. The combination of event data and process mining techniques allows them to analyze the operational processes within a hospital based on facts, thus providing a solid basis for managing and improving processes within hospitals. To this end, they also explicitly elaborate on data quality issues that are relevant for the data aspects of the healthcare reference model. This book mainly targets advanced professionals involved in areas related to business process management, business intelligence, data mining, and business process redesign for healthcare systems as well as graduate students specializing in healthcare information systems and process analysis.
This is the second edition of Wil van der Aalst’s seminal book on process mining, which now discusses the field also in the broader context of data science and big data approaches. It includes several additions and updates, e.g. on inductive mining techniques, the notion of alignments, a considerably expanded section on software tools and a completely new chapter of process mining in the large. It is self-contained, while at the same time covering the entire process-mining spectrum from process discovery to predictive analytics. After a general introduction to data science and process mining in Part I, Part II provides the basics of business process modeling and data mining necessary to understand the remainder of the book. Next, Part III focuses on process discovery as the most important process mining task, while Part IV moves beyond discovering the control flow of processes, highlighting conformance checking, and organizational and time perspectives. Part V offers a guide to successfully applying process mining in practice, including an introduction to the widely used open-source tool ProM and several commercial products. Lastly, Part VI takes a step back, reflecting on the material presented and the key open challenges. Overall, this book provides a comprehensive overview of the state of the art in process mining. It is intended for business process analysts, business consultants, process managers, graduate students, and BPM researchers.
What Is Process Mining Process mining is a collection of approaches that relates the fields of data science and process management to support the study of operational processes based on event logs. These techniques were developed to help companies improve their business processes. The objective of process mining is to derive insights and take appropriate action from event data. The availability of event data and the aspiration to achieve process improvement are the driving forces behind process mining, which is an essential component of data science. The approaches of process mining make use of event data in order to demonstrate what individuals, machines, and organizations are actually doing. Process mining gives fresh insights that may be utilized to determine the execution paths taken by operational processes and address the performance and compliance concerns that are caused by these processes. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Process Mining Chapter 2: Workflow Chapter 3: Event-Driven Process Chain Chapter 4: Business Process Management Chapter 5: Sequential Pattern Mining Chapter 6: Business Process Discovery Chapter 7: Alpha Algorithm Chapter 8: Conformance Checking Chapter 9: Decision Mining Chapter 10: Artifact-Centric Business Process Model (II) Answering the public top questions about process mining. (III) Real world examples for the usage of process mining in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of process mining' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of process mining.
This book constitutes the refereed proceedings of ten international workshops held in Eindhoven, The Netherlands, in conjunction with the 12th International Conference on Business Process Management, BPM 2014, in September 2014. The ten workshops comprised Process-oriented Information Systems in Healthcare (ProHealth 2014), Security in Business Processes (SBP 2014), Process Model Collections: Management and Reuse (PMC-MR 2014), Business Processes in Collective Adaptive Systems (BPCAS 2014), Data- and Artifact-centric BPM (DAB 2014), Business Process Intelligence (BPI 2014), Business Process Management in the Cloud (BPMC 2014), Theory and Applications of Process Visualization (TaProViz 2014), Business Process Management and Social Software (BPMS2 2014) and Decision Mining and Modeling for Business Processes (DeMiMoP 2014). The 38 revised full and eight short papers presented were carefully reviewed and selected from 84 submissions. In addition, six short papers resulting from the Doctoral Consortium at BPM 2014 are included in this book.
This book contains the proceedings of two long-running events held along with the CAiSE conference relating to the areas of enterprise, business-process and information systems modeling: * the 22nd International Conference on Business Process Modeling, Development and Support, BPMDS 2021, and * the 26th International Conference on Exploring Modeling Methods for Systems Analysis and Development, EMMSAD 2021. The conferences were planned to take place in Melbourne, Australia, during June 28–29, 2021, but changed to an online format due to the COVID-19 pandemic. For BPMDS 10 full papers and 1 short paper were carefully reviewed and selected for publication from a total of 26 submissions; for EMMSAD 13 full papers and 1 short paper were accepted from 34 submissions. The papers were organized in topical sections as follows: BPMDS: Improving event data quality in coherence with business requirements; enhancing the value of data in processes improvement; event stream and predictive monitoring; modeling languages and reference models; EMMSAD: Enterprise modeling; handling models and modeling methods; threat and evidence modeling; and model-driven engineering and applications.
Master's Thesis from the year 2016 in the subject Computer Science - Commercial Information Technology, grade: -, Hamburg University of Technology (TUHH; Universität zu Lübeck), language: English, abstract: To manage business processes, companies must previously define, configure, implement and enact them. Analysts try to identify companies’ business processes. However, large companies might have complex business processs (BPs) and consist of many business units. Therefore, classical business process modelling hardly scales. Both, companies and analysts are interested in automated approaches for business process modelling, saving time and money. Today’s business process analysts often use process mining techniques to extract company’s business processes by analyzing event logs of applications. This technique has its limitations, and is strongly dependent on the kind of log files of deployed applications. By designing our mission oriented network analysis (MONA) approach using algorithms having polynomial complexity, we show that identification of business processes is tractable. Identification of related tasks which constitute business processes is based on analysis of communication patterns in network traffic. We assume that today’s business processes are based on network-aided applications. Our software presents identified business processes using business process modelling notation.
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 book describes process mining use cases and business impact along the value chain, from corporate to local applications, representing the state of the art in domain know-how. Providing a set of industrial case studies and best practices, it complements academic publications on the topic. Further the book reveals the challenges and failures in order to offer readers practical insights and guidance on how to avoid the pitfalls and ensure successful operational deployment. The book is divided into three parts: Part I provides an introduction to the topic from fundamental principles to key success factors, and an overview of operational use cases. As a holistic description of process mining in a business environment, this part is particularly useful for readers not yet familiar with the topic. Part II presents detailed use cases written by contributors from a variety of functions and industries. Lastly, Part III provides a brief overview of the future of process mining, both from academic and operational perspectives. Based on a solid academic foundation, process mining has received increasing interest from operational businesses, with many companies already reaping the benefits. As the first book to present an overview of successful industrial applications, it is of particular interest to professionals who want to learn more about the possibilities and opportunities this new technology offers. It is also a valuable resource for researchers looking for empirical results when considering requirements for enhancements and further developments.
This book focuses on the theory, practice, and concepts of process mining techniques in detail, especially pattern recognition in diverse society, science, medicine, engineering, and business. The book deliberates several perspectives on process mining techniques in the broader context of data science and big data approaches. Process Mining Techniques for Pattern Recognition: Concepts, Theory, and Practice provides an introduction to process mining techniques and pattern recognition. After that, it delivers the fundamentals of process modelling and mining essential to comprehend the book. The text emphasizes discovery as an important process mining task and includes case studies as well as real-life examples to guide users in successfully applying process mining techniques for pattern recognition in practice. Intended to be an introduction to process mining and pattern recognition for students, academics, and practitioners, this book is perfect for those who want to learn the basics, and also gain an understanding of the concepts on a deeper level.