This book starts with an introduction to process modeling and process paradigms, then explains how to query and analyze process models, and how to analyze the process execution data. In this way, readers receive a comprehensive overview of what is needed to identify, understand and improve business processes.
The book chiefly focuses on concepts, techniques and methods. It covers a large body of knowledge on process analytics – including process data querying, analysis, matching and correlating process data and models – to help practitioners and researchers understand the underlying concepts, problems, methods, tools and techniques involved in modern process analytics. Following an introduction to basic business process and process analytics concepts, it describes the state of the art in this area before examining different analytics techniques in detail. In this regard, the book covers analytics over different levelsof process abstractions, from process execution data and methods for linking and correlating process execution data, to inferring process models, querying process execution data and process models, and scalable process data analytics methods. In addition, it provides a review of commercial process analytics tools and their practical applications.The book is intended for a broad readership interested in business process management and process analytics. It provides researchers with an introduction to these fields by comprehensively classifying the current state of research, by describing in-depth techniques and methods, and by highlighting future research directions. Lecturers will find a wealth of material to choose from for a variety of courses, ranging from undergraduate courses in business process management to graduate courses in business process analytics. Lastly, it offers professionals a reference guide to the state of the art in commercial tools and techniques, complemented by many real-world use case scenarios.
Table des matières
Introduction.- Business Process Paradigms.- Process matching techniques.- Model-based Business Process Query techniques and languages.- Business Process Data Analysis.- Tools, Use Cases and Discussions.- References.
A propos de l’auteur
Seyed-Mehdi-Reza Beheshti is a Lecturer & Research Fellow in the Service-Oriented Computing Research (SOCR) Group at the University of New South Wales (UNSW), Sydney, Australia. His main research interests are big data/data/business analytics and service/social/cloud computing.
Boualem Benatallah is a Scientia Professor in the School of Computer Science and Engineering at UNSW, and the founder and leader of the SOCR Group. His research interests include Web service protocols analysis and management, enterprise services integration, process modeling, and service-oriented architectures for pervasive computing.
Sherif Sakr is a Senior Researcher in the Software Systems Research Group at National ICT Australia (NICTA), ATP Lab, Sydney, Australia; and also a Conjoint Senior Lecturer in the School of Computer Science and Engineering (CSE) at UNSW.
Daniela Grigori is a Professor at Université Paris-Dauphine. Her research interests include process modelling, process matching and querying, and web services discovery, composition and integration.
Hamid Reza Motahari-Nezhad is a research staff member and data scientist at IBM Almaden Research Center, San José, CA, USA. His work focuses on research and innovation in technologies in software engineering, services and business process management.
Moshe Chai Barukh is a Lecturer & Research Fellow in the SOCR Group at UNSW. His primary research interests include Web services integration and composition techniques, cloud computing, and business process management with a particular interest in agile/flexible support and case management.
Ahmed Gater is big data leader at Ikayros. His research interests include process matching and querying, and Web services discovery.
Seung-Hwan Ryu is a Research Fellow at UNSW. His interests include similarity search, entity matching, big data, and service-oriented architectures.
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