This book presents a framework for developing as well as a comprehensive collection of state-of-the-art process querying methods. Process querying combines concepts from Big Data and Process Modeling and Analysis with Business Process Intelligence and Process Analytics to study techniques for retrieving and manipulating models of real-world and envisioned processes to organize and extract process-related information for subsequent systematic use. The book comprises sixteen contributed chapters distributed over four parts and two auxiliary chapters. The auxiliary chapters by the editor provide an introduction to the area of process querying and a summary of the presented methods, techniques, and applications for process querying. The introductory chapter also examines a process querying framework. The contributed chapters present various process querying methods, including discussions on how they instantiate the framework components, thus supporting the comparison of the methods. The four parts are due to the distinctive features of the methods they include. The first three are devoted to querying event logs generated by IT-systems that support business processes at organizations, querying process designs captured in process models, and methods that address querying both event logs and process models. The methods in these three parts usually define a language for specifying process queries. The fourth part discusses methods that operate over inputs other than event logs and process models, e.g., streams of process events, or do not develop dedicated languages for specifying queries, e.g., methods for assessing process model similarity. This book is mainly intended for researchers. All the chapters in this book are contributed by active researchers in the research disciplines of business process management, process mining, and process querying. They describe state-of-the-art methods for process querying, discuss use cases of process querying, and suggest directions for future work for advancing the field. Yet, also other groups like business or data scientists and other professionals, lecturers, graduate students, and tool vendors will find relevant information for their distinctive needs. Chapter "Celonis PQL: A Query Language for Process Mining" is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

This book provides conceptual understanding of machine learning algorithms though supervised, unsupervised, and advanced learning techniques. The book consists of four parts: foundation, supervised learning, unsupervised learning, and advanced learning. The first part provides the fundamental materials, background, and simple machine learning algorithms, as the preparation for studying machine learning algorithms. The second and the third parts provide understanding of the supervised learning algorithms and the unsupervised learning algorithms as the core parts. The last part provides advanced machine learning algorithms: ensemble learning, semi-supervised learning, temporal learning, and reinforced learning. Provides comprehensive coverage of both learning algorithms: supervised and unsupervised learning; Outlines the computation paradigm for solving classification, regression, and clustering; Features essential techniques for building the a new generation of machine learning.

People have a hard time communicating, and also have a hard time finding business knowledge in the environment. With the sophistication of search technologies like Google, business people expect to be able to get their questions answered about the business just like you can do an internet search. The truth is, knowledge management is primitive today, and it is due to the fact that we have poor business metadata management.
This book is about all the groundwork necessary for IT to really support the business properly. By providing not just data, but the context behind the data. For the IT professional, it will be tactically practical--very "how to" and a detailed approach to implementing best practices supporting knowledge management. And for the the IT or other manager who needs a guide for creating and justifying projects, it will help provide a strategic map.

* First book that helps businesses capture corporate (human) knowledge and unstructured data, and offer solutions for codifying it for use in IT and management.
* Written by Bill Inmon, one of the fathers of the data warehouse and well-known author, and filled with war stories, examples, and cases from current projects.
* Very practical, includes a complete metadata acquisition methodology and project plan to guide readers every step of the way.
* Includes sample unstructured metadata for use in self-testing and developing skills.

This book provides an overview of the topics of data, sovereignty, and governance with respect to data and online activities through a legal lens and from a cybersecurity perspective. This first chapter explores the concepts of data, ownerships, and privacy with respect to digital media and content, before defining the intersection of sovereignty in law with application to data and digital media content. The authors delve into the issue of digital governance, as well as theories and systems of governance on a state level, national level, and corporate/organizational level. Chapter three jumps into the complex area of jurisdictional conflict of laws and the related issues regarding digital activities in international law, both public and private. Additionally, the book discusses the many technical complexities which underlay the evolution and creation of new law and governance strategies and structures. This includes socio-political, legal, and industrial technical complexities which can apply in these areas. The fifth chapter is a comparative examination of the legal strategies currently being explored by a variety of nations. The book concludes with a discussion about emerging topics which either influence, or are influenced by, data sovereignty and digital governance, such as indigenous data sovereignty, digital human rights and self-determination, artificial intelligence, and global digital social responsibility. Cumulatively, this book provides the full spectrum of information, from foundational principles underlining the described topics, through to the larger, more complex, evolving issues which we can foresee ahead of us.

This textbook provides guidance to both students and practitioners of enterprise architecture (EA) on how to develop and maintain enterprise models. Rather than providing yet another list of EA notations and frameworks from A to Z, it focuses on methods to perform such tasks. The problem of EA maintenance, named Enterprise Cartography, is an important aspect addressed in this book because EA is a never ending challenge that increases as the organization transformations pace also increases. The long time perspective also entails the evolution of architectural frameworks and notations, something that does not occur when developing new models. Thus, a catalogue of patterns, principles and methods is presented to develop and maintain EA models and views. After a general introduction to the book in chapter 1, chapter 2 presents basic concepts for EA modeling. Chapter 3 further details the set of EA concepts needed to present the patterns, and principles, which are subsequently introduced in chapter 4. Next, chapter 5 describes enterprise cartography concepts and principles. The remaining book then turns to techniques and methodologies. In chapter 6 an EA development method is summarized. In chapter 7 an enterprise strategy design approach is proposed, while in chapter 8 a business process design methodology is described. Chapters 9 and 10 focus on information architecture and information systems architecture design approaches, including information systems architecture planning and application portfolio management. Eventually, chapter 11 describes a method for enterprise cartography (EC) design. Last not least, several case studies on EA and EC are proposed in the last chapter.

While good data is an enterprise asset, bad data is an enterprise liability. Data governance enables you to effectively and proactively manage data assets throughout the enterprise by providing guidance in the form of policies, standards, processes and rules and defining roles and responsibilities outlining who will do what, with respect to data. While implementing data governance is not rocket science, it is not a simple exercise. There is a lot confusion around what data governance is, and a lot of challenges in the implementation of data governance. Data governance is not a project or a one-off exercise but a journey that involves a significant amount of effort, time and investment and cultural change and a number of factors to take into consideration to achieve and sustain data governance success. Data Governance Success: Growing and Sustaining Data Governance is the third and final book in the Data Governance series and discusses the following: * Data governance perceptions and challenges * Key considerations when implementing data governance to achieve and sustain success* Strategy and data governance* Different data governance maturity frameworks* Data governance - people and process elements* Data governance metrics This book shares the combined knowledge related to data and data governance that the author has gained over the years of working in different industrial and research programs and projects associated with data, processes, and technologies and unique perspectives of Thought Leaders and Data Experts through Interviews conducted. This book will be highly beneficial for IT students, academicians, information management and business professionals and researchers to enhance their knowledge to support and succeed in data governance implementations. This book is technology agnostic and contains a balance of concepts and examples and illustrations making it easy for the readers to understand and relate to their own specific data projects.

Software systems that used to be relatively autonomous entities such as e.g. accounting systems, order-entry systems etc. are now interlinked in large networks comprising extensive information infrastructures. What earlier used to be stand-alone proprietary systems are now for the most part replaced by more or less standardized interdependent systems that form large networks of production and use. Organizations have to make decisions about what office suite to purchase? The easiest option is to continuously upgrade the existing office suite to the latest version, but the battle between WordPerfect and Microsoft Word demonstrated that the choice is not obvious. What instant messenger network to join for global communication? Preferably the one most colleagues and friends use; AOL Instant Messenger, Microsoft Messenger, and ICQ represent three satisfactory, but disjunctive alternatives. Similarly organizations abandon their portfolio of homegrown IT systems and replace them with a single Enterprise Resource Planning (ERP) system. Several ERP alternatives exist on the market, but which is the right one for you? The argumentation and rationale behind these considerations are obviously related to the technological and social networks we are embedded in, but it is not always easy to specify how.
Networked Information Technologies: Diffusion and Adoption offers contributions from academics and practitioners who study networked information systems from a diffusion and adoption point of view. Themes related to the conceptualisation of diffusion and adoption of networked information systems are discussed along with studies of the diffusion of networked information systems in public sector institutions and private businesses.
This volume contains the edited proceedings of the IFIP Conference on The Diffusion and Adoption of Networked Information Technologies, which was sponsored by the International Federation for Information Processing (IFIP) Working Group 8.6 and held in Copenhagen, Denmark in October 2003.

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 book offers state-of-the-art descriptions of intelligent service innovations in industry, supported by novel scientific approaches. It gathers findings presented at the 3rd Intelligent Services Summit, which took place in Zurich in September 2020, and chiefly focused on the design and application of Digital Twin as an enabler for business development in the field of smart services. Divided into three parts, the book addresses the challenges involved in the successful development and implementation of smart services for industry and science, ranging from data management to product design and lifecycle management. The four main aspects covered are industrial challenges, value system design (how to integrate resources into service ecosystems to create value), value creation through value proposition (how to create value for ecosystem actors), and value capture (how to create value for ecosystem businesses). Given its scope, the book offers an essential guide for practitioners and advanced students alike.

This write-in workbook is an invaluable resource to help students improve their Maths and English skills and help prepare for Level 1 and Level 2 Functional Skills exams. The real-life questions are all written with a business administration context to help students find essential Maths and English theory understandable, engaging and achievable. Written by Carole Vella, lecturer with a wealth of experience in the Retail and Business Administration industry, this workbook is an effective resource to support Maths and English learning in the classroom, at work and for personal study at home.

This book addresses many of the gaps in how industry and academia are currently tackling problems associated with big data. It introduces novel concepts, describes the end-to-end process, and connects the various pieces of the puzzle to offer a holistic view. In addition, it explains important concepts for a wide audience, using accessible language, diagrams, examples and analogies to do so. The book is intended for readers working in industry who want to expand their knowledge or pursue a related degree, and employs an industry-centered perspective.

The three volumes of Interest Rate Modeling present a comprehensive and up-to-date treatment of techniques and models used in the pricing and risk management of fixed income securities. Written by two leading practitioners and seasoned industry veterans, this unique series combines finance theory, numerical methods, and approximation techniques to provide the reader with an integrated approach to the process of designing and implementing industrial-strength models for fixed income security valuation and hedging. Aiming to bridge the gap between advanced theoretical models and real-life trading applications, the pragmatic, yet rigorous, approach taken in this book will appeal to students, academics, and professionals working in quantitative finance. Volume I provides the theoretical and computational foundations for the series, emphasizing the construction of efficient grid- and simulation-based methods for contingent claims pricing. The second part of Volume I is dedicated to local-stochastic volatility modeling and to the construction of vanilla models for individual swap and Libor rates. Although the focus is eventually turned toward fixed income securities, much of the material in this volume applies to generic financial markets and will be of interest to anybody working in the general area of asset pricing.

Professional data management is the foundation for the successful digital transformation of traditional companies. Unfortunately, many companies fail to implement data governance because they do not fully understand the complexity of the challenge (organizational structure, employee empowerment, change management, etc.) and therefore do not include all aspects in the planning and implementation of their data governance. This book explains the driving role that a responsive data organization can play in a company's digital transformation. Using proven process models, the book takes readers from the basics, through planning and implementation, to regular operations and measuring the success of data governance. All the important decision points are highlighted, and the advantages and disadvantages are discussed in order to identify digitization potential, implement it in the company, and develop customized data governance. The book will serve as a useful guide for interested newcomers as well as for experienced managers.

Sustainability requires companies to develop in an economically, environmentally and socially sustainable manner. Corporate sustainable development in turn requires movement towards cleaner production. In order to recognize the potential from cleaner production reduced costs and fewer environmental impacts through the reduced use of materials environmental management accounting (EMA) is a necessary information management tool.

Environmental Management Accounting for Cleaner Production reveals a set of tools for companies to collect, evaluate and interpret the information they need to estimate their potential to use cleaner production to realize cost savings and to make the best decisions about the available cleaner production options. EMA is therefore the key for driving environmental progress, cost savings, increased competitiveness and corporate sustainability through the means of cleaner production."

This book constitutes a selection of the best papers from the 15th International Conference on Business Excellence, Digital Economy and New Value Creation, ICBE 2021, held in Bucharest, Romania, in March 2021. This book is a collection of research findings and perspectives related to the digital economy and new value creation, led by the set of improvements and changes in the economic, societal and technological structures and processes towards the effort of reaching the sustainability goals.

This book projects a futuristic scenario that is more existent than they have been at any time earlier. To be conscious of the bursting prospective of IoT, it has to be amalgamated with AI technologies. Predictive and advanced analysis can be made based on the data collected, discovered and analyzed. To achieve all these compatibility, complexity, legal and ethical issues arise due to automation of connected components and gadgets of widespread companies across the globe. While these are a few examples of issues, the authors' intention in editing this book is to offer concepts of integrating AI with IoT in a precise and clear manner to the research community. In editing this book, the authors' attempt is to provide novel advances and applications to address the challenge of continually discovering patterns for IoT by covering various aspects of implementing AI techniques to make IoT solutions smarter. The only way to remain pace with this data generated by the IoT and acquire the concealed acquaintance it encloses is to employ AI as the eventual catalyst for IoT. IoT together with AI is more than an inclination or existence; it will develop into a paradigm. It helps those researchers who have an interest in this field to keep insight into different concepts and their importance for applications in real life. This has been done to make the edited book more flexible and to stimulate further interest in topics. All these motivated the authors toward integrating AI in achieving smarter IoT. The authors believe that their effort can make this collection interesting and highly attract the student pursuing pre-research, research and even master in multidisciplinary domain.

Cases on Information Technology and Organizational Politics and Culture documents real-life cases describing issues, challenges, and solutions related to information technology, and how it affects organizational politics and culture. The cases included in this book cover a wide variety of topics, such as: an integrated online library resources automation project, IT within a government agency, the politics of information management, and many others. ""Cases on Information Technology and Organizational Politics and Culture"" provides a much needed understanding of how management can deal with the impact of politics and culture on the overall utilization of information technology within an organization. Lessons learned from these cases are very instrumental in providing a better understanding of the issues and challenges involved in managing information technology, and its impact on organizational politics and cultures.

Foundations of Social Entrepreneurship presents definitions of social entrepreneurship, explains its benefits and challenges, describes the components of an ecosystem of support, and presents practical tools to approach social entrepreneurial projects. It is designed to be easily approachable by anyone without prior in-depth knowledge of the subject. The book is divided into two parts; the first provides readers with theoretical foundations to understand the phenomenon of social entrepreneurship, its different interpretations, the context in which it developed, and its socio-economic function. The second part of the book covers what it takes to create and manage a social entrepreneurial initiative. Pedagogical features are incorporated throughout to aid learning. They include summary tables, international case studies of social entrepreneurs from both developed and emerging economies, as well as suggested exercises and examples of how the tools presented are used in practice. Truly global in its scope, with a strong emphasis on combining theory with practice, this text should be core reading for advanced undergraduate and postgraduate students studying Social Entrepreneurship, Enterprise, and Responsible Business. Online resources include links to resources, chapter-by-chapter PowerPoint slides and instructor's manual.

This book is a review of the analytical methods required in most of the quantitative courses taught at MBA programs. Students with no technical background, or who have not studied mathematics since college or even earlier, may easily feel overwhelmed by the mathematical formalism that is typical of economics and finance courses. These students will benefit from a concise and focused review of the analytical tools that will become a necessary skill in their MBA classes. The objective of this book is to present the essential quantitative concepts and methods in a self-contained, non-technical, and intuitive way.

This book presents a comprehensive collection of case studies on augmented reality and virtual realty (AR/VR) applications in various industries. Augmented reality and virtual reality are changing the business landscape, providing opportunities for businesses to offer unique services and experiences to their customers. The case studies provided in this volume explore business uses of the technology across multiple industries such as healthcare, tourism, hospitality, events, fashion, entertainment, retail, education and video gaming. The book includes solutions of different maturities as well as those from startups to large enterprises thereby providing a thorough view of how augmented reality and virtual reality can be used in business.

This book examines strategic executive decision-making. Using data collected over a seven-year period, the author describes how some 1,500 executives actually do make strategic decisions--and how reality differs substantially from theories about executive decision-making. The author identifies and explains the limitations of much of the current research in strategic decision-making. He then offers a rigorous alternative that reflects what actually happens when executives grapple with strategic decisions involving joint ventures, market entry, diversification, acquisitions, project selection, and long-term goals. Management Review This groundbreaking contribution to business literature examines executive decisionmaking behavior concerning corporate and competitive business strategy. In contrast to previous studies, Strategic Executive Decisions does not offer a prescription for executive decisionmaking. Rather, with the help of extensive data collected over a seven-year period, Stahl describes how some 1500 executives actually do make strategic decisions from the executives' self reports of their own priorities, showing how reality differs substantially from existing theories widely used to explain executive decisionmaking behavior. Required reading for students of management and finance, this book offers an important new methodological alternative to existing theoretical explanations of executive decisionmaking behavior. Based on the observed difference between theory and actual practice, Stahl identifies and explains the limitations of much of the current research in strategic decisionmaking. He goes on to offer a methodologically rigorous alternative that more closely reflects what actually happens when executives grapple with key decisions involving joint ventures, market entry, diversification, acquisitions, project selection, and long-term strategic goals. The bulk of the study is devoted to a detailed analysis of executive decisionmaking in practice. Stahl shows that the only reliable means of determining how strategic decisions are made is unbiased observation of several decisions followed by calculation of what is really important to the decisionmaker. Questionnaires or interviews with the executives, Stahl demonstrates, will often produce misleading information about how a particular decision was made.

With this textbook, Vaisman and Zimanyi deliver excellent coverage of data warehousing and business intelligence technologies ranging from the most basic principles to recent findings and applications. To this end, their work is structured into three parts. Part I describes "Fundamental Concepts" including conceptual and logical data warehouse design, as well as querying using MDX, DAX and SQL/OLAP. This part also covers data analytics using Power BI and Analysis Services. Part II details "Implementation and Deployment," including physical design, ETL and data warehouse design methodologies. Part III covers "Advanced Topics" and it is almost completely new in this second edition. This part includes chapters with an in-depth coverage of temporal, spatial, and mobility data warehousing. Graph data warehouses are also covered in detail using Neo4j. The last chapter extensively studies big data management and the usage of Hadoop, Spark, distributed, in-memory, columnar, NoSQL and NewSQL database systems, and data lakes in the context of analytical data processing. As a key characteristic of the book, most of the topics are presented and illustrated using application tools. Specifically, a case study based on the well-known Northwind database illustrates how the concepts presented in the book can be implemented using Microsoft Analysis Services and Power BI. All chapters have been revised and updated to the latest versions of the software tools used. KPIs and Dashboards are now also developed using DAX and Power BI, and the chapter on ETL has been expanded with the implementation of ETL processes in PostgreSQL. Review questions and exercises complement each chapter to support comprehensive student learning. Supplemental material to assist instructors using this book as a course text is available online and includes electronic versions of the figures, solutions to all exercises, and a set of slides accompanying each chapter. Overall, students, practitioners and researchers alike will find this book the most comprehensive reference work on data warehouses, with key topics described in a clear and educational style. "I can only invite you to dive into the contents of the book, feeling certain that once you have completed its reading (or maybe, targeted parts of it), you will join me in expressing our gratitude to Alejandro and Esteban, for providing such a comprehensive textbook for the field of data warehousing in the first place, and for keeping it up to date with the recent developments, in this current second edition." From the foreword by Panos Vassiliadis, University of Ioannina, Greece.

This book defines and develops the concept of data capital. Using an interdisciplinary perspective, this book focuses on the key features of the data economy, systematically presenting the economic aspects of data science. The book (1) introduces an alternative interpretation on economists' observation of which capital has changed radically since the twentieth century; (2) elaborates on the composition of data capital and it as a factor of production; (3) describes morphological changes in data capital that influence its accumulation and circulation; (4) explains the rise of data capital as an underappreciated cause of phenomena from data sovereign, economic inequality, to stagnating productivity; (5) discusses hopes and challenges for industrial circles, the government and academia when an intangible wealth brought by data (and information or knowledge as well); (6) proposes the development of criteria for measuring regulating data capital in the twenty-first century for regulatory purposes by looking at the prospects for data capital and possible impact on future society. Providing the first a thorough introduction to the theory of data as capital, this book will be useful for those studying economics, data science, and business, as well as those in the financial industry who own, control, or wish to work with data resources.

O'Brien's Introduction to Information Systems 16e reflects the contemporary use of enterprise-wide business systems. New real-world case studies continue to correspond with this industry reality. The text's focus is on teaching the future manager the potential effect on business of the most current IT technologies such as the Internet, Intranets, and Extranets for enterprise collaboration, and how IT contributes to competitive advantage, reengineering business processes, problem solving, and decision-making. The benchmark text for the syllabus organized by technology (a week on databases, a week on networks, a week on systems development, etc.) taught from a managerial perspective. O'Brien defines technology and then explains how companies use the technology to improve performance. Real world cases finalize the explanation.