Fuzzy cognitive maps (FCMs) have gained popularity in the scientific community due to their capabilities in modeling and decision making for complex problems.This book presents a novel algorithm called glassoFCM to enable automatic learning of FCM models from data. Specifically, glassoFCM is a combination of two methods, glasso (a technique originated from machine learning) for data modeling and FCM simulation for decision making. The book outlines that glassoFCM elaborates simple, accurate, and more stable models that are easy to interpret and offer meaningful decisions. The research results presented are based on an investigation related to a real-world business intelligence problem to evaluate characteristics that influence employee work readiness.Finally, this book provides readers with a step-by-step guide of the 'fcm' package to execute and visualize their policies and decisions through the FCM simulation process.

Analytics is changing the landscape of businesses across sectors globally. This has led to the stimulation of interest of scholars and practitioners worldwide in this domain. The emergence of 'big data', has fanned the usages of machine learning techniques and the acceptance of 'Analytics Enabled Decision Making'. This book provides a holistic theoretical perspective combined with the application of such theories by drawing on the experiences of industry professionals and academicians from around the world. The book discusses several paradigms including pattern mining, clustering, classification, and data analysis to name a few. The main objective of this book is to offer insight into the process of decision-making that is accelerated and made more precise with the help of analytics.

Widely acclaimed as one of the top agile books in its first edition, Project Management the Agile Way has now been updated and redesigned by popular demand. This second edition is in a modular format to facilitate training and advanced course instruction, and provides new coverage of agile, such as hybrid agile methods, the latest public sector practices, and a chapter dedicated to transitioning to agile. It discusses the ""grand bargain"" between project management and business; the shift in dominance from plans to product and from input to output; and introduces new concepts such as return on benefit. Experienced practitioners and students that want to learn how to make agile work effectively in the enterprise should read this book. Also, individuals preparing for the PMI Agile Certified Practitioner (PMI-ACP)examination, and training providers developing courses, will find this second edition quite helpful.

The authors of this book alert that professional services like law, accountancy, and consultancy firms are set to face major disruption. The most important driver and enabler are the new technologies that help and in part substitute the work done by professionals. The second important disruptor is the new generation of professionals - "NewGen" - who are less interested in building their careers in a hierarchical organization and more interested in entrepreneurial challenges in small teams, with more rapid returns. In the meanwhile, major service conglomerates - the "big four" accounting firms, the "big three" consulting firms to name a few examples - build their network using their brand and substantial resources. All along, the relentless pressure from clients to receive more services at lower cost continues. Medium-sized professional firms as well as one-person independents appear to suffer most from these disruptions and are most anxious to find new ways to conduct their business. But the leaders of large firms also feel that they are increasingly unable to support the innovative entrepreneurship of their most promising professionals while their organizations institutionalize and their overheads continue to grow. This book proposes a new orientation and model of a professional service firm as an answer to these challenges, by creating a Professional Service Community. It is a synergistic team of organizations that share a vision of their role in society and main lines of their mission as well as the quality of their deliverables and their key clients. At the same time, they are independent in designing their internal business models - like recruitment, training, knowledge management, and economics. The Professional Service Community provides a unique and highly attractive level of entrepreneurship, flexibility, and efficiency to the benefit of its clients, partners, staff, and other stakeholders. It is the way of the future.

This book encompasses a systematic exploration of Cybersecurity Data Science (CSDS) as an emerging profession, focusing on current versus idealized practice. This book also analyzes challenges facing the emerging CSDS profession, diagnoses key gaps, and prescribes treatments to facilitate advancement. Grounded in the management of information systems (MIS) discipline, insights derive from literature analysis and interviews with 50 global CSDS practitioners. CSDS as a diagnostic process grounded in the scientific method is emphasized throughout Cybersecurity Data Science (CSDS) is a rapidly evolving discipline which applies data science methods to cybersecurity challenges. CSDS reflects the rising interest in applying data-focused statistical, analytical, and machine learning-driven methods to address growing security gaps. This book offers a systematic assessment of the developing domain. Advocacy is provided to strengthen professional rigor and best practices in the emerging CSDS profession. This book will be of interest to a range of professionals associated with cybersecurity and data science, spanning practitioner, commercial, public sector, and academic domains. Best practices framed will be of interest to CSDS practitioners, security professionals, risk management stewards, and institutional stakeholders. Organizational and industry perspectives will be of interest to cybersecurity analysts, managers, planners, strategists, and regulators. Research professionals and academics are presented with a systematic analysis of the CSDS field, including an overview of the state of the art, a structured evaluation of key challenges, recommended best practices, and an extensive bibliography.

This book highlights the economic and social science perspectives in light of COVID-19. During 2020, leaders found themselves at historic crossroads, taking decisions under remarkable pressures and uncertainties. However, windows of opportunity are being created to shape the economic recovery, restore the health of the environment, develop sustainable business models, strengthen regional development, revitalize global cooperation, harness Industry 4.0, and redesign the social contracts, skills, and jobs. This book is an excellent resource for all those interested in economics and social sciences perspectives on digitalization and big data, especially in the light of the recent crisis determined by COVID-19. The chapters cover topics related to new models in entrepreneurship and innovation, sustainability and education, data science and digitalization, marketing and finance, etc., that will develop innovative instruments for countries, businesses, and education to revive after the crisis.

The public sector uses electronic data interchange (EDI) in areas such as the health sector, procurement, meat inspection, taxation, transportation, and the courts. Also, government encourages private sector to enhance their use of electronic communication to secure faster and more reliable exchange of data, to save manpower, and to enable organizational transformation to the information society. In EDI and Data Networking in the Public Sector authors from eight countries report on the modes of governmental intervention to stimulate the usage of EDI and the challenges facing government as they try to smoothen their own units via organizational management. With cases (a total of 14 chapters) from Singapore, Denmark, USA, Sweden, England, Australia, and the Netherlands, this book finds that government in some countries is ahead of the private sector in utilization of EDI and that government at both supranational, central and local levels are active players on the field. Local levels of government and the quasi-governmental organizations provide an important supplement to the efforts by central government to increase the overall usage of EDI. EDI and Data Networking in the Public Sector also indicates that the payoff from investments in EDI are slow to evolve. Dreams of quick return are not likely to emerge. Also, return of investments are highly linked to the organizations' ability to change their work processes along with the investments in EDI. The authors indicate that many public sector organizations lack the steam and vision necessarily to transform the government to a more technological based organization, where public procurement, taxation, medicine ordering etc. takes advantage ofthe potentials in EDI and data networking. EDI and Data Networking in the Public Sector will be of interest to all researchers and practitioners in MIS, Computer Science and International Trade working on the adoption of new technology.

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 in a Plumbing context to help students find essential Maths and English theory understandable, engaging and achievable. Written by Gary Taylor, lecturer with a wealth of experience in the Construction Multi-Skills and Plumbing industries, this workbook is an effective resource to support Maths and English learning in the classroom, at work and for personal study at home.

Dynamic complexity results from hidden, un known factors-or more precisely, interactions between factors-that can unexpectedly im pact the perfor mance of systems. When the influences of dynamic complexity are not meas ured and understood, new never-seen-before behaviors can come as unwelcomed surprises, which disrupt the performance of systems. Left alone, processes that were once prized for their effi ciency unexpectedly begin to degrade-costs increase, while volumes and quality decline. Evidence of problems may come too late for effective resolution as technology advance ments induce rapid change and compress the time available to react to that change. The results of dynamic complexity are always negative and unmanaged dynamic complexity can bring business or global systems to the point of sudden chaos. The 2009 H1N1 pandemic, 2008 Credit Crunch and 2011 Fukushima Daiichi nuclear disaster are global examples of the dangers of undiagnosed dynamic complexity. With increasing frequency executive leaders today are discovering that their business and IT system performance levels are not meeting expectations. In most cases these performance deficiencies are caused by dynamic complexity, which lies hidden like a cancer until the symptoms reveal themselves-often when it is too late to avoid negative impacts on business outcomes. This book examines the growing business problem of dynamic complexity and presents a path to a practical solution. To achieve better predictability, organizations must be able to expose new, dangerous patterns of behavior in time to take corrective actions and know which actions will yield the optimal results. The book authors promote new methods of risk management that use data collection, analytics, machine learning and automation processes to help organizations more accurately predict the future and take strategic actions to improve performance outcomes. The presented means of achieving this goal are based upon the authors' practical experiences, backed by scientific principles, and results achieved through consulting engagements with over 350 global organizations.

This handbook collects the most up-to-date scholarship, knowledge, and new developments of big data and data analytics by bringing together many strands of contextual and disciplinary research. In recent times, while there has been considerable research in exploring the role of big data, data analytics, and textual analytics in accounting, and auditing, we still lack evidence on what kinds of best practices academics, practitioners, and organizations can implement and use. To achieve this aim, the handbook focuses on both conventional and contemporary issues facing by academics, practitioners, and organizations particularly when technology and business environments are changing faster than ever. All the chapters in this handbook provide both retrospective and contemporary views and commentaries by leading and knowledgeable scholars in the field, who offer unique insights on the changing role of accounting and auditing in today's data and analytics driven environment. Aimed at academics, practitioners, students, and consultants in the areas of accounting, auditing, and other business disciplines, the handbook provides high-level insight into the design, implementation, and working of big data and data analytics practices for all types of organizations worldwide. The leading scholars in the field provide critical evaluations and guidance on big data and data analytics by illustrating issues related to various sectors such as public, private, not-for-profit, and social enterprises. The handbook's content will be highly desirable and accessible to accounting and non-accounting audiences across the globe.

This book systematically and integrally introduces the new security management theories and methods in the e-commerce environment. Based on the perspective of dynamic governance of the whole process, starting from the theoretical framework, this book analyzes the gap between the current situation and requirements of security management, defines its nature, function, object and system, and designs and constructs the whole process security management organization and operation system of e-commerce. It focuses on the core and most prominent risk control links (i.e. security impact factors) in e-commerce security, including e-commerce information and network security risk, e-commerce transaction risk, e-commerce credit risk, e-commerce personnel risk, etc. Then, the tools and methods for identifying and controlling various risks are described in detail, at the same time, management decision-making and coordination are integrated into the risk management. Finally, a closed loop of self-optimization is established by a continuous optimization evolution path of e-commerce security management.

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.

Information Technology (IT) - the field that links computer and communications equipment and software - is transforming the way modern business is done. Examples of factors leading these changes are: rapidly decreasing costs of computer hardware, government de-regulation, accelerating global competitiveness, an increasing management awareness, and the knowledge of how to employ Information Technology successfully. These have all led to the increase of IT's effects on existing markets, and, in the process, are creating entirely new markets. This book explores a variety of advances in IT by a group of researchers who are at the cutting edge of this research. Moreover, the book examines these innovative developments in terms of the Information Technology field and its effect on modern business. It is becoming increasingly apparent that IT is critical to success in today's competitive marketplace. As a result, this book examines a host of emerging effects at work in these developments and seeks to make sense out of these counter-acting, sometimes multiplicative, effects which can become obstacles for managers who wish to develop competitive applications of IT. These effects and the development of IT are grouped into four general categories in the book: Future Markets, Inter-Organizational Systems, Focused Applications, and Future Strategies.

This book covers important issues related to managing supply chain risks from various perspectives. Supply chains today are vulnerable to disruptions with a significant impact on firms' business and performance. The aim of supply chain risk management is to identify the potential sources of risks and implement appropriate actions in order to mitigate supply chain disruptions. This book presents a set of models, frameworks, strategies, and analyses that are essential for managing supply chain risks. As a comprehensive collection of the latest research and most recent cutting-edge developments on supply chain risk and its management, the book is structured into three main parts: 1) Supply Chain Risk Management; 2) Supply Chain Vulnerability and Disruptions Management; and 3) Toward a Resilient Supply Chain. Leading academic researchers as well as practitioners have contributed chapters, combining theoretical findings and research results with a practical and contemporary view on how companies can manage the supply chain risks and disruptions, as well as how to create a resilient supply chain. This book can serve as an essential source for students and scholars who are interested in pursuing research or teaching courses in the rapidly growing area of supply chain risk management. It can also provide an interesting and informative read for managers and practitioners who need to deepen their knowledge of effective supply chain risk management.

This book adopts the managerial perspective to the study of smart cities. As such, this book is a necessary addition to the existing body of literature on smart cities. The chapters included in this book prove the case that transformation of cities to smart cities is a function of effective and efficient management practices implemented at diverse levels of smart cities. While advances in information and communication technology (ICT) are crucial, it is the ability to apply ICT consciously and efficiently that drives the transformation of cities to smart cities in a manner conducive to cities' sustainability and resilience. The book covers three sets of interconnected topics: Management and decision-making for urban design and infrastructure development Management and decision-making in context of smart cities development Ways of promoting and ensuring participation, representation and co-creation in smart cities These three groups of topics offer a great opportunity to acquire a clear, direct, and practice-driven knowledge and understanding of how effective management allows ICT-enhanced tools and applications to change smart cities, possibly making them smarter.

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.

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 discusses the effective use of modern ICT solutions for business needs, including the efficient use of IT resources, decision support systems, business intelligence, data mining and advanced data processing algorithms, as well as the processing of large datasets (inter alia social networking such as Twitter and Facebook, etc.). The ability to generate, record and process qualitative and quantitative data, including in the area of big data, the Internet of Things (IoT) and cloud computing offers a real prospect of significant improvements for business, as well as the operation of a company within Industry 4.0. The book presents new ideas, approaches, solutions and algorithms in the area of knowledge representation, management and processing, quantitative and qualitative data processing (including sentiment analysis), problems of simulation performance, and the use of advanced signal processing to increase the speed of computation. The solutions presented are also aimed at the effective use of business process modeling and notation (BPMN), business process semantization and investment project portfolio selection. It is a valuable resource for researchers, data analysts, entrepreneurs and IT professionals alike, and the research findings presented make it possible to reduce costs, increase the accuracy of investment, optimize resources and streamline operations and marketing.

Benchmarking is considered a must for modern management. This book presents an approach to benchmarking that has a solid mathematical basis and is easy to understand and apply. The book focuses on three main topics. It shows how to formalize the representation of benchmarking objects. Furthermore, it presents different methods from decision making and voting and their application to benchmarking. Finally, it discusses suitable features for different benchmarking objects. The objects considered are taken from IT management, but can be easily transferred to other business areas, which makes the book interesting for all practitioners in the management field.

This title is a Pearson Global Edition. The Editorial team at Pearson has worked closely with educators around the world to include content which is especially relevant to students outside the United States. This package includes MyLab. For courses in Business Statistics. A classic text for accuracy and statistical precision Statistics for Business and Economics enables students to conduct serious analysis of applied problems rather than running simple "canned" applications. This text is also at a mathematically higher level than most business statistics texts and provides students with the knowledge they need to become stronger analysts for future managerial positions. In this regard, it emphasizes an understanding of the assumptions that are necessary for professional analysis. In particular, it has greatly expanded the number of applications that utilize data from applied policy and research settings. The Ninth Edition of this book has been revised and updated to provide students with improved problem contexts for learning how statistical methods can improve their analysis and understanding of business and economics. This revision recognizes the globalization of statistical study and in particular the global market for this book. Reach every student by pairing this text with MyLab Statistics MyLab (TM) is the teaching and learning platform that empowers you to reach every student. By combining trusted author content with digital tools and a flexible platform, MyLab personalizes the learning experience and improves results for each student. MyLab Statistics should only be purchased when required by an instructor. Please be sure you have the correct ISBN and Course ID. Instructors, contact your Pearson representative for more information.

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.

Inspired by the podcast Dear Multi-Hyphenate, this book explores how to be a multi-hypenate - an artist with multiple proficiencies - in the entertainment industry. Answers questions about individual mission-driven entrepreneurship in the Theatre industry. Each chapter features an interview with a notable theatre artist.

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 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.