This book describes the setup of digital enterprises and how to manage them, focusing primarily on the important knowledge and essential understanding of digital enterprise management required by managers and decision makers in organizations. It covers ten essential knowledge areas of this field: * Foundation of Digital Enterprise * Technology Foundation and Talent Management for Digital Enterprise * Digital Enterprise Strategy Planning and Implementation * B2C Digital Enterprise: E-tailing * B2C Digital Enterprise: E-Services * B2B Digital Enterprise and Supply Chain * Digital Platforms * Digital Marketing and Advertising * Digital Payment Systems * Mobile Enterprise Overall, this text provides the reader with the basics to understand the rapid development of digitization, facilitated by the dramatic advancements in digital technologies, extensively connected networks, and wider adoption of computing devices (especially mobile devices), as more and more organizations are realizing the strategic importance of digitization (e.g., sustainable growth of the organization, competitive advantage development and enhancement) and are embarking on digital enterprise.

Knowledge management has been growing in importance and popularity as a research topic and business initiative. ""Knowledge Management in Modern Organizations"" documents the latest key issues of knowledge management. The innovative chapters in this book discuss the philosophical foundations of knowledge management, serving as a viable resource for academicians, practitioners, researchers, and students. ""Knowledge Management in Modern Organizations"" depicts a global perspective as the contributors come from Asia, Europe, the Middle East, and the United States, and adds value to any course focused on KM in organizations.

The proliferation of computers in business requires increased managerial expertise in decision support systems and expert systems. Managers, executives, and scholars will find this in-depth examination of the latest tools and technologies available for decision support invaluable. The book provides a clear and complete discussion of the foundations and management applications of decision support, expert systems, artificial intelligence, and other management support systems. Practical examples are provided throughout, giving the business professional a useful tool for evaluating and utilizing the variety of decision support and information technologies available. In addition, Bidgoli explores the growing field of applicable artificial intelligence, including expert systems, fuzzy logic, and neural networks. This book enhances expertise in a succinct, practical, and readable way.

This book presents a comprehensive and up-to-date treatise of a range of methodological and algorithmic issues. It also discusses implementations and case studies, identifies the best design practices, and assesses data analytics business models and practices in industry, health care, administration and business.Data science and big data go hand in hand and constitute a rapidly growing area of research and have attracted the attention of industry and business alike. The area itself has opened up promising new directions of fundamental and applied research and has led to interesting applications, especially those addressing the immediate need to deal with large repositories of data and building tangible, user-centric models of relationships in data. Data is the lifeblood of today's knowledge-driven economy.Numerous data science models are oriented towards end users and along with the regular requirements for accuracy (which are present in any modeling), come the requirements for ability to process huge and varying data sets as well as robustness, interpretability, and simplicity (transparency). Computational intelligence with its underlying methodologies and tools helps address data analytics needs.The book is of interest to those researchers and practitioners involved in data science, Internet engineering, computational intelligence, management, operations research, and knowledge-based systems.

Managers and analysts routinely collect and examine key performance measures to better understand their operations and make good decisions. Being able to render the complexity of operations data into a coherent account of significant events requires an understanding of how to work well with raw data and to make appropriate inferences. Although some statistical techniques for analyzing data and making inferences are sophisticated and require specialized expertise, there are methods that are understandable and applicable by anyone with basic algebra skills and the support of a spreadsheet package. By applying these fundamental methods themselves rather than turning over both the data and the responsibility for analysis and interpretation to an expert, managers will develop a richer understanding and potentially gain better control over their environment. This text is intended to describe these fundamental statistical techniques to managers, data analysts, and students. Statistical analysis of sample data is enhanced by the use of computers. Spreadsheet software is well suited for the methods discussed in this text. Examples in the text apply Microsoft Excel. Readers will have access to the example workbooks and Adobe Flash videos illustrating key steps using Microsoft Excel from the Business Expert Press website.

This book presents a key solution for current and future technological issues, adopting an integrated system approach with a combination of software engineering applications. Focusing on how software dominates and influences the performance, reliability, maintainability and availability of complex integrated systems, it proposes a comprehensive method of improving the entire process. The book provides numerous qualitative and quantitative analyses and examples of varied systems to help readers understand and interpret the derived results and outcomes. In addition, it examines and reviews foundational work associated with decision and control systems for information systems, to inspire researchers and industry professionals to develop new and integrated foundations, theories, principles, and tools for information systems. It also offers guidance and suggests best practices for the research community and practitioners alike. The book's twenty-two chapters examine and address current and future research topics in areas like vulnerability analysis, secured software requirements analysis, progressive models for planning and enhancing system efficiency, cloud computing, healthcare management, and integrating data-information-knowledge in decision-making. As such it enables organizations to adopt integrated approaches to system and software engineering, helping them implement technological advances and drive performance. This in turn provides actionable insights on each and every technical and managerial level so that timely action-based decisions can be taken to maintain a competitive edge. Featuring conceptual work and best practices in integrated systems and software engineering applications, this book is also a valuable resource for all researchers, graduate and undergraduate students, and management professionals with an interest in the fields of e-commerce, cloud computing, software engineering, software & system security and analysis, data-information-knowledge systems and integrated systems.

This book discusses the unique nature and complexity of fog data analytics (FDA) and develops a comprehensive taxonomy abstracted into a process model. The exponential increase in sensors and smart gadgets (collectively referred as smart devices or Internet of things (IoT) devices) has generated significant amount of heterogeneous and multimodal data, known as big data. To deal with this big data, we require efficient and effective solutions, such as data mining, data analytics and reduction to be deployed at the edge of fog devices on a cloud. Current research and development efforts generally focus on big data analytics and overlook the difficulty of facilitating fog data analytics (FDA). This book presents a model that addresses various research challenges, such as accessibility, scalability, fog nodes communication, nodal collaboration, heterogeneity, reliability, and quality of service (QoS) requirements, and includes case studies demonstrating its implementation. Focusing on FDA in IoT and requirements related to Industry 4.0, it also covers all aspects required to manage the complexity of FDA for IoT applications and also develops a comprehensive taxonomy.

This edited two-volume collection presents the most interesting and compelling articles pertaining to the formulation of research methods used to study information systems from the 30 year publication history of the Journal of Information Technology (JIT).

The Media Convergence Handbook sheds new light on the complexity of media convergence and the related business challenges. Approaching the topic from a managerial, technological as well as end-consumer perspective, it acts as a reference book and educational resource in the field. Media convergence at business level may imply transforming business models and using multiplatform content production and distribution tools. However, it is shown that the implementation of convergence strategies can only succeed when expectations and aspirations of every actor involved are taken into account. Media consumers, content producers and managers face different challenges in the process of media convergence. Volume I of the Media Convergence Handbook encourages an active discourse on media convergence by introducing the concept through general perspective articles and addressing the real-world challenges of conversion in the publishing, broadcasting and social media sectors.

The exponential growth of disruptive technology is changing our world. The development of cloud computing, big data, the internet of things, artificial intelligence, machine learning, deep learning, and other related autonomous systems, such as self-driving vehicles, have triggered the emergence of new products and services. These significant technological breakthroughs have opened the door to new economic models such as the sharing and platform-based economy. As a result, companies are becoming increasingly data- and algorithm-driven, coming to be more like "decentralized platforms". New transaction or payment methods such as Bitcoin and Ethereum, based on trust-building systems using Blockchain, smart contracts, and other distributed ledger technology, also constitute an essential part of this new economic model. The sharing economy and digital platforms also include the everyday exchange of goods allowing individuals to commodify their surplus resources. Information and innovation technologies are used in order to then match these resources with existing demand in the market. Online platforms such as Airbnb, Uber, and Amazon reduce information asymmetry, increase the value of unused resources, and create new opportunities for collaboration and innovation. Moreover, the sharing economy is playing a major role in the transition from exclusive ownership of personal assets toward access-based exploitation of resources. The success of online matching platforms depends not only on the reduction of search costs but also on the trustworthiness of platform operators. From a legal perspective, the uncertainties triggered by the emergence of a new digital reality are particularly urgent. How should these tendencies be reflected in legal systems in each jurisdiction? This book collects a series of contributions by leading scholars in the newly emerging fields of sharing economy and Legal Tech. The aim of the book is to enrich legal debates on the social, economic, and political meaning of these cutting-edge technologies. The chapters presented in this edition attempt to answer some of these lingering questions from the perspective of diverse legal backgrounds.

Building and Managing Enterprise-Wide Portals discusses the technology behind portals and grid networks, gives information to the history behind grid technology evolution, and supplies the reader with the knowledge to make qualified managerial decisions. This book looks at next-generation computing platforms and global cyber-infrastructure for solving large-scale problems in science, engineering, and business. ""Building and Managing Enterprise-Wide Portals"" includes a diverse range of topics, such as deployment considerations, facilities and tools for creating and managing grid and portal applications, including tools for visually linking corporate databases into grid or portal applications. This book also provides an overview of other web technologies, such as EJB and servlets, while comparing them to portals and grids and suggesting how to approach various analysis and design challenges.

The Business Analysts completely dissolves the perception that the IT industry dictates to businesses what IT systems they will use and dispels the myth that business users and IT technicians are from different planets. It suggests how to create an environment in which everybody works together in an exciting and refreshing way – a paradigm shift in the way business analysis projects are done.

The IT industry has to move to a point where it realises that the users of IT systems and the technical personnel are both equally responsible for getting the system to work. The users of the IT system should be an integral part of the team when the system is being put together. This, unfortunately, is not the norm within the industry. It is the business analyst’s responsibility, among others, to make sure that communication flows freely between all the parties involved.

This book gives the business analyst the tools and techniques to find out what the business users of IT systems really need and to guide the project to meet those needs.

As a fundamental change that is very large in scope, net centricity remains a main topic of debate among defense enterprises, industries, and contracting organizations. Net Centricity and Technological Interoperability in Organizations: Perspectives and Strategies provides understanding on the achievement of interoperability among organizations, focusing on new structural design concepts. A leading reference source for practitioners, academicians, and researchers involved in related fields of net centricity, this innovative publication is exceptional in its integration and explanation of complex topics.

This book describes pragmatic instruments and methods that enable business experts and software engineers to develop a common understanding of the software to be created, to determine their key requirements, and to manage the project in a way that fosters trust, encourages innovation and distributes risk fairly between clients and contractors. After an introduction to the fundamentals of agile software development in Part I, Part II describes the Interaction Room, an actual room where digitalization and mobilization strategies are developed, where technology potentials are evaluated, where software projects are planned and managed, and where business and technical stakeholders can communicate face to face, visualize complex relationships intuitively, and highlight value, effort and risk drivers that are keys to the project's success. After addressing these constructive aspects, the book focuses on the commercial aspects of software development: The adVANTAGE contract model described in Part III ensures that the insight-driven innovation process of software development does not just function, but is allowed to flourish in a trusted client-contractor relationship. Even though software contracting and construction may be grounded in two different academic disciplines, they are inseparable in practice, and how they interact is illustrated in the case study of developing a private health insurance benefit system in Part IV. Ultimately though, the success of every software project depends on the skills of the stakeholders. Part V therefore describes the qualification profile that software engineers and domain experts have to satisfy today. This book is aimed at CIOs, project managers and software engineers in industrial software development practice who want to learn how to effectively deal with the inevitable uncertainty of complex projects, who want to achieve higher levels of understanding and cooperation in their relationships with clients and contractors, and who want to run lower-risk software projects despite their inherent uncertainties.

This book highlights some of the most fascinating current uses, thought-provoking changes, and biggest challenges that Big Data means for our society. The explosive growth of data and advances in Big Data analytics have created a new frontier for innovation, competition, productivity, and well-being in almost every sector of our society, as well as a source of immense economic and societal value. From the derivation of customer feedback-based insights to fraud detection and preserving privacy; better medical treatments; agriculture and food management; and establishing low-voltage networks - many innovations for the greater good can stem from Big Data. Given the insights it provides, this book will be of interest to both researchers in the field of Big Data, and practitioners from various fields who intend to apply Big Data technologies to improve their strategic and operational decision-making processes.

This book presents the edited proceedings of the 16th IEEE/ACIS International Conference on Computer and Information Science (ICIS 2017), which was held on May 24-26, 2017 in Wuhan, China. The aim of this conference was to bring together researchers and scientists, businessmen and entrepreneurs, teachers, engineers, computer users, and students to discuss the various fields of computer science, share their experiences and exchange new ideas and information. The research results included relate to all aspects (theory, applications and tools) of computer and information science, and discuss the practical challenges encountered and the solutions adopted to solve them. The work selected represents 17 of the most promising papers from the conference, written by authors who are certain to make further significant contributions to the field of computer and information science.

The field of enterprise systems integration is constantly evolving, as every new technology that is introduced appears to make all previous ones obsolete. Despite this continuous evolution, there is a set of underlying concepts and technologies that have been gaining an increasing importance in this field. Examples are asynchronous messaging through message queues, data and application adapters based on XML and Web services, the principles associated with the service-oriented architecture (SOA), service composition, orchestrations, and advanced mechanisms such as correlations and long-running transactions. Today, these concepts have reached a significant level of maturity and they represent the foundation over which most integration platforms have been built.

This book addresses integration with a view towards supporting business processes. From messaging systems to data and application adapters, and then to services, orchestrations, and choreographies, the focus is placed on the connection between systems and business processes, and particularly on how it is possible to develop an integrated application infrastructure in order to implement the desired business processes. For this purpose, the text follows a layered, bottom-up approach, with application-oriented integration at the lowest level, followed by service-oriented integration and finally completed by process-oriented integration at the topmost level. The presentation of concepts is accompanied by a set of instructive examples using state-of-the-art technologies such as Java Message Service (JMS), Microsoft Message Queuing (MSMQ), Web Services, Microsoft BizTalk Server, and the Business Process Execution Language (BPEL).

The book is intended as a textbook for advance undergraduate or beginning graduate students in computer science, especially for those in an information systems curriculum. IT professionals with a background in programming, databases and XML will also benefit from the step-by-step description of the various integration levels and the related implementation examples.

This edited two-volume collection presents the most interesting and compelling articles pertaining to the formulation of research methods used to study information systems from the 30-year publication history of the Journal of Information Technology .

This book presents the characteristics and benefits industrial organizations can reap from the Industrial Internet of Things (IIoT). These characteristics and benefits include enhanced competitiveness, increased proactive decision-making, improved creativity and innovation, augmented job creation, heightened agility to respond to continuously changing challenges, and intensified data-driven decision making. In a straightforward fashion, the book also helps readers understand complex concepts that are core to IIoT enterprises, such as Big Data, analytic architecture platforms, machine learning (ML) and data science algorithms, and the power of visualization to enrich the domains experts' decision making. The book also guides the reader on how to think about ways to define new business paradigms that the IIoT facilitates, as well how to increase the probability of success in managing analytic projects that are the core engine of decision-making in the IIoT enterprise. The book starts by defining an IIoT enterprise and the framework used to efficiently operate. A description of the concepts of industrial analytics, which is a major engine for decision making in the IIoT enterprise, is provided. It then discusses how data and machine learning (ML) play an important role in increasing the competitiveness of industrial enterprises that operate using the IIoT technology and business concepts. Real world examples of data driven IIoT enterprises and various business models are presented and a discussion on how the use of ML and data science help address complex decision-making problems and generate new job opportunities. The book presents in an easy-to-understand manner how ML algorithms work and operate on data generated in the IIoT enterprise. Useful for any industry professional interested in advanced industrial software applications, including business managers and professionals interested in how data analytics can help industries and to develop innovative business solutions, as well as data and computer scientists who wish to bridge the analytics and computer science fields with the industrial world, and project managers interested in managing advanced analytic projects.

This book surveys big data tools used in macroeconomic forecasting and addresses related econometric issues, including how to capture dynamic relationships among variables; how to select parsimonious models; how to deal with model uncertainty, instability, non-stationarity, and mixed frequency data; and how to evaluate forecasts, among others. Each chapter is self-contained with references, and provides solid background information, while also reviewing the latest advances in the field. Accordingly, the book offers a valuable resource for researchers, professional forecasters, and students of quantitative economics.

In Unmasking Project Management, Moraveck explores approaches to help managers implement successful information systems projects. Moraveck goes beyond traditional approaches to management of information systems (MIS) by introducing several types of principles, concepts, and research models that can be used for project management, and by presenting information about organizations in a new way.
Written from the perspective of an insider, Unmasking Project Management helps professionals in information technology (IT) and business identify successful approaches to MIS for their organizations and projects. Since IT managers and project managers face unique challenges, this book provides the knowledge to overcome these challenges by exploring business initiatives that drive changes, highlighting important project management essentials, examining the organization environment and real-world experiences of IT and project managers, reviewing MIS options and choices, and demonstrating how to get the emotional and peer support that managers need in order to complete their projects successfully.
In addition to being useful for IT and project managers, Unmasking Project Management is ideal for students and practitioners who need help making decisions about managing information systems.

This book summarizes the results of Design Thinking Research Program at Stanford University in Palo Alto, California, USA and the Hasso Plattner Institute in Potsdam, Germany. Offering readers a closer look at design thinking, its innovation processes and methods, it covers topics ranging from how to design ideas, methods and technologies, to creativity experiments and creative collaboration in the real world, and the interplay between designers and engineers. But the topics go beyond this in their detailed exploration of design thinking and its use in IT systems engineering fields, and even from a management perspective. The authors show how these methods and strategies actually work in companies, and introduce new technologies and their functions. Furthermore, readers learn how special-purpose design thinking can be used to solve thorny problems in complex fields. Thinking and devising innovations are fundamentally and inherently human activities - so is design thinking. Accordingly, design thinking is not merely the result of special courses nor of being gifted or trained: it's a way of dealing with our environment and improving techniques, technologies and life. This edition offers a historic perspective on the theoretical foundations of design thinking. Within the four topic areas, various frameworks, methodologies, mindsets, systems and tools are explored and further developed. The first topic area focuses on team interaction, while the second part addresses tools and techniques for productive collaboration. The third section explores new approaches to teaching and enabling creative skills and lastly the book examines how design thinking is put into practice. All in all, the contributions shed light and provide deeper insights into how to support the collaboration of design teams in order to systematically and successfully develop innovations and design progressive solutions for tomorrow.

In scheduling theory, the models that have attracted considerable attention during the last two decades allow the processing times to be variable, i.e., to be subjected to various effects that make the actual processing time of a job dependent on its location in a schedule. The impact of these effects includes, but is not limited to, deterioration and learning. Under the first type of effect, the later a job is scheduled, the longer its actual processing time becomes. In the case of learning, delaying a job will result in shorter processing times. Scheduling with Time-Changing Effects and Rate-Modifying Activities covers and advances the state-of-the-art research in this area. The book focuses on single machine and parallel machine scheduling problems to minimize either the maximum completion time or the sum of completion times of all jobs, provided that the processing times are subject to various effects. Models that describe deterioration, learning and general non-monotone effects to be considered include positional, start-time dependent, cumulative and their combinations, which cover most of the traditionally used models. The authors also consider more enhanced models in which the decision-maker may insert certain Rate-Modifying Activities (RMA) on processing machines, such as for example, maintenance or rest periods. In any case, the processing times of jobs are not only dependent on effects mentioned above but also on the place of a job in a schedule relative to an RMA. For most of the enhanced models described in the book, polynomial-time algorithms are presented which are based on similar algorithmic ideas such as reduction to linear assignment problems (in a full form or in a reduced form), discrete convexity, and controlled generation of options.

The objective of this book is to contribute to the development of the intelligent information and database systems with the essentials of current knowledge, experience and know-how. The book contains a selection of 40 chapters based on original research presented as posters during the 8th Asian Conference on Intelligent Information and Database Systems (ACIIDS 2016) held on 14-16 March 2016 in Da Nang, Vietnam. The papers to some extent reflect the achievements of scientific teams from 17 countries in five continents. The volume is divided into six parts: (a) Computational Intelligence in Data Mining and Machine Learning, (b) Ontologies, Social Networks and Recommendation Systems, (c) Web Services, Cloud Computing, Security and Intelligent Internet Systems, (d) Knowledge Management and Language Processing, (e) Image, Video, Motion Analysis and Recognition, and (f) Advanced Computing Applications and Technologies. The book is an excellent resource for researchers, those working in artificial intelligence, multimedia, networks and big data technologies, as well as for students interested in computer science and other related fields.

This book describes in detail sampling techniques that can be used for unsupervised and supervised cases, with a focus on sampling techniques for machine learning algorithms. It covers theory and models of sampling methods for managing scalability and the "curse of dimensionality", their implementations, evaluations, and applications. A large part of the book is dedicated to database comprising standard feature vectors, and a special section is reserved to the handling of more complex objects and dynamic scenarios. The book is ideal for anyone teaching or learning pattern recognition and interesting teaching or learning pattern recognition and is interested in the big data challenge. It provides an accessible introduction to the field and discusses the state of the art concerning sampling techniques for supervised and unsupervised task. Provides a comprehensive description of sampling techniques for unsupervised and supervised tasks; Describe implementation and evaluation of algorithms that simultaneously manage scalable problems and curse of dimensionality; Addresses the role of sampling in dynamic scenarios, sampling when dealing with complex objects, and new challenges arising from big data. "This book represents a timely collection of state-of-the art research of sampling techniques, suitable for anyone who wants to become more familiar with these helpful techniques for tackling the big data challenge." M. Emre Celebi, Ph.D., Professor and Chair, Department of Computer Science, University of Central Arkansas "In science the difficulty is not to have ideas, but it is to make them work" From Carlo Rovelli