This book examines the field of parallel database management systems and illustrates the great variety of solutions based on a shared-storage or a shared-nothing architecture. Constantly dropping memory prices and the desire to operate with low-latency responses on large sets of data paved the way for main memory-based parallel database management systems. However, this area is currently dominated by the shared-nothing approach in order to preserve the in-memory performance advantage by processing data locally on each server. The main argument this book makes is that such an unilateral development will cease due to the combination of the following three trends: a) Today's network technology features remote direct memory access (RDMA) and narrows the performance gap between accessing main memory on a server and of a remote server to and even below a single order of magnitude. b) Modern storage systems scale gracefully, are elastic and provide high-availability. c) A modern storage system such as Stanford's RAM Cloud even keeps all data resident in the main memory. Exploiting these characteristics in the context of a main memory-based parallel database management system is desirable. The book demonstrates that the advent of RDMA-enabled network technology makes the creation of a parallel main memory DBMS based on a shared-storage approach feasible.

Soft computing techniques are innovative tools that use nature-inspired algorithms to run predictive analysis of industries from business to software measurement. These tools have gained momentum in recent years for their practicality and flexibility. The Handbook of Research on Fuzzy and Rough Set Theory in Organizational Decision Making collects both empirical and applied research in the field of fuzzy set theory, and bridges the gap between the application of soft computational approaches and the organizational decision making process. This publication is a pivotal reference for business professionals, IT specialists, software engineers, and advanced students of business and information technology.

A revised and expanded advanced-undergraduate/graduate text (first ed., 1978) about optimization algorithms for problems that can be formulated on graphs and networks. This edition provides many new applications and algorithms while maintaining the classic foundations on which contemporary algorithm

How might one determine if a financial institution is taking risk in a balanced and productive manner? A powerful tool to address this question is economic capital, which is a model-based measure of the amount of equity that an entity must hold to satisfactorily offset its risk-generating activities. This book, with a particular focus on the credit-risk dimension, pragmatically explores real-world economic-capital methodologies and applications. It begins with the thorny practical issues surrounding the construction of an (industrial-strength) credit-risk economic-capital model, defensibly determining its parameters, and ensuring its efficient implementation. It then broadens its gaze to examine various critical applications and extensions of economic capital; these include loan pricing, the computation of loan impairments, and stress testing. Along the way, typically working from first principles, various possible modelling choices and related concepts are examined. The end result is a useful reference for students and practitioners wishing to learn more about a centrally important financial-management device.

Modern businesses generate huge volumes of accounting data on a daily basis. The recent advancements in information technology have given organizations the ability to capture and store data in an efficient and effective manner. However, there is a widening gap between this data storage and usage of the data. Business intelligence techniques can help an organization obtain and process relevant accounting data quickly and cost efficiently. Such techniques include: query and reporting tools, online analytical processing (OLAP), statistical analysis, text mining, data mining, and visualization. Business Intelligence Techniques is a compilation of chapters written by experts in the various areas. While these chapters stand on their own, taken together they provide a comprehensive overview of how to exploit accounting data in the business environment.

Big Data technologies have the potential to revolutionize the agriculture sector, in particular food safety and quality practices. This book is designed to provide a foundational understanding of various applications of Big Data in Food Safety. Big Data requires the use of sophisticated approaches for cleaning, processing and extracting useful information to improve decision-making. The contributed volume reviews some of these approaches and algorithms in the context of real-world food safety studies. Food safety and quality related data are being generated in large volumes and from a variety of sources such as farms, processors, retailers, government organizations, and other industries. The editors have included examples of how big data can be used in the fields of bacteriology, virology and mycology to improve food safety. Additional chapters detail how the big data sources are aggregated and used in food safety and quality areas such as food spoilage and quality deterioration along the supply chain, food supply chain traceability, as well as policy and regulations. The volume also contains solutions to address standardization, data interoperability, and other data governance and data related technical challenges. Furthermore, this volume discusses how the application of machine-learning has successfully improved the speed and/or accuracy of many processes in the food supply chain, and also discusses some of the inherent challenges. Included in this volume as well is a practical example of the digital transformation that happened in Dubai, with a particular emphasis on how data is enabling better decision-making in food safety. To complete this volume, researchers discuss how although big data is and will continue to be a major disruptor in the area of food safety, it also raises some important questions with regards to issues such as security/privacy, data control and data governance, all of which must be carefully considered by governments and law makers.

This book covers sustainable development in smart society's 5.0 using data analytics. The data analytics is the approach of integrating diversified heterogeneous data for predictive analysis to accredit innovation, decision making, business analysis, and strategic decision making. The data science brings together the research in the field of data analytics, online information analytics, and big data analytics to synthesize issues, challenges, and opportunities across smart society 5.0. Accordingly, the book offers an interesting and insightful read for researchers in the areas of decision analytics, cognitive analytics, big data analytics, visual analytics, text analytics, spatial analytics, risk analytics, graph analytics, predictive analytics, and analytics-enabled applications.

* Covers the entire new venture management process, from ideas to finance to HRM * Now includes international cases in all chapters * Offers a complete and contemporary business plan for students to follow * Includes material on the latest issues in entrepreneurship, such as equity crowdfunding and 'blitzscaling'

Advances in information technology (IT) have influenced how organizations do business. With IT playing such a pivotal role in the operations and success of an organization, it is imperative that it be used strategically. As a repository of cases, Cases on E-Readiness and Information Systems Management in Organizations: Tools for Maximizing Strategic Alignment contains research that readers can use to assess the e-readiness of their own organizations. This book presents principles, tools, and techniques about e-readiness, while also offering in-depth perspectives on applying the e-readiness model for the purpose of aligning IT with organizational strategies.

This book describes a conceptual management system derived from the Business and Technology Relationship Model (BTRM). The BTRM describes the relationship between business and technology and provides simple definitions for service quality, alignment, agility, and governance. It explains our problems with traditional methods, democratizes the management and governance of enterprise technologies, and is suitable for introducing process automation. This book describes in detail how the BTRM, combined with a focus on value creation and value delivery, will enable continuous change, in the context of current, emerging and future technologies. It illustrates the potential for real-time insight and control not previously considered and provides a wide range of information to plan an implementation, understand where AI can be applied, and its importance in the world of self-managing systems. The topic of this book is particularly relevant for business managers, business technology managers and technology service providers.

This book addresses the concept of organizing which is centered around collective learning and on the organization paradigm. It presents a theory of organizational learning based on a model of memory, explaining processes and dynamics through which memory is built and updated.

Rapidly developing Artificial Intelligence (AI) systems hold tremendous potential to change various domains and exert considerable influence on societies and organizations alike. More than merely a technical discipline, AI requires interaction between various professions. Based on the results of fundamental literature and empirical research, this book addresses the management's awareness of the ethical and moral aspects of AI. It seeks to fill a literature gap and offer the management guidance on tackling Trustworthy AI Implementation (TAII) while also considering ethical dependencies within the company. The TAII Framework introduced here pursues a holistic approach to identifying systemic ethical relationships within the company ecosystem and considers corporate values, business models, and common goods aspects like the Sustainable Development Goals and the Universal Declaration of Human Rights. Further, it provides guidance on the implementation of AI ethics in organisations without requiring a deeper background in philosophy and considers the social impacts outside of the software and data engineering setting. Depending on the respective legal context or area of application, the TAII Framework can be adapted and used with a range of regulations and ethical principles. This book can serve as a case study or self-review for c-level managers and students who are interested in this field. It also offers valuable guidelines and perspectives for policymakers looking to pursue an ethical approach to AI.

This book discusses theoretically and empirically the trade-off relationship between the frequency of product adaptation activities and the constraints on development resources, and how companies can respond to these constraints. The objective of this book is to identify effective management practices in continuous product development. With the continuation of development activities, companies are required to constantly adapt their products to changes in the external environment. In continuous product development, the development process extends beyond product release, and interaction with the external environment is not limited to the planning stage but occurs multiple times throughout the process. What impact does the multiple adaptation activities have on the product performance as development activities become more continuous, and how to use limited development resources to provide stable and constant high-quality adaptation activities with optimal frequency have become urgent issues in the development sites. To address these research questions, this book focuses primarily on the development activities of the online game industry. The factors that bring about superior product performance are examined by combining case studies and questionnaire surveys on online game development projects. Furthermore, user community management is also discussed from the perspective of the interaction process between multiple user groups.

Artificial intelligence (AI) and platforms are closely related. Most investments in AI, especially in critical technologies, are provided by large platforms. This book describes how platforms invest in AI and how AI will impact the next generation of competences, following a twofold approach to do so: on the one hand, the book seeks to understand how platforms for investment in intangibles and AI are organized, but on the other hand, it provides a framework to describe how AI will change jobs and competences in the future. Moreover, the book addresses five main themes: 1. platforms, platformization, and the foundations of their business models; 2. artificial intelligence, technological tendencies, and the policy agenda; 3. artificial intelligence, productivity, and the next generation of competences; 4. artificial intelligence, productivity, and the digital divide; 5. artificial intelligence, ethics, and the post-truth society. The book's content is mostly based on papers presented at the last two installments of the World Conference on Intellectual Capital for Communities. It brings together the views of leading scholars and experts on how artificial intelligence and platformization will impact competences in the near future.

This book discusses the effect of global pandemic, Covid-19, on human resource and draws strategies with new job designing tools and techniques. It provides insights on how to develop new strategies for HR professionals in corporates and academicians. This book explores the implication of descriptive, predictive and prescriptive HR analytics practices for different functional domains and in different countries during COVID-19. It brings new dimensions of study in HR analytics which are sure to change after COVID-19 as it has affected the way people are going to work.

"Service Level Agreements for Cloud Computing" provides a unique combination of business-driven application scenarios and advanced research in the area of service-level agreements for Clouds and service-oriented infrastructures. Current state-of-the-art research findings are presented in this book, as well as business-ready solutions applicable to Cloud infrastructures or ERP (Enterprise Resource Planning) environments. "Service Level Agreements for Cloud Computing" contributes to the various levels of service-level management from the infrastructure over the software to the business layer, including horizontal aspects like service monitoring. This book provides readers with essential information on how to deploy and manage Cloud infrastructures. Case studies are presented at the end of most chapters. "Service Level Agreements for Cloud Computing" is designed as a reference book for high-end practitioners working in cloud computing, distributed systems and IT services. Advanced-level students focused on computer science will also find this book valuable as a secondary text book or reference.

This book explores the possibility of integrating design thinking into today's technical contexts. Despite the popularity of design thinking in research and practice, this area is still too often treated in isolation without a clear, consistent connection to the world of software development. The book presents design thinking approaches and experiences that can facilitate the development of software-intensive products and services. It argues that design thinking and related software engineering practices, including requirements engineering and user-centric design (UX) approaches, are not mutually exclusive. Rather, they provide complementary methods and tools for designing software-intensive systems with a human-centric approach. Bringing together prominent experts and practitioners to share their insights, approaches and experiences, the book sheds new light on the specific interpretations and meanings of design thinking in various fields such as engineering, management, and information technology. As such, it provides a framework for professionals to demonstrate the potential of design thinking for software development, while offering academic researchers a roadmap for further research.

This open access book presents how cutting-edge digital technologies like Big Data, Machine Learning, Artificial Intelligence (AI), and Blockchain are set to disrupt the financial sector. The book illustrates how recent advances in these technologies facilitate banks, FinTech, and financial institutions to collect, process, analyze, and fully leverage the very large amounts of data that are nowadays produced and exchanged in the sector. To this end, the book also describes some more the most popular Big Data, AI and Blockchain applications in the sector, including novel applications in the areas of Know Your Customer (KYC), Personalized Wealth Management and Asset Management, Portfolio Risk Assessment, as well as variety of novel Usage-based Insurance applications based on Internet-of-Things data. Most of the presented applications have been developed, deployed and validated in real-life digital finance settings in the context of the European Commission funded INFINITECH project, which is a flagship innovation initiative for Big Data and AI in digital finance. This book is ideal for researchers and practitioners in Big Data, AI, banking and digital finance.

Create a Business that Runs Itself Going from small business to successful startup to scalable growth takes more than just good luck, it takes a system. Over the last 34 years franchising consultant and growth expert Mark Siebert has been sought out by more than 70,000 executives looking to expanding their company. Out of those 70,000 only 5,000 had the right systems in place to go from successful to scalable. What do these companies have in common? 1. They are good at what they do. Being good at the core of your business that you continue to see a healthy return on your investment. 2. They have a system in place and a manual on hand. Their process is documented and routinely integrated into every aspect of their business, so if someone follows the system the business can virtually run itself.

This book trailblazes co-evolution approaches which have been prototyped and tried out by the authors, with global academic and practitioner backgrounds. It was devised to help humanity, people, perceived as complex adaptive systems, to self-organize, co-create, and manage complexity, by showcasing with own example, as individuals and open networks. The book bundles main components needed for facilitation in complexity, while each chapter covers conceptual solutions for specific complexity strategies, tactics, operations - projects. These solutions serve as blueprints and roadmaps, providing approaches for practitioners and researchers alike. The main features incorporated in all the approaches are transcending silos and organizational hierarchies toward a borderless collaboration between diverse stakeholders with dynamic roles and accountabilities regarding purposes, missions and solutions. The book includes suggestions for strategic, tactical and operational managerial and governance approaches for disruptive, short-term, innovative, open, large-scale engagements where rapid onboarding, situational awareness, innovation and innovation in context, and action are expected while fast facilitation, dynamic reconfiguration, and self-organization are required. It also describes how long-term sustained co-creative action needs to be facilitated, to adapt to external and internal complexity dynamics while initiating positive change. This book showcases how co-creation and co-dreaming emerge with co-evolution. Chapters 1, 2, and 11 are available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

As the most comprehensive reference work dealing with decision support systems (DSS), this book is essential for the library of every DSS practitioner, researcher, and educator. Written by an international array of DSS luminaries, its more than 70 chapters approach decision support systems from a wide variety of perspectives ranging from classic foundations to cutting-edge thought, informative to provocative, theoretical to practical, historical to futuristic, human to technological, and operational to strategic. The chapters are conveniently organized into 10 major sections: foundations of decision support systems, DSS fundamentals, multiparticipant DSSs, intelligent DSSs, effects of decision support, time and space issues, scopes of decision support, developing and managing decision support systems, cases and applications, and decision support horizons. Novices and experts alike will refer to the authoritative and stimulating content again and again for years to come.

This book celebrates the 10-year anniversary of Software Center (a collaboration between 18 European companies and five Swedish universities) by presenting some of the most impactful and relevant journal or conference papers that researchers in the center have published over the last decade. The book is organized around the five themes around which research in Software Center is organized, i.e. Continuous Delivery, Continuous Architecture, Metrics, Customer Data and Ecosystems Driven Development, and AI Engineering. The focus of the Continuous Delivery theme is to help companies to continuously build high quality products with the right degree of automation. The Continuous Architecture theme addresses challenges that arise when balancing the need for architectural quality and more agile ways of working with shorter development cycles. The Metrics theme studies and provides insight to understand, monitor and improve software processes, products and organizations. The fourth theme, Customer Data and Ecosystem Driven Development, helps companies make sense of the vast amounts of data that are continuously collected from products in the field. Eventually, the theme of AI Engineering addresses the challenge that many companies struggle with in terms of deploying machine- and deep-learning models in industrial contexts with production quality. Each theme has its own part in the book and each part has an introduction chapter and then a carefully selected reprint of the most important papers from that theme. This book mainly aims at researchers and advanced professionals in the areas of software engineering who would like to get an overview about the achievement made in various topics relevant for industrial large-scale software development and management - and to see how research benefits from a close cooperation between industry and academia.

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.