Modern optimization approaches have attracted an increasing number of scientists, decision makers, and researchers. As new issues in this field emerge, different optimization methodologies must be developed and implemented. Exploring Critical Approaches of Evolutionary Computation is a vital scholarly publication that explores the latest developments, methods, approaches, and applications of evolutionary models in a variety of fields. It also emphasizes evolutionary models of computation such as genetic algorithms, evolutionary strategies, classifier systems, evolutionary programming, genetic programming, and related fields such as swarm intelligence and other evolutionary computation techniques. Highlighting a range of pertinent topics such as neural networks, data mining, and data analytics, this book is designed for IT developers, IT theorists, computer engineers, researchers, practitioners, and upper-level students seeking current research on enhanced information exchange methods and practical aspects of computational systems.

This book makes use of the LISP programming language to provide readers with the necessary background to understand and use fuzzy logic to solve simple to medium-complexity real-world problems. It introduces the basics of LISP required to use a Fuzzy LISP programming toolbox, which was specifically implemented by the author to "teach" the theory behind fuzzy logic and at the same time equip readers to use their newly-acquired knowledge to build fuzzy models of increasing complexity. The book fills an important gap in the literature, providing readers with a practice-oriented reference guide to fuzzy logic that offers more complexity than popular books yet is more accessible than other mathematical treatises on the topic. As such, students in first-year university courses with a basic tertiary mathematical background and no previous experience with programming should be able to easily follow the content. The book is intended for students and professionals in the fields of computer science and engineering, as well as disciplines including astronomy, biology, medicine and earth sciences. Software developers may also benefit from this book, which is intended as both an introductory textbook and self-study reference guide to fuzzy logic and its applications. The complete set of functions that make up the Fuzzy LISP programming toolbox can be downloaded from a companion book's website.

This book provides formal and informal definitions and taxonomies for self-aware computing systems, and explains how self-aware computing relates to many existing subfields of computer science, especially software engineering. It describes architectures and algorithms for self-aware systems as well as the benefits and pitfalls of self-awareness, and reviews much of the latest relevant research across a wide array of disciplines, including open research challenges. The chapters of this book are organized into five parts: Introduction, System Architectures, Methods and Algorithms, Applications and Case Studies, and Outlook. Part I offers an introduction that defines self-aware computing systems from multiple perspectives, and establishes a formal definition, a taxonomy and a set of reference scenarios that help to unify the remaining chapters. Next, Part II explores architectures for self-aware computing systems, such as generic concepts and notations that allow a wide range of self-aware system architectures to be described and compared with both isolated and interacting systems. It also reviews the current state of reference architectures, architectural frameworks, and languages for self-aware systems. Part III focuses on methods and algorithms for self-aware computing systems by addressing issues pertaining to system design, like modeling, synthesis and verification. It also examines topics such as adaptation, benchmarks and metrics. Part IV then presents applications and case studies in various domains including cloud computing, data centers, cyber-physical systems, and the degree to which self-aware computing approaches have been adopted within those domains. Lastly, Part V surveys open challenges and future research directions for self-aware computing systems. It can be used as a handbook for professionals and researchers working in areas related to self-aware computing, and can also serve as an advanced textbook for lecturers and postgraduate students studying subjects like advanced software engineering, autonomic computing, self-adaptive systems, and data-center resource management. Each chapter is largely self-contained, and offers plenty of references for anyone wishing to pursue the topic more deeply.

This book explores the future of cyber technologies and cyber operations which will influence advances in social media, cyber security, cyber physical systems, ethics, law, media, economics, infrastructure, military operations and other elements of societal interaction in the upcoming decades. It provides a review of future disruptive technologies and innovations in cyber security. It also serves as a resource for wargame planning and provides a strategic vision of the future direction of cyber operations. It informs military strategist about the future of cyber warfare. Written by leading experts in the field, chapters explore how future technical innovations vastly increase the interconnectivity of our physical and social systems and the growing need for resiliency in this vast and dynamic cyber infrastructure. The future of social media, autonomy, stateless finance, quantum information systems, the internet of things, the dark web, space satellite operations, and global network connectivity is explored along with the transformation of the legal and ethical considerations which surround them. The international challenges of cyber alliances, capabilities, and interoperability is challenged with the growing need for new laws, international oversight, and regulation which informs cybersecurity studies. The authors have a multi-disciplinary scope arranged in a big-picture framework, allowing both deep exploration of important topics and high level understanding of the topic. Evolution of Cyber Technologies and Operations to 2035 is as an excellent reference for professionals and researchers working in the security field, or as government and military workers, economics, law and more. Students will also find this book useful as a reference guide or secondary text book.

During the last few years, software evolution research has explored new domains such as the study of socio-technical aspects and collaboration between different individuals contributing to a software system, the use of search-based techniques and meta-heuristics, the mining of unstructured software repositories, the evolution of software requirements, and the dynamic adaptation of software systems at runtime. Also more and more attention is being paid to the evolution of collections of inter-related and inter-dependent software projects, be it in the form of web systems, software product families, software ecosystems or systems of systems.

With this book, the editors present insightful contributions on these and other domains currently being intensively explored, written by renowned researchers in the respective fields of software evolution. Each chapter presents the state of the art in a particular topic, as well as the current research, available tool support and remaining challenges. The book is complemented by a glossary of important terms used in the community, a reference list of nearly 1,000 papers and books and tips on additional resources that may be useful to the reader (reference books, journals, standards and major scientific events in the domain of software evolution and datasets).This book is intended for all those interested in software engineering, and more particularly, software maintenance and evolution. Researchers and software practitioners alike will find in the contributed chapters an overview of the most recent findings, covering a broad spectrum of software evolution topics. In addition, it can also serve as the basis of graduate or postgraduate courses on e.g., software evolution, requirements engineering, model-driven software development or social informatics.

With the growing popularity of "big data", the potential value of personal data has attracted more and more attention. Applications built on personal data can create tremendous social and economic benefits. Meanwhile, they bring serious threats to individual privacy. The extensive collection, analysis and transaction of personal data make it difficult for an individual to keep the privacy safe. People now show more concerns about privacy than ever before. How to make a balance between the exploitation of personal information and the protection of individual privacy has become an urgent issue. In this book, the authors use methodologies from economics, especially game theory, to investigate solutions to the balance issue. They investigate the strategies of stakeholders involved in the use of personal data, and try to find the equilibrium. The book proposes a user-role based methodology to investigate the privacy issues in data mining, identifying four different types of users, i.e. four user roles, involved in data mining applications. For each user role, the authors discuss its privacy concerns and the strategies that it can adopt to solve the privacy problems. The book also proposes a simple game model to analyze the interactions among data provider, data collector and data miner. By solving the equilibria of the proposed game, readers can get useful guidance on how to deal with the trade-off between privacy and data utility. Moreover, to elaborate the analysis on data collector's strategies, the authors propose a contract model and a multi-armed bandit model respectively. The authors discuss how the owners of data (e.g. an individual or a data miner) deal with the trade-off between privacy and utility in data mining. Specifically, they study users' strategies in collaborative filtering based recommendation system and distributed classification system. They built game models to formulate the interactions among data owners, and propose learning algorithms to find the equilibria.

This book presents the latest developments regarding a detailed mobile agent-enabled anomaly detection and verification system for resource constrained sensor networks; a number of algorithms on multi-aspect anomaly detection in sensor networks; several algorithms on mobile agent transmission optimization in resource constrained sensor networks; an algorithm on mobile agent-enabled in situ verification of anomalous sensor nodes; a detailed Petri Net-based formal modeling and analysis of the proposed system, and an algorithm on fuzzy logic-based cross-layer anomaly detection and mobile agent transmission optimization. As such, it offers a comprehensive text for interested readers from academia and industry alike.

This book introduces Software Thermal Management (STM) as a means of reducing power consumption in a computing system in order to manage heat, improve component reliability and increase system safety. Readers will benefit from this pragmatic guide to the field of STM for embedded systems and its catalog of software power management techniques. Since thermal management is a key bottleneck in embedded systems design, this book focuses on root cause of heat in embedded systems: power. Since software has an enormous impact on power consumption in an embedded system, this book urges software engineers to manage heat effectively by understanding, categorizing and developing new ways to reduce static and dynamic power consumption. Whereas most books on thermal management describe mechanisms to remove heat, this book focuses on ways for software engineers to avoid generating heat in the first place.

Computer Aided Software Engineering (CASE) tools typically support individual users in the automation of a set of tasks within a software development process. Such tools have helped organizations in their efforts to develop better software within budget and time constraints. However, many organizations are failing to take full advantage of CASE technology as they struggle to make coordinated use of collections of tools, often obtained at different times from different vendors. This book provides an in-depth analysis of the CASE tool integration problem, and describes practical approaches that can be used with current CASE technology to help your organization take greater advantage of integrated CASE.

"Social Data Analytics" is the first practical guide for professionals who want to employ social data for analytics and business intelligence (BI). This book provides a comprehensive overview of the technologies and platforms and shows you how to access and analyze the data. You'll explore the five major types of social data and learn from cases and platform examples to help you make the most of sentiment, behavioral, social graph, location, and rich media data. A four-step approach to the social BI process will help you access, evaluate, collaborate, and share social data with ease.

You'll learn everything you need to know to monitor social media and get an overview of the leading vendors in a crowded space of BI applications. By the end of this book, you will be well prepared for your organization s next social data analytics project.
Provides foundational understanding of new and emerging technologies-social data, collaboration, big data, advanced analyticsIncludes case studies and practical examples of success and failuresWill prepare you to lead projects and advance initiatives that will benefit you and your organization"

The book is organized around basic principles of software project management: planning and estimating, measuring and controlling, leading and communicating, and managing risk. Introduces software development methods, from traditional (hacking, requirements to code, and waterfall) to iterative (incremental build, evolutionary, agile, and spiral). Illustrates and emphasizes tailoring the development process to each project, with a foundation in the fundamentals that are true for all development methods. Topics such as the WBS, estimation, schedule networks, organizing the project team, and performance reporting are integrated, rather than being relegating to appendices. Each chapter in the book includes an appendix that covers the relevant topics from CMMI-DEV-v1.2, IEEE/ISO Standards 12207, IEEE Standard 1058, and the PMI Body of Knowledge.

Machine learning is widely used for data analysis. Dynamic fuzzy data are one of the most difficult types of data to analyse in the field of big data, cloud computing, the Internet of Things, and quantum information. At present, the processing of this kind of data is not very mature. The authors carried out more than 20 years of research, and show in this book their most important results. The seven chapters of the book are devoted to key topics such as dynamic fuzzy machine learning models, dynamic fuzzy self-learning subspace algorithms, fuzzy decision tree learning, dynamic concepts based on dynamic fuzzy sets, semi-supervised multi-task learning based on dynamic fuzzy data, dynamic fuzzy hierarchy learning, examination of multi-agent learning model based on dynamic fuzzy logic. This book can be used as a reference book for senior college students and graduate students as well as college teachers and scientific and technical personnel involved in computer science, artificial intelligence, machine learning, automation, data analysis, mathematics, management, cognitive science, and finance. It can be also used as the basis for teaching the principles of dynamic fuzzy learning.

With the proliferation of Software-as-a-Service (SaaS) offerings, it is becoming increasingly important for individual SaaS providers to operate their services at a low cost. This book investigates SaaS from the perspective of the provider and shows how operational costs can be reduced by using "multi tenancy," a technique for consolidating a large number of customers onto a small number of servers. Specifically, the book addresses multi tenancy on the database level, focusing on in-memory column databases, which are the backbone of many important new enterprise applications. For efficiently implementing multi tenancy in a farm of databases, two fundamental challenges must be addressed, (i) workload modeling and (ii) data placement. The first involves estimating the (shared) resource consumption for multi tenancy on a single in-memory database server. The second consists in assigning tenants to servers in a way that minimizes the number of required servers (and thus costs) based on the assumed workload model. This step also entails replicating tenants for performance and high availability. This book presents novel solutions to both problems.

"Time and Relational Theory" provides an in-depth description of temporal database systems, which provide special facilities for storing, querying, and updating historical and future data. Traditionally, database management systems provide little or no special support for temporal data at all. This situation is changing because: Cheap storage enables retention of large volumes of historical data in data warehousesUsers are now faced with temporal data problems, and need solutions Temporal features have recently been incorporated into the SQL standard, and vendors have begun to add temporal support to their DBMS products

Based on the groundbreaking text "Temporal Data & the Relational Model" (Morgan Kaufmann, 2002) and new research led by the authors, "Time and Relational Theory" is the only book to offer a complete overview of the functionality of a temporal DBMS. Expert authors Nikos Lorentzos, Hugh Darwen, and Chris Date describe an approach to temporal database management that is firmly rooted in classical relational theory and will stand the test of time.

This book covers the SQL:2011 temporal extensions in depth and identifies and discusses the temporal functionality still missing from SQL.
Understand how the relational model provides an ideal basis for taming the complexities of temporal databases Learn how to analyze and evaluate commercial temporal products with this timely and important information Be able to use sound principles in designing and using temporal databases Understand the temporal support recently added to SQL with coverage of the new SQL features in this unique, accurate, and authoritative reference Appreciate the benefits of a truly relational approach to the problem with this clear, user friendly presentation

Since its first volume in 1960, Advances in Computers has presented detailed coverage of innovations in computer hardware, software, theory, design, and applications. It has also provided contributors with a medium in which they can explore their subjects in greater depth and breadth than journal articles usually allow. As a result, many articles have become standard references that continue to be of significant, lasting value in this rapidly expanding field.

A new product can be easy or difficult to use, it can be efficient or cumbersome, engaging or dispiriting, it can support the way we work and think - or not. What options are available for systematically addressing such parameters and provide users with an appropriate functionality, usability and experience? In the last decades, several fields have evolved that encompass a user-centred approach to create better products for the people who use them. This book provides a comprehensible introduction to the subject. It is aimed first and foremost at people involved in software and product development - product managers, project managers, consultants and analysts, who face the major challenge of developing highly useful and usable products. Topics include: The most important user-centred techniques and their alignment in the development process Planning examples of user-centred activities for projects User-oriented approaches for organisations Real-life case studies Checklists, tips and a lot of background information provide help for practitioners

System Quality and Software Architecture collects state-of-the-art knowledge on how to intertwine software quality requirements with software architecture and how quality attributes are exhibited by the architecture of the system. Contributions from leading researchers and industry evangelists detail the techniques required to achieve quality management in software architecting, and the best way to apply these techniques effectively in various application domains (especially in cloud, mobile and ultra-large-scale/internet-scale architecture) Taken together, these approaches show how to assess the value of total quality management in a software development process, with an emphasis on architecture. The book explains how to improve system quality with focus on attributes such as usability, maintainability, flexibility, reliability, reusability, agility, interoperability, performance, and more. It discusses the importance of clear requirements, describes patterns and tradeoffs that can influence quality, and metrics for quality assessment and overall system analysis. The last section of the book leverages practical experience and evidence to look ahead at the challenges faced by organizations in capturing and realizing quality requirements, and explores the basis of future work in this area.

Since its first volume in 1960, "Advances in Computers" has presented detailed coverage of innovations in computer hardware, software, theory, design, and applications. It has also provided contributors with a medium in which they can explore their subjects in greater depth and breadth than journal articles usually allow. As a result, many articles have become standard references that continue to be of significant, lasting value in this rapidly expanding field.
In-depth surveys and tutorials on new computer technologyWell-known authors and researchers in the fieldExtensive bibliographies with most chaptersMany of the volumes are devoted to single themes or subfields of computer science