Most applications generate large datasets, like social networking and social influence programs, smart cities applications, smart house environments, Cloud applications, public web sites, scientific experiments and simulations, data warehouse, monitoring platforms, and e-government services. Data grows rapidly, since applications produce continuously increasing volumes of both unstructured and structured data. Large-scale interconnected systems aim to aggregate and efficiently exploit the power of widely distributed resources. In this context, major solutions for scalability, mobility, reliability, fault tolerance and security are required to achieve high performance and to create a smart environment. The impact on data processing, transfer and storage is the need to re-evaluate the approaches and solutions to better answer the user needs. A variety of solutions for specific applications and platforms exist so a thorough and systematic analysis of existing solutions for data science, data analytics, methods and algorithms used in Big Data processing and storage environments is significant in designing and implementing a smart environment. Fundamental issues pertaining to smart environments (smart cities, ambient assisted leaving, smart houses, green houses, cyber physical systems, etc.) are reviewed. Most of the current efforts still do not adequately address the heterogeneity of different distributed systems, the interoperability between them, and the systems resilience. This book will primarily encompass practical approaches that promote research in all aspects of data processing, data analytics, data processing in different type of systems: Cluster Computing, Grid Computing, Peer-to-Peer, Cloud/Edge/Fog Computing, all involving elements of heterogeneity, having a large variety of tools and software to manage them. The main role of resource management techniques in this domain is to create the suitable frameworks for development of applications and deployment in smart environments, with respect to high performance. The book focuses on topics covering algorithms, architectures, management models, high performance computing techniques and large-scale distributed systems.

The process of developing predictive models includes many stages. Most resources focus on the modeling algorithms but neglect other critical aspects of the modeling process. This book describes techniques for finding the best representations of predictors for modeling and for nding the best subset of predictors for improving model performance. A variety of example data sets are used to illustrate the techniques along with R programs for reproducing the results.

Database System Concepts by Silberschatz, Korth and Sudarshan is now in its 7th edition and is one of the cornerstone texts of database education. It presents the fundamental concepts of database management in an intuitive manner geared toward allowing students to begin working with databases as quickly as possible. The text is designed for a first course in databases at the junior/senior undergraduate level or the first year graduate level. It also contains additional material that can be used as supplements or as introductory material for an advanced course. Because the authors present concepts as intuitive descriptions, a familiarity with basic data structures, computer organization, and a high-level programming language are the only prerequisites. Important theoretical results are covered, but formal proofs are omitted. In place of proofs, figures and examples are used to suggest why a result is true.

This book aims to help the reader better understand the importance of data analysis in project management. Moreover, it provides guidance by showing tools, methods, techniques and lessons learned on how to better utilize the data gathered from the projects. First and foremost, insight into the bridge between data analytics and project management aids practitioners looking for ways to maximize the practical value of data procured. The book equips organizations with the know-how necessary to adapt to a changing workplace dynamic through key lessons learned from past ventures. The book's integrated approach to investigating both fields enhances the value of research findings.

This book constitutes the refereed proceedings of the 22nd International TRIZ Future Conference on Automated Invention for Smart Industries, TFC 2022, which took place in Warsaw, Poland, in September 2022; the event was sponsored by IFIP WG 5.4.The 39 full papers presented were carefully reviewed and selected from 43 submissions. They are organized in the following thematic sections: New perspectives of TRIZ; AI in systematic innovation; systematic innovations supporting IT and AI; TRIZ applications; TRIZ education and ecosystem.

Modern information systems differ in essence from their predecessors. They support operations at multiple locations and different time zones, are distributed and network-based, and use multidimensional data analysis, data warehousing, knowledge discovery, knowledge management, mobile computing, and other modern information processing methods. This book considers fundamental issues of modern information systems. It discusses query processing, data quality, data mining, knowledge management, mobile computing, software engineering for information systems construction, and other topics. The book presents research results that are not available elsewhere. With more than 40 contributors, it is a solid source of information about the state of the art in the field of databases and information systems. It is intended for researchers, advanced students, and practitioners who are concerned with the development of advanced information systems.

This book provides a thorough overview of cutting-edge research on electronics applications relevant to industry, the environment, and society at large. It covers a broad spectrum of application domains, from automotive to space and from health to security, while devoting special attention to the use of embedded devices and sensors for imaging, communication and control. The volume is based on the 2021 ApplePies Conference, held online in September 2021, which brought together researchers and stakeholders to consider the most significant current trends in the field of applied electronics and to debate visions for the future. Areas addressed by the conference included information communication technology; biotechnology and biomedical imaging; space; secure, clean and efficient energy; the environment; and smart, green and integrated transport. As electronics technology continues to develop apace, constantly meeting previously unthinkable targets, further attention needs to be directed toward the electronics applications and the development of systems that facilitate human activities. This book, written by industrial and academic professionals, represents a valuable contribution in this endeavor.

Intelligent Integration of Information presents a collection of chapters bringing the science of intelligent integration forward. The focus on integration defines tasks that increase the value of information when information from multiple sources is accessed, related, and combined. This contributed volume has also been published as a special double issue of the Journal of Intelligent Information Systems (JIIS), Volume 6:2/3.

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.

Error Coding for Engineers provides a useful tool for practicing engineers, students, and researchers, focusing on the applied rather than the theoretical. It describes the processes involved in coding messages in such a way that, if errors occur during transmission or storage, they are detected and, if necessary, corrected. Very little knowledge beyond a basic understanding of binary manipulation and Boolean algebra is assumed, making the subject accessible to a broad readership including non-specialists. The approach is tutorial: numerous examples, illustrations, and tables are included, along with over 30 pages of hands-on exercises and solutions. Error coding is essential in many modern engineering applications. Engineers involved in communications design, DSP-based applications, IC design, protocol design, storage solutions, and memory product design are among those who will find the book to be a valuable reference. Error Coding for Engineers is also suitable as a text for basic and advanced university courses in communications and engineering.

Artificial Intelligence for Capital Market throws light on application of AI/ML techniques in the financial capital markets. This book discusses the challenges posed by the AI/ML techniques as these are prone to "black box" syndrome. The complexity of understanding the underlying dynamics for results generated by these methods is one of the major concerns which is highlighted in this book: Features: Showcases artificial intelligence in finance service industry Explains Credit and Risk Analysis Elaborates on cryptocurrencies and blockchain technology Focuses on optimal choice of asset pricing model Introduces Testing of market efficiency and Forecasting in Indian Stock Market This book serves as a reference book for Academicians, Industry Professional, Traders, Finance Mangers and Stock Brokers. It may also be used as textbook for graduate level courses in financial services and financial Analytics.

For the first time in history, the International Federation for Information Processing (IFIP) and the International Medical Informatics Association (IMIA) held the joint "E-Health" Symposium as part of "Treat IT" stream of the IFIP World Congress 2010 at Brisbane, Australia during September 22-23, 2010. IMIA is an independent organization established under Swiss law in 1989. The organization originated in 1967 from Technical Committee 4 of IFIP that is a n- governmental, non-profit umbrella organization for national societies working in the field of information processing. It was established in 1960 under the auspices of UNESCO following the First World Computer Congress held in Paris in 1959. Today, IFIP has several types of members and maintains friendly connections to specialized agencies of the UN system and non-governmental organizations. Technical work, which is the heart of IFIP's activity, is managed by a series of Technical Committees. Due to strong needs for promoting informatics in healthcare and the rapid progress of information and communication technology, IMIA President Reinhold Haux p- posed to strengthen the collaboration with IFIP. The IMIA General Assembly (GA) approved the move and an IMIA Vice President (VP) for special services (Hiroshi Takeda) was assigned as a liaison to IFIP at Brisbane during MEDINFO2007 where th the 40 birthday of IMIA was celebrated.

This book addresses a range of aging intensity functions, which make it possible to measure and compare aging trends for lifetime random variables. Moreover, they can be used for the characterization of lifetime distributions, also with bounded support. Stochastic orders based on the aging intensities, and their connections with some other orders, are also discussed. To demonstrate the applicability of aging intensity in reliability practice, the book analyzes both real and generated data. The estimated, properly chosen, aging intensity function is mainly recommended to identify data's lifetime distribution, and secondly, to estimate some of the parameters of the identified distribution. Both reliability researchers and practitioners will find the book a valuable guide and source of inspiration.

Intelligent information and database systems are two closely related subfields of modern computer science which have been known for over thirty years. They focus on the integration of artificial intelligence and classic database technologies to create the class of next generation information systems. The book focuses on new trends in intelligent information and database systems and discusses topics addressed to the foundations and principles of data, information, and knowledge models, methodologies for intelligent information and database systems analysis, design, and implementation, their validation, maintenance and evolution. They cover a broad spectrum of research topics discussed both from the practical and theoretical points of view such as: intelligent information retrieval, natural language processing, semantic web, social networks, machine learning, knowledge discovery, data mining, uncertainty management and reasoning under uncertainty, intelligent optimization techniques in information systems, security in databases systems, and multimedia data analysis. Intelligent information systems and their applications in business, medicine and industry, database systems applications, and intelligent internet systems are also presented and discussed in the book. The book consists of 38 chapters based on original works presented during the 7th Asian Conference on Intelligent Information and Database Systems (ACIIDS 2015) held on 23-25 March 2015 in Bali, Indonesia. The book is divided into six parts related to Advanced Machine Learning and Data Mining, Intelligent Computational Methods in Information Systems, Semantic Web, Social Networks and Recommendation Systems, Cloud Computing and Intelligent Internet Systems, Knowledge and Language Processing, and Intelligent Information and Database Systems: Applications.

The book explores a new general approach to selecting-and designing-data processing techniques. Symmetry and invariance ideas behind this algebraic approach have been successful in physics, where many new theories are formulated in symmetry terms. The book explains this approach and expands it to new application areas ranging from engineering, medicine, education to social sciences. In many cases, this approach leads to optimal techniques and optimal solutions. That the same data processing techniques help us better analyze wooden structures, lung dysfunctions, and deep learning algorithms is a good indication that these techniques can be used in many other applications as well. The book is recommended to researchers and practitioners who need to select a data processing technique-or who want to design a new technique when the existing techniques do not work. It is also recommended to students who want to learn the state-of-the-art data processing.

As organizations continue to develop, there is an increasing need for technological methods that can keep up with the rising amount of data and information that is being generated. Machine learning is a tool that has become powerful due to its ability to analyze large amounts of data quickly. Machine learning is one of many technological advancements that is being implemented into a multitude of specialized fields. An extensive study on the execution of these advancements within professional industries is necessary. Advanced Multi-Industry Applications of Big Data Clustering and Machine Learning is an essential reference source that synthesizes the analytic principles of clustering and machine learning to big data and provides an interface between the main disciplines of engineering/technology and the organizational, administrative, and planning abilities of management. Featuring research on topics such as project management, contextual data modeling, and business information systems, this book is ideally designed for engineers, economists, finance officers, marketers, decision makers, business professionals, industry practitioners, academicians, students, and researchers seeking coverage on the implementation of big data and machine learning within specific professional fields.

As data is an important asset for any organization, it is essential to apply semantic technologies in data science to fulfill the need of any organization. This volume of a two-volume handbook set provides a roadmap for new trends and future developments of data science with semantic technologies. Data Science with Semantic Technologies: New Trends and Future Developments highlights how data science enables the user to create intelligence through these technologies. In addition, this book offers the answers to various questions such as can semantic technologies be able to facilitate data science? Which type of data science problems can be tackled by semantic technologies? How can data scientists get benefited from these technologies? What is the role of semantic technologies in data science? What is the current progress and future of data science with semantic technologies? Which types of problems require the immediate attention of the researchers? What should be the vision 2030 for data science? This volume can serve as an important guide towards applications of data science with semantic technologies for the upcoming generation and thus becomes a unique resource for scholars, researchers, professionals, and practitioners in this field.

Gone are the days when data was interlinked with related data by humans and to find insights coherently, human interpretation was required. Data is no more just data. It is now considered a Thing or Entity or Concept- to bring the meaning to it, so that a machine not only understands the concept but also extrapolates the way humans do. Data Science with Semantic Technologies: Deployment and Exploration volume of a two-volume handbook set provides a roadmap for the deployment of semantic technologies in the field of data science and enables the user to create intelligence through these technologies by exploring the opportunities and eradicating the challenges in the current and future time frame. In addition, this book offers the answer to various questions like What makes a technology semantic as opposed to other approaches to data science? What is knowledge data science? How does knowledge data science relate to other fields? This book explores the optimal use of these technologies to provide the highest benefit to the user under one comprehensive source and title. As there is no dedicated book available in the market on this topic at this time, this proposed new book becomes a unique and only resource for scholars, researchers, data scientists, professionals, and practitioners. This volume can serve as an important guide towards applications of data science with semantic technologies for the upcoming generation and thus becomes a unique resource for scholars, researchers, professionals, and practitioners in this field.

Geographic Information Systems (GIS) have been experiencing a steady and unprecedented growth in terms of general interest, theory development, and new applications in the last decade or so. GIS is an inter-disciplinary field that brings together many diverse areas such as computer science, geography, cartography, engineering, and urban planning. Database Issues in Geographic Information Systems approaches several important topics in GIS from a database perspective. Database management has a central role to play in most computer-based information systems, and is expected to have an equally important role to play in managing information in GIS as well. Existing database technology, however, focuses on the alphanumeric data that are required in business applications. GIS, like many other application areas, requires the ability to handle spatial as well as alphanumeric data. This requires new innovations in data management, which is the central theme of this monograph. The monograph begins with an overview of different application areas and their data and functional requirements. Next it addresses the following topics in the context of GIS: representation and manipulation of spatial data, data modeling, indexing, and query processing. Future research directions are outlined in each of the above topics. The last chapter discusses issues that are emerging as important areas of technological innovations in GIS. Database Issues in Geographic Information Systems is suitable as a secondary text for a graduate level course on Geographic Information Systems, Database Systems or Cartography, and as a reference for researchers and practitioners in industry.

"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.

Welcome to the Second International IFIP Entertainment Computing Symposium on st Cultural Computing (ECS 2010), which was part of the 21 IFIP World Computer Congress, held in Brisbane, Australia during September 21-23, 2010. On behalf of the people who made this conference happen, we wish to welcome you to this inter- tional event. The IFIP World Computer Congress has offered an opportunity for researchers and practitioners to present their findings and research results in several prominent areas of computer science and engineering. In the last World Computer Congress, WCC 2008, held in Milan, Italy in September 2008, IFIP launched a new initiative focused on all the relevant issues concerning computing and entertainment. As a - sult, the two-day technical program of the First Entertainment Computing Symposium (ECS 2008) provided a forum to address, explore and exchange information on the state of the art of computer-based entertainment and allied technologies, their design and use, and their impact on society. Based on the success of ECS 2008, at this Second IFIP Entertainment Computing Symposium (ECS 2010), our challenge was to focus on a new area in entertainment computing: cultural computing.

This book presents the collection of the accepted research papers presented in the 1st 'International Conference on Computational Intelligence and Sustainable Technologies (ICoCIST-2021)'. This edited book contains the articles related to the themes on artificial intelligence in machine learning, big data analysis, soft computing techniques, pattern recognitions, sustainable infrastructural development, sustainable grid computing and innovative technology for societal development, renewable energy, and innovations in Internet of Things (IoT).

This book presents the potential use and implementation of intelligent techniques in decision making processes involved in organizations and companies. It provides a thorough analysis of decisions, reviewing the classical decision theory, and describing usual methods for modeling the decision process. It describes the chronological evolution of Decision Support Systems (DSS) from early Management Information Systems until the appearance of Intelligent Decision Support Systems (IDSS). It explains the most commonly used intelligent techniques, both data-driven and model-driven, and illustrates the use of knowledge models in Decision Support through case studies. The author pays special attention to the whole Data Science process, which provides intelligent data-driven models in IDSS. The book describes main uncertainty models used in Artificial Intelligence to model inexactness; covers recommender systems; and reviews available development tools for inducing data-driven models, for using model-driven methods and for aiding the development of Intelligent Decision Support Systems