As Web-based systems and e-commerce carry businesses into the 21st century, databases are becoming workhorses that shoulder each and every online transaction. For organizations to have effective 24/7 Web operations, they need powerhouse databases that deliver at peak performance-all the time. High Performance Web Databases: Design, Development, and Deployment arms you with every essential technique from design and modeling to advanced topics such as data conversion, performance tuning, Web access and interfacing legacy systems, and security

This book negotiates the hyper dimensions of the Internet through stories from myriads of Web sites, with its fluent presentation and simple but chronological organization of topics highlighting numerous opportunities and providing a solid starting point not only for inexperienced entrepreneurs and managers but anyone interested in applying information technology in business through real or virtual enterprise networks to date. "A Manager's Primer on e-Networking" is an easy to follow primer on modern enterprise networking that every manager needs to read.

This edited book first consolidates the results of the EU-funded EDISON project (Education for Data Intensive Science to Open New science frontiers), which developed training material and information to assist educators, trainers, employers, and research infrastructure managers in identifying, recruiting and inspiring the data science professionals of the future. It then deepens the presentation of the information and knowledge gained to allow for easier assimilation by the reader. The contributed chapters are presented in sequence, each chapter picking up from the end point of the previous one. After the initial book and project overview, the chapters present the relevant data science competencies and body of knowledge, the model curriculum required to teach the required foundations, profiles of professionals in this domain, and use cases and applications. The text is supported with appendices on related process models. The book can be used to develop new courses in data science, evaluate existing modules and courses, draft job descriptions, and plan and design efficient data-intensive research teams across scientific disciplines.

Information is a key factor in business today, and data warehousing has become a major activity in the development and management of information systems to support the proper flow of information. Unfortunately, the majority of information systems are based on structured information stored in organizational databases, which means that the company is isolated from the business environment by concentrating on their internal data sources only. It is therefore vital that organizations take advantage of external business information, which can be retrieved from Internet services and mechanically organized within the existing information structures. Such a continuously extending integrated collection of documents and data could facilitate decision-making processes in the organization. Filtering the Web to Feed Data Warehouses discusses areas such as:- how to use data warehouse for filtering Web content- how to retrieve relevant information from diverse sources on the Web - how to handle the time aspect - how to mechanically establish links among data warehouse structures and documents filtered from external sources - how to use collected information to increase corporate knowledge and gives a comprehensive example, illustrating the idea of supplying data warehouses with relevant information filtered from the Web.

Interactive Graphics for Data Analysis: Principles and Examples discusses exploratory data analysis (EDA) and how interactive graphical methods can help gain insights as well as generate new questions and hypotheses from datasets. Fundamentals of Interactive Statistical GraphicsThe first part of the book summarizes principles and methodology, demonstrating how the different graphical representations of variables of a dataset are effectively used in an interactive setting. The authors introduce the most important plots and their interactive controls. They also examine various types of data, relations between variables, and plot ensembles. Case Studies Illustrate the PrinciplesThe second section focuses on nine case studies. Each case study describes the background, lists the main goals of the analysis and the variables in the dataset, shows what further numerical procedures can add to the graphical analysis, and summarizes important findings. Wherever applicable, the authors also provide the numerical analysis for datasets found in Cox and Snell's landmark book. Understand How to Analyze Data through Graphical Means This full-color text shows that interactive graphical methods complement the traditional statistical toolbox to achieve more complete, easier to understand, and easier to interpret analyses.

Large-Scale 3D Data Integration: Challenges and Opportunities examines the fundamental aspects of 3D geo-information, focusing on the latest developments in 3D GIS (geographic information) and AEC (architecture, engineering, construction) systems. This book addresses policy makers, designers and engineers, and individuals that need to overcome obstacles in integrating modeling perspectives and data. Organized into four major parts, the book begins by presenting a historical overview of the issues involved in integrating GIS and AEC. Part II then focuses on the data issue from several viewpoints: data collection; database structures and representation; database management; and visualization. Part III covers the areas of semantics, ontology, and standardization from a theoretical perspective and details many of the best examples of this approach in developing real-world applications. The book concludes with contributions that focus on recent advances in virtual geographic environments and alternative modeling schemes for the potential AEC/GIS interface.

This book constitutes the refereed proceedings of the 11th IFIP WG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2020, held in Costa de Caparica, Portugal, in July 2020. The 20 full papers and 24 short papers presented were carefully reviewed and selected from 91 submissions. The papers present selected results produced in engineering doctoral programs and focus on technological innovation for industry and service systems. Research results and ongoing work are presented, illustrated and discussed in the following areas: collaborative networks; decisions systems; analysis and synthesis algorithms; communication systems; optimization systems; digital twins and smart manufacturing; power systems; energy control; power transportation; biomedical analysis and diagnosis; and instrumentation in health.

This book highlights state-of-the-art research on big data and the Internet of Things (IoT), along with related areas to ensure efficient and Internet-compatible IoT systems. It not only discusses big data security and privacy challenges, but also energy-efficient approaches to improving virtual machine placement in cloud computing environments. Big data and the Internet of Things (IoT) are ultimately two sides of the same coin, yet extracting, analyzing and managing IoT data poses a serious challenge. Accordingly, proper analytics infrastructures/platforms should be used to analyze IoT data. Information technology (IT) allows people to upload, retrieve, store and collect information, which ultimately forms big data. The use of big data analytics has grown tremendously in just the past few years. At the same time, the IoT has entered the public consciousness, sparking people's imaginations as to what a fully connected world can offer. Further, the book discusses the analysis of real-time big data to derive actionable intelligence in enterprise applications in several domains, such as in industry and agriculture. It explores possible automated solutions in daily life, including structures for smart cities and automated home systems based on IoT technology, as well as health care systems that manage large amounts of data (big data) to improve clinical decisions. The book addresses the security and privacy of the IoT and big data technologies, while also revealing the impact of IoT technologies on several scenarios in smart cities design. Intended as a comprehensive introduction, it offers in-depth analysis and provides scientists, engineers and professionals the latest techniques, frameworks and strategies used in IoT and big data technologies.

This book presents 3D3C platforms - three-dimensional systems for community, creation and commerce. It discusses tools including bots in social networks, team creativity, privacy, and virtual currencies & micropayments as well as their applications in areas like healthcare, energy, collaboration, and art. More than 20 authors from 10 countries share their experiences, research fi ndings and perspectives, off ering a comprehensive resource on the emerging fi eld of 3D3C worlds. The book is designed for both the novice and the expert as a way to unleash the emerging opportunities in 3D3C worlds. This Handbook maps with breadth and insight the exciting frontier of building virtual worlds with digital technologies. David Perkins, Research Professor, Harvard Graduate School of Education This book is from one of the most adventurous and energetic persons I have ever met. Yesha takes us into new undiscovered spaces and provides insight into phenomena of social interaction and immersive experiences that transform our lives. Cees de Bont, Dean of School of Design & Chair Professor of Design, School of Design of the Hong Kong Polytechnic University When you read 3D3C Platforms you realize what a domain like ours -- 3D printing -- can and should do for the world. Clearly we are just starting. Inspiring.David Reis, CEO, Stratasys Ltd This book provides a stunning overview regarding how virtual worlds are reshaping possibilities for identity and community. Th e range of topics addressed by the authors- from privacy and taxation to fashion and health care-provide a powerful roadmap for addressing the emerging potential of these online environments. Tom Boellstorff , Professor, Department of Anthropology, University of California, Irvine Handbook on 3D3C Platforms amassed a unique collection of multidisciplinary academic thinking. A primer on innovations that will touch every aspect of the human community in the 21st century. Eli Talmor, Professor, London Business School

This book presents machine learning models and algorithms to address big data classification problems. Existing machine learning techniques like the decision tree (a hierarchical approach), random forest (an ensemble hierarchical approach), and deep learning (a layered approach) are highly suitable for the system that can handle such problems. This book helps readers, especially students and newcomers to the field of big data and machine learning, to gain a quick understanding of the techniques and technologies; therefore, the theory, examples, and programs (Matlab and R) presented in this book have been simplified, hardcoded, repeated, or spaced for improvements. They provide vehicles to test and understand the complicated concepts of various topics in the field. It is expected that the readers adopt these programs to experiment with the examples, and then modify or write their own programs toward advancing their knowledge for solving more complex and challenging problems. The presentation format of this book focuses on simplicity, readability, and dependability so that both undergraduate and graduate students as well as new researchers, developers, and practitioners in this field can easily trust and grasp the concepts, and learn them effectively. It has been written to reduce the mathematical complexity and help the vast majority of readers to understand the topics and get interested in the field. This book consists of four parts, with the total of 14 chapters. The first part mainly focuses on the topics that are needed to help analyze and understand data and big data. The second part covers the topics that can explain the systems required for processing big data. The third part presents the topics required to understand and select machine learning techniques to classify big data. Finally, the fourth part concentrates on the topics that explain the scaling-up machine learning, an important solution for modern big data problems.

This book is focused on the development of a data integration framework for retrieval of biodiversity information from heterogeneous and distributed data sources. The data integration system proposed in this book links remote databases in a networked environment, supports heterogeneous databases and data formats, links databases hosted on multiple platforms, and provides data security for database owners by allowing them to keep and maintain their own data and to choose information to be shared and linked. The book is a useful guide for researchers, practitioners, and graduate-level students interested in learning state-of-the-art development for data integration in biodiversity.

Focusing on a data-centric perspective, this book provides a complete overview of data mining: its uses, methods, current technologies, commercial products, and future challenges. Three parts divide Data Mining: Part I describes technologies for data mining - database systems, warehousing, machine learning, visualization, decision support, statistics, parallel processing, and architectural support for data mining Part II presents tools and techniques - getting the data ready, carrying out the mining, pruning the results, evaluating outcomes, defining specific approaches, examining a specific technique based on logic programming, and citing literature and vendors for up-to-date information Part III examines emerging trends - mining distributed and heterogeneous data sources; multimedia data, such as text, images, video; mining data on the World Wide Web; metadata aspects of mining; and privacy issues. This self-contained book also contains two appendices providing exceptional information on technologies, such as data management, and artificial intelligence. Is there a need for mining? Do you have the right tools? Do you have the people to do the work? Do you have sufficient funds allocated to the project? All these answers must be answered before embarking on a project. Data Mining provides singular guidance on appropriate applications for specific techniques as well as thoroughly assesses valuable product information.

Big data technologies are used to achieve any type of analytics in a fast and predictable way, thus enabling better human and machine level decision making. Principles of distributed computing are the keys to big data technologies and analytics. The mechanisms related to data storage, data access, data transfer, visualization and predictive modeling using distributed processing in multiple low cost machines are the key considerations that make big data analytics possible within stipulated cost and time practical for consumption by human and machines. However, the current literature available in big data analytics needs a holistic perspective to highlight the relation between big data analytics and distributed processing for ease of understanding and practitioner use. This book fills the literature gap by addressing key aspects of distributed processing in big data analytics. The chapters tackle the essential concepts and patterns of distributed computing widely used in big data analytics. This book discusses also covers the main technologies which support distributed processing. Finally, this book provides insight into applications of big data analytics, highlighting how principles of distributed computing are used in those situations. Practitioners and researchers alike will find this book a valuable tool for their work, helping them to select the appropriate technologies, while understanding the inherent strengths and drawbacks of those technologies.

The aim of this book is to provide an internationally respected collection of scientific research methods, technologies and applications in the area of data science. This book can prove useful to the researchers, professors, research students and practitioners as it reports novel research work on challenging topics in the area surrounding data science. In this book, some of the chapters are written in tutorial style concerning machine learning algorithms, data analysis, information design, infographics, relevant applications, etc. The book is structured as follows: * Part I: Data Science: Theory, Concepts, and Algorithms This part comprises five chapters on data Science theory, concepts, techniques and algorithms. * Part II: Data Design and Analysis This part comprises five chapters on data design and analysis. * Part III: Applications and New Trends in Data Science This part comprises four chapters on applications and new trends in data science.

Big Data is everywhere. It shapes our lives in more ways than we know and understand. This comprehensive introduction unravels the complex terabytes that will continue to shape our lives in ways imagined and unimagined. Drawing on case studies like Amazon, Facebook, the FIFA World Cup and the Aadhaar scheme, this book looks at how Big Data is changing the way we behave, consume and respond to situations in the digital age. It looks at how Big Data has the potential to transform disaster management and healthcare, as well as prove to be authoritarian and exploitative in the wrong hands. The latest offering from the authors of Artificial Intelligence: Evolution, Ethics and Public Policy, this accessibly written volume is essential for the researcher in science and technology studies, media and culture studies, public policy and digital humanities, as well as being a beacon for the general reader to make sense of the digital age.

Pattern Recognition Algorithms for Data Mining addresses different pattern recognition (PR) tasks in a unified framework with both theoretical and experimental results. Tasks covered include data condensation, feature selection, case generation, clustering/classification, and rule generation and evaluation. This volume presents various theories, methodologies, and algorithms, using both classical approaches and hybrid paradigms. The authors emphasize large datasets with overlapping, intractable, or nonlinear boundary classes, and datasets that demonstrate granular computing in soft frameworks. Organized into eight chapters, the book begins with an introduction to PR, data mining, and knowledge discovery concepts. The authors analyze the tasks of multi-scale data condensation and dimensionality reduction, then explore the problem of learning with support vector machine (SVM). They conclude by highlighting the significance of granular computing for different mining tasks in a soft paradigm.

Oracle 11i E-Business Suite from the Front Lines is the first book to compile the tips, techniques, and practical advice for administering Oracle E-Business Suite 11i. The author examines Active Directory Utilities, patching, cloning, and the new features that 11i brings to the market. The book benefits those with limited experience with Oracle Application but with more extensive background in Oracle Database Administration. This volume is valuable to systems administrators or DBAs who have experience with older versions of Oracle Financials and want to expand their knowledge to include the changes inherent in 11i. The book details the steps in installing a new 11i environment, and explains the process of upgrading from a 10.7 or an 11.0.3 release. It also explores the techniques and results of migrating from one maintenance release of 11i to another. This analysis offers you real-world hints and recommendations to help you with day-to-day tuning, troubleshooting, and maintenance and will help you deliver reliable service to your end users. It is also a helpful tool that enables managers and co-workers to understand the daily challenges that Apps DBAs face.

This book presents the outcomes of the Third National Conference on Communication, Cloud and Big Data (CCB) held on November 2-3, 2018, at Sikkim Manipal Institute of Technology, Majitar, Sikkim. Featuring a number of papers from the conference, it explores various aspects of communication, computation, cloud, and big data, including routing in cognitive radio wireless sensor networks, big data security issues, routing in ad hoc networks, routing protocol for Internet of things (IoT), and algorithm for imaging quality enhancement.

Most widely available approaches to semantic integration provide ad-hoc, non-systematic, subjective manual mappings that lead to procrustean amalgamations to fit the target standard, an outcome that pleases no one. Written by experts in the field, Theories of Geographic Concepts: Ontological Approaches to Semantic Integration emphasizes the real issues involved in integrating existing geo-ontologies. The book addresses theoretical, formal, and pragmatic issues of geographic knowledge representation and integration based on an ontological approach. The authors highlight the importance of philosophical, cognitive, and formal theories in preserving the semantics of geographic concepts during ontology development and integration. They elucidate major theoretical issues, then introduce a number of formal tools. The book delineates a general framework with the necessary processes and guidelines to ontology integration and applies it to a selection of ontology integration cases. It concludes with a retrospection of key issues and identifies open research questions. Copiously illustrated, the book contains more than 80 illustrations and several examples to various approaches that provide a better understanding of the complexity of ontology integration tasks. The authors provide guidance on selecting the most appropriate approach and details on its application to indicative integration problems.

This open access book explores the collision between the sustainable energy transition and the Internet of Things (IoT). In that regard, this book's arrival is timely. Not only is the Internet of Things for energy applications, herein called the energy Internet of Things (eIoT), rapidly developing but also the transition towards sustainable energy to abate global climate is very much at the forefront of public discourse. It is within the context of these two dynamic thrusts, digitization and global climate change, that the energy industry sees itself undergoing significant change in how it is operated and managed. This book recognizes that they impose five fundamental energy management change drivers: 1.) the growing demand for electricity, 2.) the emergence of renewable energy resources, 3.) the emergence of electrified transportation, 4.) the deregulation of electric power markets, 5.) and innovations in smart grid technology. Together, they challenge many of the assumptions upon which the electric grid was first built. The goal of this book is to provide a single integrated picture of how eIoT can come to transform our energy infrastructure. This book links the energy management change drivers mentioned above to the need for a technical energy management solution. It, then, describes how eIoT meets many of the criteria required for such a technical solution. In that regard, the book stresses the ability of eIoT to add sensing, decision-making, and actuation capabilities to millions or perhaps even billions of interacting "smart" devices. With such a large scale transformation composed of so many independent actions, the book also organizes the discussion into a single multi-layer energy management control loop structure. Consequently, much attention is given to not just network-enabled physical devices but also communication networks, distributed control & decision making, and finally technical architectures and standards. Having gone into the detail of these many simultaneously developing technologies, the book returns to how these technologies when integrated form new applications for transactive energy. In that regard, it highlights several eIoT-enabled energy management use cases that fundamentally change the relationship between end users, utilities, and grid operators. Consequently, the book discusses some of the emerging applications for utilities, industry, commerce, and residences. The book concludes that these eIoT applications will transform today's grid into one that is much more responsive, dynamic, adaptive and flexible. It also concludes that this transformation will bring about new challenges and opportunities for the cyber-physical-economic performance of the grid and the business models of its increasingly growing number of participants and stakeholders.

As the use of digital technology has grown, so necessarily has the body of research into its effects at the personal, group and organizational levels, but there is no one book that looks at how digital technology has specifically influenced creativity. Digital Creativity: Individuals, Groups, and Organizations discusses all spectrums of influence that digital technologies have on creativity from the individual, team, and organization level. This book offers a new kind of creativity model encompassing all three levels of creativity. It combines each level into a unified creativity framework in which organizations regardless of their industry types could benefit in reengineering their business processes as well as strategies. For this purpose, the book considers various factors that would affect creativity- individuals' digital efficacy, heterogeneity among members (i.e., age, gender, races, tenure, education, and culture, etc), CMC (Computer-Mediated Communication), task complexity, exploitation, exploration, culture, organizational learning capability, and knowledge networks among members. This book introduces a theorized and systematic glimpse into the exciting realm of digital creativity. It is organized with contents starting from individuals to teams and ultimately to organizations, each with various techniques and cases. Each chapter shows how individuals, teams, and organizations can become more creative through use of digital technologies.

This book is focused on an emerging area, i.e. combination of IoT and semantic technologies, which should enable breaking the silos of local and/or domain-specific IoT deployments. Taking into account the way that IoT ecosystems are realized, several challenges can be identified. Among them of definite importance are (this list is, obviously, not exhaustive): (i) How to provide common representation and/or shared understanding of data that will enable analysis across (systematically growing) ecosystems? (ii) How to build ecosystems based on data flows? (iii) How to track data provenance? (iv) How to ensure/manage trust? (v) How to search for things/data within ecosystems? (vi) How to store data and assure its quality? Semantic technologies are often considered among the possible ways of addressing these (and other, related) questions. More precisely, in academic research and in industrial practice, semantic technologies materialize in the following contexts (this list is, also, not exhaustive, but indicates the breadth of scope of semantic technology usability): (i) representation of artefacts in IoT ecosystems and IoT networks, (ii) providing interoperability between heterogeneous IoT artefacts, (ii) representation of provenance information, enabling provenance tracking, trust establishment, and quality assessment, (iv) semantic search, enabling flexible access to data originating in different places across the ecosystem, (v) flexible storage of heterogeneous data. Finally, Semantic Web, Web of Things, and Linked Open Data are architectural paradigms, with which the aforementioned solutions are to be integrated, to provide production-ready deployments.

Most of the research aimed at counterterrorism, fraud detection, or other forensic applications assumes that this is a specialized application domain for mainstream knowledge discovery. Unfortunately, knowledge discovery changes completely when the datasets being used have been manipulated in order to conceal some underlying activity. Knowledge Discovery for Counterterrorism and Law Enforcement operates from the premise that detection algorithms must be rethought to be effective in this domain, and presents a new approach based on cutting-edge analysis for use in adversarial settings. Reveals How Criminals Conceal Information This volume focuses on four main forms of knowledge discovery: prediction, clustering, relationship discovery, and textual analysis. For each of these application areas, the author discusses opportunities for concealment that are available to criminals and reveals some of the tactics that can aid in detecting them. He reviews what is known about the different technologies for each area and evaluates their effectiveness. The book also supplies a preview of technologies currently under development and describes how they will fit in to existing approaches to knowledge discovery. Provides Proactive Formulas for Staying One Step Ahead of Adversaries While all knowledge-discovery systems are susceptible to manipulation, designers and users of algorithmic systems who are armed with the knowledge of these subversive tactics are better able to create systems to avoid these vulnerabilities. This book delineates an effective process for integrating knowledge-discovery tools, provides a unique understanding of the limits of the technology, and contains a clear presentation of the upsides and pitfalls of data collection. It is a powerful weapon in the arsenal of anyone confronting the increasingly sophistic

In recent years, a considerable amount of effort has been devoted, both in industry and academia, to improving maintenance. Time is a critical factor in maintenance, and efforts are placed to monitor, analyze, and visualize machine or asset data in order to anticipate to any possible failure, prevent damage, and save costs. The MANTIS Book aims to highlight the underpinning fundamentals of Condition-Based Maintenance related conceptual ideas, an overall idea of preventive maintenance, the economic impact and technical solution. The core content of this book describes the outcome of the Cyber-Physical System based Proactive Collaborative Maintenance project, also known as MANTIS, and funded by EU ECSEL Joint Undertaking under Grant Agreement n 662189. The ambition has been to support the creation of a maintenance-oriented reference architecture that support the maintenance data lifecycle, to enable the use of novel kinds of maintenance strategies for industrial machinery. The key enabler has been the fine blend of collecting data through Cyber-Physical Systems, and the usage of machine learning techniques and advanced visualization for the enhanced monitoring of the machines. Topics discussed include, in the context of maintenance: Cyber-Physical Systems, Communication Middleware, Machine Learning, Advanced Visualization, Business Models, Future Trends. An important focus of the book is the application of the techniques in real world context, and in fact all the work is driven by the pilots, all of them centered on real machines and factories. This book is suitable for industrial and maintenance managers that want to implement a new strategy for maintenance in their companies. It should give readers a basic idea on the first steps to implementing a maintenance-oriented platform or information system.

Expert Bytes: Computer Expertise in Forensic Documents - Players, Needs, Resources and Pitfalls -introduces computer scientists and forensic document examiners to the computer expertise of forensic documents and assists them with the design of research projects in this interdisciplinary field. This is not a textbook on how to perform the actual forensic document expertise or program expertise software, but a project design guide, an anthropological inquiry, and a technology, market, and policies review. After reading this book you will have deepened your knowledge on: What computational expertise of forensic documents is What has been done in the field so far and what the future looks like What the expertise is worth, what its public image is, and how to improve both Who is doing what in the field, where, and for how much How the expertise software functions The primary target readers are computer scientists and forensic document examiners, at the student and professional level. Paleographers, historians of science and technology, and scientific policy makers can also profit from the book. Concise and practical, featuring an attractive and functional layout design, the book is supplemented with graphical data representations, statistics, resource lists, and extensive references to facilitate further study.