This book is about the rise of data as a driver of innovation and economic growth. It charts the evolution of business data as a valuable resource and explores some of the key business, economic and social issues surrounding the data-driven revolution we are currently going through. Readers will gain an understanding of the historical underpinnings of the data business and why the collection and use of data has been driven by commercial needs. Readers will also gain insights into the rise of the modern data-driven technology giants, their business models and the reasons for their success. Alongside this, some of the key social issues including privacy are considered and the challenges these pose to policymakers and regulators. Finally, the impact of pervasive computing and the Internet of Things (IoT) is explored in the context of the new sources of data that are being generated. This book is useful for students and practitioners wanting to better understand the origins and drivers of the current technological revolution and the key role that data plays in innovation and business success.

Optimize Your Chemical Database Design and Use of Relational Databases in Chemistry helps programmers and users improve their ability to search and manipulate chemical structures and information, especially when using chemical database "cartridges". It illustrates how the organizational, data integrity, and extensibility properties of relational databases are best utilized when working with chemical information. The author facilitates an understanding of existing relational database schemas and shows how to design new schemas that contain tables of data and chemical structures. By using database extension cartridges, he provides methods to properly store and search chemical structures. He explains how to download and install a fully functioning database using free, open-source chemical extension cartridges within PostgreSQL. The author also discusses how to access a database on a computer network using both new and existing applications. Through examples of good database design, this book shows you that relational databases are the best way to store, search, and operate on chemical information.

This book compares four parameters of problems in arbitrary information systems: complexity of problem representation and complexity of deterministic, nondeterministic, and strongly nondeterministic decision trees for problem solving. Deterministic decision trees are widely used as classifiers, as a means of knowledge representation, and as algorithms. Nondeterministic (strongly nondeterministic) decision trees can be interpreted as systems of true decision rules that cover all objects (objects from one decision class). This book develops tools for the study of decision trees, including bounds on complexity and algorithms for construction of decision trees for decision tables with many-valued decisions. It considers two approaches to the investigation of decision trees for problems in information systems: local, when decision trees can use only attributes from the problem representation; and global, when decision trees can use arbitrary attributes from the information system. For both approaches, it describes all possible types of relationships among the four parameters considered and discusses the algorithmic problems related to decision tree optimization. The results presented are useful for researchers who apply decision trees and rules to algorithm design and to data analysis, especially those working in rough set theory, test theory and logical analysis of data. This book can also be used as the basis for graduate courses.

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.

The datafication of our world offers huge challenges and opportunities for social science. The 'data-drivenness' of computational research can occur at the expense of theoretical reflection and interpretation. Additionally, it can be difficult to reconcile the 'quantitative' dimensions of big data with the 'qualitative' sensibilities needed for its understanding. At the same time, this opens up possibilities for reimagining key principles of social inquiry. In this experimental and provocative book, Simon Lindgren argues that a hybrid approach to data and theory must be developed in order to make sense of today's ambivalent, turbulent, and media-saturated political landscape. He pushes for the development of a critical science of data, joining the interpretive theoretical and ethical sensibilities of social science with the predictive and prognostic powers of data science and computational methods. In order for theories and research methods to be more useful and relevant, they must be dismantled and put together in new, alternative, and unexpected ways. Data Theory is essential reading for social scientists and data scientists, as well as students taking courses in social theory and data, digital methods, big data, and data and society.

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 provides an overview of the resources and research projects that are bringing Big Data and High Performance Computing (HPC) on converging tracks. It demystifies Big Data and HPC for the reader by covering the primary resources, middleware, applications, and tools that enable the usage of HPC platforms for Big Data management and processing.Through interesting use-cases from traditional and non-traditional HPC domains, the book highlights the most critical challenges related to Big Data processing and management, and shows ways to mitigate them using HPC resources. Unlike most books on Big Data, it covers a variety of alternatives to Hadoop, and explains the differences between HPC platforms and Hadoop.Written by professionals and researchers in a range of departments and fields, this book is designed for anyone studying Big Data and its future directions. Those studying HPC will also find the content valuable.

Automatic Indexing and Abstracting of Document Texts summarizes the latest techniques of automatic indexing and abstracting, and the results of their application. It also places the techniques in the context of the study of text, manual indexing and abstracting, and the use of the indexing descriptions and abstracts in systems that select documents or information from large collections. Important sections of the book consider the development of new techniques for indexing and abstracting. The techniques involve the following: using text grammars, learning of the themes of the texts including the identification of representative sentences or paragraphs by means of adequate cluster algorithms, and learning of classification patterns of texts. In addition, the book is an attempt to illuminate new avenues for future research. Automatic Indexing and Abstracting of Document Texts is an excellent reference for researchers and professionals working in the field of content management and information retrieval.

The central purpose of this collection of essays is to make a creative addition to the debates surrounding the cultural heritage domain. In the 21st century the world faces epochal changes which affect every part of society, including the arenas in which cultural heritage is made, held, collected, curated, exhibited, or simply exists. The book is about these changes; about the decentring of culture and cultural heritage away from institutional structures towards the individual; about the questions which the advent of digital technologies is demanding that we ask and answer in relation to how we understand, collect and make available Europe's cultural heritage. Cultural heritage has enormous potential in terms of its contribution to improving the quality of life for people, understanding the past, assisting territorial cohesion, driving economic growth, opening up employment opportunities and supporting wider developments such as improvements in education and in artistic careers. Given that spectrum of possible benefits to society, the range of studies that follow here are intended to be a resource and stimulus to help inform not just professionals in the sector but all those with an interest in cultural heritage.

The internet has launched the world into an era into which enormous amounts of data are generated every day through technologies with both positive and negative consequences. This often refers to big data . This book explores big data in organisations operating in the criminology and criminal justice fields. Big data entails a major disruption in the ways we think about and do things, which certainly applies to most organisations including those operating in the criminology and criminal justice fields. Big data is currently disrupting processes in most organisations - how different organisations collaborate with one another, how organisations develop products or services, how organisations can identify, recruit, and evaluate talent, how organisations can make better decisions based on empirical evidence rather than intuition, and how organisations can quickly implement any transformation plan, to name a few. All these processes are important to tap into, but two underlying processes are critical to establish a foundation that will permit organisations to flourish and thrive in the era of big data - creating a culture more receptive to big data and implementing a systematic data analytics-driven process within the organisation. Written in a clear and direct style, this book will appeal to students and scholars in criminology, criminal justice, sociology, and cultural studies but also to government agencies, corporate and non-corporate organisations, or virtually any other institution impacted by big data.

Cultural forces govern a synergistic relationship among information institutions that shapes their roles collectively and individually. Cultural synergy is the combination of perception- and behavior-shaping knowledge within, between, and among groups. Our hyperlinked era makes information-sharing among institutions critically important for scholarship as well as for the advancement of humankind. Information institutions are those that have, or share in, the mission to preserve, conserve, and disseminate information objects and their informative content. A central idea is the notion of social epistemology that information institutions arise culturally from social forces of the cultures they inhabit, and that their purpose is to disseminate that culture. All information institutions are alike in critical ways. Intersecting lines of cultural mission are trajectories for synergy for allowing us to perceive the universe of information institutions as interconnected and evolving and moving forward in distinct ways for the improvement of the condition of humankind through the building up of its knowledge base and of its information-sharing processes. This book is an exploration of the cultural synergy that can be realized by seeing commonalities among information institutions (sometimes also called cultural heritage institutions): museums, libraries, and archives. The hyperlinked era of the Semantic Web makes information sharing among institutions critically important for scholarship as well as the advancement of mankind. The book addresses the origins of cultural information institutions, the history of the professions that run them, and the social imperative of information organization as a catalyst for semantic synergy.

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.

These proceedings gather cutting-edge papers exploring the principles, techniques, and applications of Microservices in Big Data Analytics. The ICETCE-2019 is the latest installment in a successful series of annual conferences that began in 2011. Every year since, it has significantly contributed to the research community in the form of numerous high-quality research papers. This year, the conference's focus was on the highly relevant area of Microservices in Big Data Analytics.

This book explores recent advances in the Internet of things (IoT) via advanced technologies and provides an overview of most aspects which are relevant for advance secure, distributed, decentralized blockchain technology in the Internet of things, their applications, and industry IoT. The book provides an in-depth analysis of the step-by-step evolution of IoT to create a change by enhancing the productivity of industries. It introduces how connected things, data, and their communication (data sharing) environment build a transparent, reliable, secure environment for people, processes, systems, and services with the help of blockchain technology.

This edited book adopts a cognitive perspective to provide breadth and depth to state-of-the-art research related to understanding, analyzing, predicting and improving one of the most prominent and important classes of behavior of modern humans, information search. It is timely as the broader research area of cognitive computing and cognitive technology have recently attracted much attention, and there has been a surge in interest to develop systems and technology that are more compatible with human cognitive abilities. Divided into three interlocking sections, the first introduces the foundational concepts of information search from a cognitive computing perspective to highlight the research questions and approaches that are shared among the contributing authors. Relevant concepts from psychology, information and computing sciences are addressed. The second section discusses methods and tools that are used to understand and predict information search behavior and how the cognitive perspective can provide unique insights into the complexities of the behavior in various contexts. The final part highlights a number of areas of applications of which education and training, collaboration and conversational search interfaces are important ones. Understanding and Improving Information Search - A Cognitive Approach includes contributions from cognitive psychologists, information and computing scientists around the globe, including researchers from Europe (France, Netherlands, Germany), the US, and Asia (India, Japan), providing their unique but coherent perspectives to the core issues and questions most relevant to our current understanding of information search behavior and improving information search.

This book presents the latest advances in machine intelligence and big data analytics to improve early warning of cyber-attacks, for cybersecurity intrusion detection and monitoring, and malware analysis. Cyber-attacks have posed real and wide-ranging threats for the information society. Detecting cyber-attacks becomes a challenge, not only because of the sophistication of attacks but also because of the large scale and complex nature of today's IT infrastructures. It discusses novel trends and achievements in machine intelligence and their role in the development of secure systems and identifies open and future research issues related to the application of machine intelligence in the cybersecurity field. Bridging an important gap between machine intelligence, big data, and cybersecurity communities, it aspires to provide a relevant reference for students, researchers, engineers, and professionals working in this area or those interested in grasping its diverse facets and exploring the latest advances on machine intelligence and big data analytics for cybersecurity applications.

This book presents the implementation of novel concepts and solutions, which allows to enhance the cyber security of administrative and industrial systems and the resilience of economies and societies to cyber and hybrid threats. This goal can be achieved by rigorous information sharing, enhanced situational awareness, advanced protection of industrial processes and critical infrastructures, and proper account of the human factor, as well as by adequate methods and tools for analysis of big data, including data from social networks, to find best ways to counter hybrid influence. The implementation of these methods and tools is examined here as part of the process of digital transformation through incorporation of advanced information technologies, knowledge management, training and testing environments, and organizational networking. The book is of benefit to practitioners and researchers in the field of cyber security and protection against hybrid threats, as well as to policymakers and senior managers with responsibilities in information and knowledge management, security policies, and human resource management and training.

In this complete revision and expansion of his first SQL Puzzles book, Joe Celko challenges you with his trickiest puzzles and then helps solve them with a variety of solutions and explanations. Joe demonstrates the thought processes that are involved in attacking a problem from an SQL perspective to help advanced database programmers solve the puzzles you frequently face. These techniques not only help with the puzzle at hand, but help develop the mindset needed to solve the many difficult SQL puzzles you face every day. Of course, part of the fun is to see whether or not you can write better solutions than Joe s.
* A great collection of tricky SQL puzzles with a variety of solutions and explanations.
* Uses the proven format of puzzles and solutions to provide a user-friendly, practical look into SQL programming problems - many of which will help users solve their own problems.
* New edition features:
Many new puzzles added
Dozens of new solutions to puzzles, and using features in SQL-99
Code is edited to conform to SQL STYLE rules
New chapter on temporal query puzzles
New chapter on common misconceptions about SQL and RDBMS that leads to problems"

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.

Focusing on three applications of data mining, Design and Implementation of Data Mining Tools explains how to create and employ systems and tools for intrusion detection, Web page surfing prediction, and image classification. Mainly based on the authors' own research work, the book takes a practical approach to the subject. The first part of the book reviews data mining techniques, such as artificial neural networks and support vector machines, as well as data mining applications. The second section covers the design and implementation of data mining tools for intrusion detection. It examines various designs and performance results, along with the strengths and weaknesses of the approaches. The third part presents techniques to solve the WWW prediction problem. The final part describes models that the authors have developed for image classification. Showing step by step how data mining tools are developed, this hands-on guide discusses the performance results, limitations, and unique contributions of data mining systems. It provides essential information for technologists to decide on the tools to select for a particular application, for developers to focus on alternative designs if an approach is unsuitable, and for managers to choose whether to proceed with a data mining project.

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 gathers selected papers from the KES-IDT-2020 Conference, held as a Virtual Conference on June 17-19, 2020. The aim of the annual conference was to present and discuss the latest research results, and to generate new ideas in the field of intelligent decision-making. However, the range of topics discussed during the conference was definitely broader and covered methods in e.g. classification, prediction, data analysis, big data, data science, decision support, knowledge engineering, and modeling in such diverse areas as finance, cybersecurity, economics, health, management and transportation. The Problems in Industry 4.0 and IoT are also addressed. The book contains several sections devoted to specific topics, such as Intelligent Data Processing and its Applications High-Dimensional Data Analysis and its Applications Multi-Criteria Decision Analysis - Theory and Applications Large-Scale Systems for Intelligent Decision-Making and Knowledge Engineering Decision Technologies and Related Topics in Big Data Analysis of Social and Financial Issues Decision-Making Theory for Economics

The book proposes techniques, with an emphasis on the financial sector, which will make recommendation systems both accurate and explainable. The vast majority of AI models work like black box models. However, in many applications, e.g., medical diagnosis or venture capital investment recommendations, it is essential to explain the rationale behind AI systems decisions or recommendations. Therefore, the development of artificial intelligence cannot ignore the need for interpretable, transparent, and explainable models. First, the main idea of the explainable recommenders is outlined within the background of neuro-fuzzy systems. In turn, various novel recommenders are proposed, each characterized by achieving high accuracy with a reasonable number of interpretable fuzzy rules. The main part of the book is devoted to a very challenging problem of stock market recommendations. An original concept of the explainable recommender, based on patterns from previous transactions, is developed; it recommends stocks that fit the strategy of investors, and its recommendations are explainable for investment advisers.

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.

The Unified Modeling Language is rapidly gaining acceptance as the mechanism of choice to model complex software systems at various steps of their specification and design, using a number of orthogonal views that illustrate use cases, class diagrams and even detailed state machine-based behaviors of objects.
UML for Real: Design of Embedded Real-Time Systems aims to show the reality of UML as a medium for specification and implementation of real-time systems, illustrating both the current capabilities and limits of UML for this task, and future directions that will improve its usefulness for real-time and embedded product design. It will also cover selected applications examples. The book is an edited volume of solicited chapters.
The table of contents covers:

-UML and the Real-time/Embedded Domain, with chapters on the role of UML in software development and on UML and Real-Time Systems.
-Representing Key Real-Time Concepts with UML, with chapters on logical structure, on modeling system-level behavior using MSCs and extensions, on platform modeling, on hardware and software object modeling, on fine-grain and high-level patterns for real-time systems, on modeling Quality Of Service and metric time, and finally on performance and schedulability analysis using UML.
-Specific Applications, with chapters on UML in the automotive and telecom domains.
-Process and Tools, with chapters on software performance engineering and on UML tools for real-time processes.