Video Data Management and Information Retrieval combines the two important areas of research within computer technology and presents them in comprehensive, easy to understand manner. Video Data Management and Information Retrieval is ideal for graduates and under-graduates, as well as researchers working in either video data management or information retrieval. It takes an in depth look at many relevant topics within both video data management and information retrieval. In addition to dissecting those issues, the book also provides a ""big picture"" view of each topic. This shows the relevance of each issue and how those areas affect every one today.

"Advanced SQL:1999 - Understanding Object-Relational and Other Advanced Features" is the practitioner's handbook to the standard's advanced features. It is not a re-presentation of the standard, but rather an authoritative, in-depth guide to its practical application. Like its companion, "SQL:1999 - Understanding Relational Language Components," which explained the standard's basic features, this book will show you how to make your applications both effective and standard-compliant.

This handy reference has a modular format so you can explore specific topics with ease. It is equally useful to those upgrading from earlier versions of SQL and those with no previous experience. Written by the standard's distinguished editor, "Advanced SQL:1999" will complete your knowledge and support your skills like no other book can.
* Focuses entirely on the issues that matter to programmers who are connecting applications to databases.
* Details SQL:1999's object facilities, including structured user-defined types, typed tables, user-defined routines, and routine invocation.
* Examines facilities new to SQL, including those relating to on-line analytical processing (OLAP), management of external data (SQL/MED), and Java support.
* Covers the ongoing development of XML support.
* Includes appendices that cover the SQL:1999 annexes, a SQL:1999 example using UDTs, status codes, and useful information on the standardization process.

This guide helps you protect networks from unauthorized access. It discusses counter security threats, optimum use of encryption, integrity checks, and uniqueness mechanisms.

This book describes efforts to improve subject-independent automated classification techniques using a better feature extraction method and a more efficient model of classification. It evaluates three popular saliency criteria for feature selection, showing that they share common limitations, including time-consuming and subjective manual de-facto standard practice, and that existing automated efforts have been predominantly used for subject dependent setting. It then proposes a novel approach for anomaly detection, demonstrating its effectiveness and accuracy for automated classification of biomedical data, and arguing its applicability to a wider range of unsupervised machine learning applications in subject-independent settings.

This book highlights research in linking and mining data from across varied data sources. The authors focus on recent advances in this burgeoning field of multi-source data fusion, with an emphasis on exploratory and unsupervised data analysis, an area of increasing significance with the pace of growth of data vastly outpacing any chance of labeling them manually. The book looks at the underlying algorithms and technologies that facilitate the area within big data analytics, it covers their applications across domains such as smarter transportation, social media, fake news detection and enterprise search among others. This book enables readers to understand a spectrum of advances in this emerging area, and it will hopefully empower them to leverage and develop methods in multi-source data fusion and analytics with applications to a variety of scenarios. Includes advances on unsupervised, semi-supervised and supervised approaches to heterogeneous data linkage and fusion; Covers use cases of analytics over multi-view and heterogeneous data from across a variety of domains such as fake news, smarter transportation and social media, among others; Provides a high-level overview of advances in this emerging field and empowers the reader to explore novel applications and methodologies that would enrich the field.

Reengineering: An Objectoriented Model for Data, Knowledge and System Reengineering (S.M. Huang et al.). Uturn Methodology: A Database Reengineering Methodology Based on the Entity -Structure- Relationship Data Model (I.K. Jeong, D.K. Baik). The Management Perspective of Database Reengineering (C. Yau). Reengineering VSAM, IMS, and DL/1 Applications into Relational Databases (R. England). Reengineering Library Data: The Long Way from ADABAS to NIMARC (D. Aebi, R. Largo). Reverse Engineering in a Client'Server Environment Case Studies on Relational Database Design (B. Siu, J. Fong). Eliminating the Impedance Mismatch between Relational Systems and Objectoriented Programming Languages (J. Chen, Q. Huang). Generalization without Reorganization in a Simple Objectoriented DBMS (T. Beldjilali). Interoperability: Semantic Query Transformation: An Approach to Achieve Semantic Interoperability in Heterogeneous Application Domains (N. Bolloju). On Interoperability Verification and Testing of Objectoriented Databases (T.Y. Kuo, T.Y. Cheung). An Objectoriented Approach to Query Interoperability (J. Zhan, W.S. Luk). Building Parameterized Canonical Representations to Achieve Interoperability among Heterogeneous Databases (Y. Chang, L. Raschid). Flexible Transaction Management in an Interoperable Database Environment (W. Yu, F. Eliassen). A Pilot Survey of Database Reengineering for Data Interoperability (I.S.Y. Kwan). Designing Client-Server Applications for Enterprise Database Connectivity (C. Moffatt). Handling Terabyte Databases on Open Systems (T. Banham). Integration: Schema Integration Methodology including Structural Conflict Resolution and Checking Similarity (G. Suzuki, M. Yamamuro). Extensional Issues in Schema Integration (M. GarciaSolaco et al.). Towards Intelligent Integration of Heterogeneous Information Sources (S.B. Navathe, M.J. Donahoo). A Business ProcessDriven Multidatabase Integration Methodology (R.M. Muhlerger, M.E. Orlowska). A Database Integration System and an Example of Its Application (A.E. James). DEE: A Data Exchange Environment (G.N. Benadjaoud, B.T. David). Database Replica Management Strategies in Multidatabase Systems with Mobile Hosts (M. Faiz, A. Zaslavsky). Providing Multidatabase Access: An Association Approach (P. Missier et al.). Index.

As the world has entered the era of big data, there is a need to give a semantic perspective to the data to find unseen patterns, derive meaningful information, and make intelligent decisions. This 2-volume handbook set is a unique, comprehensive, and complete presentation of the current progress and future potential explorations in the field of data science and related topics. Handbook of Data Science with Semantic Technologies provides a roadmap for a new trend and future development of data science with semantic technologies. The first volume serves as an important guide towards applications of data science with semantic technologies for the upcoming generation and thus becomes a unique resource for both academic researchers and industry professionals. The second volume provides a roadmap for the deployment of semantic technologies in the field of data science that enables users to create intelligence through these technologies by exploring the opportunities while eradicating the current and future challenges. The set explores the optimal use of these technologies to provide the maximum benefit to the user under one comprehensive source. This set consisting of two separate volumes can be utilized independently or together as an invaluable resource for students, scholars, researchers, professionals, and practitioners in the field.

The use of game theoretic techniques is playing an increasingly important role in the network design domain. Understanding the background, concepts, and principles in using game theory approaches is necessary for engineers in network design. Game Theory Applications in Network Design provides the basic idea of game theory and the fundamental understanding of game theoretic interactions among network entities. The material in this book also covers recent advances and open issues, offering game theoretic solutions for specific network design issues. This publication will benefit students, educators, research strategists, scientists, researchers, and engineers in the field of network design.

Many techniques, algorithms, protocols and tools have been developed in the different aspects of cyber-security, namely, authentication, access control, availability, integrity, privacy, confidentiality and non-repudiation as they apply to both networks and systems. ""Web Services Security and E-Business"" focuses on architectures and protocols, while bringing together the understanding of security problems related to the protocols and applications of the Internet, and the contemporary solutions to these problems. ""Web Services Security and E-Business"" provides insight into uncovering the security risks of dynamically-created content, and how proper content management can greatly improve the overall security. It also studies the security lifecycle and how to respond to an attack, as well as the problems of site hijacking and phishing.

The aim of cryptography is to design primitives and protocols that withstand adversarial behavior. Information theoretic cryptography, how-so-ever desirable, is extremely restrictive and most non-trivial cryptographic tasks are known to be information theoretically impossible. In order to realize sophisticated cryptographic primitives, we forgo information theoretic security and assume limitations on what can be efficiently computed. In other words we attempt to build secure systems conditioned on some computational intractability assumption such as factoring, discrete log, decisional Diffie-Hellman, learning with errors, and many more. In this work, based on the 2013 ACM Doctoral Dissertation Award-winning thesis, we put forth new plausible lattice-based constructions with properties that approximate the sought after multilinear maps. The multilinear analog of the decision Diffie-Hellman problem appears to be hard in our construction, and this allows for their use in cryptography. These constructions open doors to providing solutions to a number of important open problems.

The widespread use of XML in business and scientific databases has prompted the development of methodologies, techniques, and systems for effectively managing and analyzing XML data. This has increasingly attracted the attention of different research communities, including database, information retrieval, pattern recognition, and machine learning, from which several proposals have been offered to address problems in XML data management and knowledge discovery. XML Data Mining: Models, Methods, and Applications aims to collect knowledge from experts of database, information retrieval, machine learning, and knowledge management communities in developing models, methods, and systems for XML data mining. This book addresses key issues and challenges in XML data mining, offering insights into the various existing solutions and best practices for modeling, processing, analyzing XML data, and for evaluating performance of XML data mining algorithms and systems.

What is text mining, and how can it be used? What relevance do these methods have to everyday work in information science and the digital humanities? How does one develop competences in text mining? Working with Text provides a series of cross-disciplinary perspectives on text mining and its applications. As text mining raises legal and ethical issues, the legal background of text mining and the responsibilities of the engineer are discussed in this book. Chapters provide an introduction to the use of the popular GATE text mining package with data drawn from social media, the use of text mining to support semantic search, the development of an authority system to support content tagging, and recent techniques in automatic language evaluation. Focused studies describe text mining on historical texts, automated indexing using constrained vocabularies, and the use of natural language processing to explore the climate science literature. Interviews are included that offer a glimpse into the real-life experience of working within commercial and academic text mining.

Even though many data analytics tools have been developed in the past years, their usage in the field of cyber twin warrants new approaches that consider various aspects including unified data representation, zero-day attack detection, data sharing across threat detection systems, real-time analysis, sampling, dimensionality reduction, resource-constrained data processing, and time series analysis for anomaly detection. Further study is required to fully understand the opportunities, benefits, and difficulties of data analytics and the internet of things in today's modern world. New Approaches to Data Analytics and Internet of Things Through Digital Twin considers how data analytics and the internet of things can be used successfully within the field of digital twin as well as the potential future directions of these technologies. Covering key topics such as edge networks, deep learning, intelligent data analytics, and knowledge discovery, this reference work is ideal for computer scientists, industry professionals, researchers, scholars, practitioners, academicians, instructors, and students.

The book provides a thorough treatment of set functions, games and capacities as well as integrals with respect to capacities and games, in a mathematical rigorous presentation and in view of application to decision making. After a short chapter introducing some required basic knowledge (linear programming, polyhedra, ordered sets) and notation, the first part of the book consists of three long chapters developing the mathematical aspects. This part is not related to a particular application field and, by its neutral mathematical style, is useful to the widest audience. It gathers many results and notions which are scattered in the literature of various domains (game theory, decision, combinatorial optimization and operations research). The second part consists of three chapters, applying the previous notions in decision making and modelling: decision under uncertainty, decision with multiple criteria, possibility theory and Dempster-Shafer theory.

Advanced Topics in Database Research is a series of books in the fields of database, software engineering, and systems analysis and design. They feature the latest research ideas and topics on how to enhance current database systems, improve information storage, refine existing database models, and develop advanced applications. Advanced Topics in Database Research, Volume 4 is a part of this series. Advanced Topics in Database Research, Volume 4 is enriched with authors who have submitted their best works for inclusion in this scholarly book. Advanced Topics in Database Research, Volume 4 is a useful reference and a valuable collection for both researchers and practitioners.

The work presented in this book is a combination of theoretical advancements of big data analysis, cloud computing, and their potential applications in scientific computing. The theoretical advancements are supported with illustrative examples and its applications in handling real life problems. The applications are mostly undertaken from real life situations. The book discusses major issues pertaining to big data analysis using computational intelligence techniques and some issues of cloud computing. An elaborate bibliography is provided at the end of each chapter. The material in this book includes concepts, figures, graphs, and tables to guide researchers in the area of big data analysis and cloud computing.

This book introduces Meaningful Purposive Interaction Analysis (MPIA) theory, which combines social network analysis (SNA) with latent semantic analysis (LSA) to help create and analyse a meaningful learning landscape from the digital traces left by a learning community in the co-construction of knowledge. The hybrid algorithm is implemented in the statistical programming language and environment R, introducing packages which capture - through matrix algebra - elements of learners' work with more knowledgeable others and resourceful content artefacts. The book provides comprehensive package-by-package application examples, and code samples that guide the reader through the MPIA model to show how the MPIA landscape can be constructed and the learner's journey mapped and analysed. This building block application will allow the reader to progress to using and building analytics to guide students and support decision-making in learning.

This book provides a comprehensive overview of different biomedical data types, including both clinical and genomic data. Thorough explanations enable readers to explore key topics ranging from electrocardiograms to Big Data health mining and EEG analysis techniques. Each chapter offers a summary of the field and a sample analysis. Also covered are telehealth infrastructure, healthcare information association rules, methods for mass spectrometry imaging, environmental biodiversity, and the global nonlinear fitness function for protein structures. Diseases are addressed in chapters on functional annotation of lncRNAs in human disease, metabolomics characterization of human diseases, disease risk factors using SNP data and Bayesian methods, and imaging informatics for diagnostic imaging marker selection. With the exploding accumulation of Electronic Health Records (EHRs), there is an urgent need for computer-aided analysis of heterogeneous biomedical datasets. Biomedical data is notorious for its diversified scales, dimensions, and volumes, and requires interdisciplinary technologies for visual illustration and digital characterization. Various computer programs and servers have been developed for these purposes by both theoreticians and engineers. This book is an essential reference for investigating the tools available for analyzing heterogeneous biomedical data. It is designed for professionals, researchers, and practitioners in biomedical engineering, diagnostics, medical electronics, and related industries.

Seeking to define a new approach to data management at the enterprise level, this work takes the reader beyond information management to information control, where the methods of data capture and manipulation supersede data quantity. Using the metadata approach ensures long-term, universal control of all data characteristics and improves the effectiveness of IT as a corporate function by minimizing the potential for errors, and improving communication and understanding between IT and other disciplines. By describing how to establish metadata management within an organization, this volume provides examples of data structure architectures, and reviews issues associated with metadata management in relation to the Internet and data warehousing. It offers to help the reader to control the factors that make data useable throughout an organization and manage data so that it becomes a valuable corporate asset. The book examines real-world business departments that can benefit from this approach and ways in which sets of metadata can be both autonomous and overlapping.

Intelligent Computing for Interactive System Design provides a comprehensive resource on what has become the dominant paradigm in designing novel interaction methods, involving gestures, speech, text, touch and brain-controlled interaction, embedded in innovative and emerging human-computer interfaces. These interfaces support ubiquitous interaction with applications and services running on smartphones, wearables, in-vehicle systems, virtual and augmented reality, robotic systems, the Internet of Things (IoT), and many other domains that are now highly competitive, both in commercial and in research contexts. This book presents the crucial theoretical foundations needed by any student, researcher, or practitioner working on novel interface design, with chapters on statistical methods, digital signal processing (DSP), and machine learning (ML). These foundations are followed by chapters that discuss case studies on smart cities, brain-computer interfaces, probabilistic mobile text entry, secure gestures, personal context from mobile phones, adaptive touch interfaces, and automotive user interfaces. The case studies chapters also highlight an in-depth look at the practical application of DSP and ML methods used for processing of touch, gesture, biometric, or embedded sensor inputs. A common theme throughout the case studies is ubiquitous support for humans in their daily professional or personal activities. In addition, the book provides walk-through examples of different DSP and ML techniques and their use in interactive systems. Common terms are defined, and information on practical resources is provided (e.g., software tools, data resources) for hands-on project work to develop and evaluate multimodal and multi-sensor systems. In a series of in-chapter commentary boxes, an expert on the legal and ethical issues explores the emergent deep concerns of the professional community, on how DSP and ML should be adopted and used in socially appropriate ways, to most effectively advance human performance during ubiquitous interaction with omnipresent computers. This carefully edited collection is written by international experts and pioneers in the fields of DSP and ML. It provides a textbook for students and a reference and technology roadmap for developers and professionals working on interaction design on emerging platforms.

This is the first textbook on attribute exploration, its theory, its algorithms forapplications, and some of its many possible generalizations. Attribute explorationis useful for acquiring structured knowledge through an interactive process, byasking queries to an expert. Generalizations that handle incomplete, faulty, orimprecise data are discussed, but the focus lies on knowledge extraction from areliable information source.The method is based on Formal Concept Analysis, a mathematical theory ofconcepts and concept hierarchies, and uses its expressive diagrams. The presentationis self-contained. It provides an introduction to Formal Concept Analysiswith emphasis on its ability to derive algebraic structures from qualitative data,which can be represented in meaningful and precise graphics.

This book, written by an international team of prominent authors, gathers the latest developments in mobile technologies for the acquisition, management, analysis and sharing of Volunteered Geographic Information (VGI) in the context of Earth observation. It is divided into three parts, the first of which presents case studies on the implementation of VGI for Earth observation, discusses the characteristics of volunteers' engagement in relation with their expertise and motivation, analyzes the tasks they are called upon to perform, and examines the available tools for developing VGI. In turn, the second part introduces readers to essential methods, techniques and algorithms used to develop mobile information systems based on VGI for distinct Earth observation tasks, while the last part focuses on the drawbacks and limitations of VGI with regard to the above-mentioned tasks and proposes innovative methods and techniques to help overcome them. Given its breadth of coverage, the book offers a comprehensive, practice-oriented reference guide for researchers and practitioners in the field of geo-information management.

This book explains the fundamentals of control theory for Internet of Things (IoT) systems and smart grids and its applications. It discusses the challenges imposed by large-scale systems, and describes the current and future trends and challenges in decision-making for IoT in detail, showing the ongoing industrial and academic research in the field of smart grid domain applications. It presents step-by-step design guidelines for the modeling, design, customisation and calibration of IoT systems applied to smart grids, in which the challenges increase with each system's increasing complexity. It also provides solutions and detailed examples to demonstrate how to use the techniques to overcome these challenges, as well as other problems related to decision-making for successful implementation. Further, it anaylses the features of decision-making, such as low-complexity and fault-tolerance, and uses open-source and publicly available software tools to show readers how they can design, implement and customise their own system control instantiations. This book is a valuable resource for power engineers and researchers, as it addresses the analysis and design of flexible decision-making mechanisms for smart grids. It is also of interest to students on courses related to control of large-scale systems, since it covers the use of state-of-the-art technology with examples and solutions in every chapter. And last but not least, it offers practical advice for professionals working with smart grids.

The ideas introduced in this book explore the relationships among rule based systems, machine learning and big data. Rule based systems are seen as a special type of expert systems, which can be built by using expert knowledge or learning from real data. The book focuses on the development and evaluation of rule based systems in terms of accuracy, efficiency and interpretability. In particular, a unified framework for building rule based systems, which consists of the operations of rule generation, rule simplification and rule representation, is presented. Each of these operations is detailed using specific methods or techniques. In addition, this book also presents some ensemble learning frameworks for building ensemble rule based systems.