Information Security and Ethics: Social and Organizational Issues brings together examples of the latest research from a number of international scholars addressing a wide range of issues significant to this important and growing field of study. These issues are relevant to the wider society, as well as to the individual, citizen, educator, student and industry professional. With individual chapters focusing on areas including web accessibility; the digital divide; youth protection and surveillance; Information security; education; ethics in the Information professions and Internet voting; this book provides an invaluable resource for students, scholars and professionals currently working in information Technology related areas.

With exponentially increasing amounts of data accumulating in real-time, there is no reason why one should not turn data into a competitive advantage. While machine learning, driven by advancements in artificial intelligence, has made great strides, it has not been able to surpass a number of challenges that still prevail in the way of better success. Such limitations as the lack of better methods, deeper understanding of problems, and advanced tools are hindering progress. Challenges and Applications of Data Analytics in Social Perspectives provides innovative insights into the prevailing challenges in data analytics and its application on social media and focuses on various machine learning and deep learning techniques in improving practice and research. The content within this publication examines topics that include collaborative filtering, data visualization, and edge computing. It provides research ideal for data scientists, data analysts, IT specialists, website designers, e-commerce professionals, government officials, software engineers, social media analysts, industry professionals, academicians, researchers, and students.

Big Data analytics is the complex process of examining big data to uncover information such as correlations, hidden patterns, trends and user and customer preferences, to allow organizations and businesses to make more informed decisions. These methods and technologies have become ubiquitous in all fields of science, engineering, business and management due to the rise of data-driven models as well as data engineering developments using parallel and distributed computational analytics frameworks, data and algorithm parallelization, and GPGPU programming. However, there remain potential issues that need to be addressed to enable big data processing and analytics in real time. In the first volume of this comprehensive two-volume handbook, the authors present several methodologies to support Big Data analytics including database management, processing frameworks and architectures, data lakes, query optimization strategies, towards real-time data processing, data stream analytics, Fog and Edge computing, and Artificial Intelligence and Big Data. The second volume is dedicated to a wide range of applications in secure data storage, privacy-preserving, Software Defined Networks (SDN), Internet of Things (IoTs), behaviour analytics, traffic predictions, gender based classification on e-commerce data, recommender systems, Big Data regression with Apache Spark, visual sentiment analysis, wavelet Neural Network via GPU, stock market movement predictions, and financial reporting. The two-volume work is aimed at providing a unique platform for researchers, engineers, developers, educators and advanced students in the field of Big Data analytics.

Relational databases have been predominant for many years and are used throughout various industries. The current system faces challenges related to size and variety of data thus the NoSQL databases emerged. By joining these two database models, there is room for crucial developments in the field of computer science. Bridging Relational and NoSQL Databases is an innovative source of academic content on the convergence process between databases and describes key features of the next database generation. Featuring coverage on a wide variety of topics and perspectives such as BASE approach, CAP theorem, and hybrid and native solutions, this publication is ideally designed for professionals and researchers interested in the features and collaboration of relational and NoSQL databases.

The optimization of traffic management operations has become a considerable challenge in today's global scope due to the significant increase in the number of vehicles, traffic congestions, and automobile accidents. Fortunately, there has been substantial progress in the application of intelligent computing devices to transportation processes. Vehicular ad-hoc networks (VANETs) are a specific practice that merges the connectivity of wireless technologies with smart vehicles. Despite its relevance, empirical research is lacking on the developments being made in VANETs and how certain intelligent technologies are being applied within transportation systems. IoT and Cloud Computing Advancements in Vehicular Ad-Hoc Networks provides emerging research exploring the theoretical and practical aspects of intelligent transportation systems and analyzing the modern techniques that are being applied to smart vehicles through cloud technology. Featuring coverage on a broad range of topics such as health monitoring, node localization, and fault tolerance, this book is ideally designed for network designers, developers, analysists, IT specialists, computing professionals, researchers, academics, and post-graduate students seeking current research on emerging computing concepts and developments in vehicular ad-hoc networks.

For Database Systems and Database Design and Application courses offered at the junior, senior and graduate levels in Computer Science departments. Written by well-known computer scientists, this introduction to database systems offers a comprehensive approach, focusing on database design, database use, and implementation of database applications and database management systems. The first half of the book provides in-depth coverage of databases from the point of view of the database designer, user, and application programmer. It covers the latest database standards SQL:1999, SQL/PSM, SQL/CLI, JDBC, ODL, and XML, with broader coverage of SQL than most other texts. The second half of the book provides in-depth coverage of databases from the point of view of the DBMS implementor. It focuses on storage structures, query processing, and transaction management. The book covers the main techniques in these areas with broader coverage of query optimisation than most other texts, along with advanced topics including multidimensional and bitmap indexes, distributed transactions, and information integration techniques.

Translation and communication between cultures can sometimes be a difficult process. Image-based assessments can offer a way for large populations to be tested on different subjects without having to create multiple testing programs. Cross-Cultural Analysis of Image-Based Assessments: Emerging Research and Opportunities is an innovative resource that offers insight into the application of visual assessments across a global and intercultural context. Highlighting applicable topics which include visual literacy, psychological assessments, assessment development, and equivalency measurements, this publication is ideal for psychologists, therapists, and researchers who would like to stay current on the most efficient way to test multi-cultural populations in various fields of knowledge.

Based on current literature and cutting-edge advances in the machine learning field, there are four algorithms whose usage in new application domains must be explored: neural networks, rule induction algorithms, tree-based algorithms, and density-based algorithms. A number of machine learning related algorithms have been derived from these four algorithms. Consequently, they represent excellent underlying methods for extracting hidden knowledge from unstructured data, as essential data mining tasks. Implementation of Machine Learning Algorithms Using Control-Flow and Dataflow Paradigms presents widely used data-mining algorithms and explains their advantages and disadvantages, their mathematical treatment, applications, energy efficient implementations, and more. It presents research of energy efficient accelerators for machine learning algorithms. Covering topics such as control-flow implementation, approximate computing, and decision tree algorithms, this book is an essential resource for computer scientists, engineers, students and educators of higher education, researchers, and academicians.

Information systems development underwent many changes as systems transitioned onto web-based forums. Complemented by advancements in security and technology, internet-based systems have become an information mainstay. The Handbook of Research on Contemporary Perspectives on Web-Based Systems is a critical scholarly resource that examines relevant theoretical frameworks, current practice guidelines, industry standards, and the latest empirical research findings in web-based systems. Featuring coverage on a wide range of topics such as data integration, mobile applications, and semantic web, this publication is geared toward computer engineers, IT specialists, software designers, professionals, researchers, and upper-level students seeking current and relevant research on the prevalence of these systems and advancements made to them.

Multimedia represents information in novel and varied formats. One of the most prevalent examples of continuous media is video. Extracting underlying data from these videos can be an arduous task. From video indexing, surveillance, and mining, complex computational applications are required to process this data. Intelligent Analysis of Multimedia Information is a pivotal reference source for the latest scholarly research on the implementation of innovative techniques to a broad spectrum of multimedia applications by presenting emerging methods in continuous media processing and manipulation. This book offers a fresh perspective for students and researchers of information technology, media professionals, and programmers.

Vehicular traffic congestion and accidents remain universal issues in today's world. Due to the continued growth in the use of vehicles, optimizing traffic management operations is an immense challenge. To reduce the number of traffic accidents, improve the performance of transportation systems, enhance road safety, and protect the environment, vehicular ad-hoc networks have been introduced. Current developments in wireless communication, computing paradigms, big data, and cloud computing enable the enhancement of these networks, equipped with wireless communication capabilities and high-performance processing tools. Cloud-Based Big Data Analytics in Vehicular Ad-Hoc Networks is a pivotal reference source that provides vital research on cloud and data analytic applications in intelligent transportation systems. While highlighting topics such as location routing, accident detection, and data warehousing, this publication addresses future challenges in vehicular ad-hoc networks and presents viable solutions. This book is ideally designed for researchers, computer scientists, engineers, automobile industry professionals, IT practitioners, academicians, and students seeking current research on cloud computing models in vehicular networks.

The Data and Analytics Playbook: Proven Methods for Governed Data and Analytic Quality explores the way in which data continues to dominate budgets, along with the varying efforts made across a variety of business enablement projects, including applications, web and mobile computing, big data analytics, and traditional data integration. The book teaches readers how to use proven methods and accelerators to break through data obstacles to provide faster, higher quality delivery of mission critical programs. Drawing upon years of practical experience, and using numerous examples and an easy to understand playbook, Lowell Fryman, Gregory Lampshire, and Dan Meers discuss a simple, proven approach to the execution of multiple data oriented activities. In addition, they present a clear set of methods to provide reliable governance, controls, risk, and exposure management for enterprise data and the programs that rely upon it. In addition, they discuss a cost-effective approach to providing sustainable governance and quality outcomes that enhance project delivery, while also ensuring ongoing controls. Example activities, templates, outputs, resources, and roles are explored, along with different organizational models in common use today and the ways they can be mapped to leverage playbook data governance throughout the organization.

For any organization, analysis of performance and effectiveness through available data allows for informed decision making. Data envelopment analysis, or DEA, is a popular, effective method that can be used to measure productive efficiency in operations management assessment. Data Envelopment Analysis and Effective Performance Assessment addresses the myriad of practical uses and innovative developments of DEA. Emphasizing the importance of analyzing productivity by measuring inputs, goals, economic growth, and performance, this book covers a wide breadth of innovative knowledge. This book is essential reading for managers, business professionals, students of business and ICT, and computer engineers.

Recent innovations have created significant developments in data storage and management. These new technologies now allow for greater security in databases and other applications. Decentralized Computing Using Block Chain Technologies and Smart Contracts: Emerging Research and Opportunities is a concise and informative source of academic research on the latest developments in block chain innovation and their application in contractual agreements. Highlighting pivotal discussions on topics such as cryptography, programming techniques, and decentralized computing, this book is an ideal publication for researchers, academics, professionals, students, and practitioners seeking content on utilizing block chains with smart contracts.

Digital libraries have been established worldwide to make information more readily available, and this innovation has changed the way information seekers interact with the data they are collecting. Faced with decentralized, heterogeneous sources, these users must be familiarized with high-level search activities in order to sift through large amounts of data. Information Seeking Behavior and Challenges in Digital Libraries addresses the problems of usability and search optimization in digital libraries. With topics addressing all aspects of information seeking activity, the research found in this book provides insight into library user experiences and human-computer interaction when searching online databases of all types. This book addresses the challenges faced by professionals in information management, librarians, developers, students of library science, and policy makers.

Intelligent Data Analysis for e-Learning: Enhancing Security and Trustworthiness in Online Learning Systems addresses information security within e-Learning based on trustworthiness assessment and prediction. Over the past decade, many learning management systems have appeared in the education market. Security in these systems is essential for protecting against unfair and dishonest conduct-most notably cheating-however, e-Learning services are often designed and implemented without considering security requirements. This book provides functional approaches of trustworthiness analysis, modeling, assessment, and prediction for stronger security and support in online learning, highlighting the security deficiencies found in most online collaborative learning systems. The book explores trustworthiness methodologies based on collective intelligence than can overcome these deficiencies. It examines trustworthiness analysis that utilizes the large amounts of data-learning activities generate. In addition, as processing this data is costly, the book offers a parallel processing paradigm that can support learning activities in real-time. The book discusses data visualization methods for managing e-Learning, providing the tools needed to analyze the data collected. Using a case-based approach, the book concludes with models and methodologies for evaluating and validating security in e-Learning systems. Indexing: The books of this series are submitted to EI-Compendex and SCOPUS

In today's digital world, the huge amount of data being generated is unstructured, messy, and chaotic in nature. Dealing with such data, and attempting to unfold the meaningful information, can be a challenging task. Feature engineering is a process to transform such data into a suitable form that better assists with interpretation and visualization. Through this method, the transformed data is more transparent to the machine learning models, which in turn causes better prediction and analysis of results. Data science is crucial for the data scientist to assess the trade-offs of their decisions regarding the effectiveness of the machine learning model implemented. Investigating the demand in this area today and in the future is a necessity. The Handbook of Research on Automated Feature Engineering and Advanced Applications in Data Science provides an in-depth analysis on both the theoretical and the latest empirical research findings on how features can be extracted and transformed from raw data. The chapters will introduce feature engineering and the recent concepts, methods, and applications with the use of various data types, as well as examine the latest machine learning applications on the data. While highlighting topics such as detection, tracking, selection techniques, and prediction models using data science, this book is ideally intended for research scholars, big data scientists, project developers, data analysts, and computer scientists along with practitioners, researchers, academicians, and students interested in feature engineering and its impact on data.

Diversity in user queries makes it challenging for search engines to effectively return a set of relevant results. Both user intentions to search the web and types of queries are vastly varied; consequently, horizontal and vertical search engines are developed to answer user queries more efficiently. However, these search engines present a variety of problems in web searching. Result Page Generation for Web Searching: Emerging Research and Opportunities is an essential reference publication that focuses on taking advantages from text and web mining in order to address the issues of recommendation and visualization in web searching. Highlighting a wide range of topics such as navigational searching, resource identification, and ambiguous queries, this book is ideally designed for computer engineers, web designers, programmers, academicians, researchers, and students.