This book presents the basics of search engines and their components. It introduces, for the first time, the concept of Cellular Automata in Web technology and discusses the prerequisites of Cellular Automata. In today's world, searching data from the World Wide Web is a common phenomenon for virtually everyone. It is also a fact that searching the tremendous amount of data from the Internet is a mammoth task - and handling the data after retrieval is even more challenging. In this context, it is important to understand the need for space efficiency in data storage. Though Cellular Automata has been utilized earlier in many fields, in this book the authors experiment with employing its strong mathematical model to address some critical issues in the field of Web Mining.

"Data Warehousing in the Age of the Big Data "will help you and your organization make the most of unstructured data with your existing data warehouse.

As Big Data continues to revolutionize how we use data, it doesn't have to create more confusion. Expert author Krish Krishnan helps you make sense of how Big Data fits into the world of data warehousing in clear and concise detail. The book is presented in three distinct parts. Part 1 discusses Big Data, its technologies and use cases from early adopters. Part 2 addresses data warehousing, its shortcomings, and new architecture options, workloads, and integration techniques for Big Data and the data warehouse. Part 3 deals with data governance, data visualization, information life-cycle management, data scientists, and implementing a Big Data-ready data warehouse. Extensive appendixes include case studies from vendor implementations and a special segment on how we can build a healthcare information factory.

Ultimately, this book will help you navigate through the complex layers of Big Data and data warehousing while providing you information on how to effectively think about using all these technologies and the architectures to design the next-generation data warehouse.
Learn how to leverage Big Data by effectively integrating it into your data warehouse. Includes real-world examples and use cases that clearly demonstrate Hadoop, NoSQL, HBASE, Hive, and other Big Data technologies Understand how to optimize and tune your current data warehouse infrastructure and integrate newer infrastructure matching data processing workloads and requirements

With the increasing advances in hardware technology for data collection, and advances in software technology (databases) for data organization, computer scientists have increasingly participated in the latest advancements of the outlier analysis field. Computer scientists, specifically, approach this field based on their practical experiences in managing large amounts of data, and with far fewer assumptions- the data can be of any type, structured or unstructured, and may be extremely large. Outlier Analysis is a comprehensive exposition, as understood by data mining experts, statisticians and computer scientists. The book has been organized carefully, and emphasis was placed on simplifying the content, so that students and practitioners can also benefit. Chapters will typically cover one of three areas: methods and techniques commonly used in outlier analysis, such as linear methods, proximity-based methods, subspace methods, and supervised methods; data domains, such as, text, categorical, mixed-attribute, time-series, streaming, discrete sequence, spatial and network data; and key applications of these methods as applied to diverse domains such as credit card fraud detection, intrusion detection, medical diagnosis, earth science, web log analytics, and social network analysis are covered.

This book is focused on the Internet of Things (IoT) services and smart environments that can be of assistance to the elderly and individuals living with dementia or some sensory impairment. The book outlines the requirements of the systems that aim to furnish some digital sensory or cognitive assistance to the individuals and their caregivers. Internet of Things and Smart Environments: Assistive Technologies for Disability, Dementia, and Aging covers the important evolutions of the IoT, the sensors, actuators, wireless communication and pervasive computing systems, and other enabling technologies that power up this megatrend infrastructure. The use of the IoT-based systems in improving the conventional assistive technologies and provisions of ambient assisted living are also covered. The book takes an impartial, and yet holistic, view to providing research insights and inspirations for more development works in the areas related to assistive IoT. It will show the potentials of using normally available interactive devices, like smartphones or smart TVs, which can be supplemented with low-cost gadgets or apps to provide assistive capabilities. It aims to accentuate the need for taking a comprehensive and combinatory view of the comprising topics and approaches that are based on the visions and ideas from all stakeholders. The book will examine these points and considerations to conclude with recommendations for future development works and research directions. This book can be of value to a diverse array of audience. The researchers and developers in healthcare and medicine, aged care and disability services, as well as those working in the IoT-related fields, may find many parts of this book useful and stimulating. It can be of great value to postgraduate and research students working in these areas. It can also be adapted for use in upper-level classroom courses relevant to communication and smart technologies, IoT applications, and assistive technologies. Many parts of the book can be of interest to the elderly and individuals living with a disability, as well as their families and caregivers. From an industry perspective, it can be of interest to software, hardware, and particularly app developers working on the IoT applications, smart homes and environments, and assistive technologies for the elderly and people living with disability or dementia.

This book is intended to present the state of the art in research on machine learning and big data analytics. The accepted chapters covered many themes including artificial intelligence and data mining applications, machine learning and applications, deep learning technology for big data analytics, and modeling, simulation, and security with big data. It is a valuable resource for researchers in the area of big data analytics and its applications.

This book provides a critical examination of how the choice of what to believe is represented in the standard model of belief change. In particular the use of possible worlds and infinite remainders as objects of choice is critically examined. Descriptors are introduced as a versatile tool for expressing the success conditions of belief change, addressing both local and global descriptor revision. The book presents dynamic descriptors such as Ramsey descriptors that convey how an agent's beliefs tend to be changed in response to different inputs. It also explores sentential revision and demonstrates how local and global operations of revision by a sentence can be derived as a special case of descriptor revision. Lastly, the book examines revocation, a generalization of contraction in which a specified sentence is removed in a process that may possibly also involve the addition of some new information to the belief set.

This edited volume presents a collection of lessons learned with, and research conducted on, OpenStreetMap, the goal being to promote the project's integration. The respective chapters address a) state-of-the-art and cutting-edge approaches to data quality analysis in OpenStreetMap, b) investigations on understanding OpenStreetMap contributors and the nature of their contributions, c) identifying patterns of contributions and contributors, d) applications of OpenStreetMap in different domains, e) mining value-added knowledge and information from OpenStreetMap, f) limitations in the analysis OpenStreetMap data, and g) integrating OpenStreetMap with commercial and non-commercial datasets. The book offers an ideal opportunity to present and disseminate a number of cutting-edge developments and applications in the field of geography, spatial statistics, GIS, social science, and cartography.

"Managing Data in Motion" describes techniques that have been developed for significantly reducing the complexity of managing system interfaces and enabling scalable architectures. Author April Reeve brings over two decades of experience to present a vendor-neutral approach to moving data between computing environments and systems. Readers will learn the techniques, technologies, and best practices for managing the passage of data between computer systems and integrating disparate data together in an enterprise environment.

The average enterprise's computing environment is comprised of hundreds to thousands computer systems that have been built, purchased, and acquired over time. The data from these various systems needs to be integrated for reporting and analysis, shared for business transaction processing, and converted from one format to another when old systems are replaced and new systems are acquired.

The management of the "data in motion" in organizations is rapidly becoming one of the biggest concerns for business and IT management. Data warehousing and conversion, real-time data integration, and cloud and "big data" applications are just a few of the challenges facing organizations and businesses today. "Managing Data in Motion" tackles these and other topics in a style easily understood by business and IT managers as well as programmers and architects.
Presents a vendor-neutral overview of the different technologies and techniques for moving data between computer systems including the emerging solutions for unstructured as well as structured data typesExplains, in non-technical terms, the architecture and components required to perform data integration Describes how to reduce the complexity of managing system interfaces and enable a scalable data architecture that can handle the dimensions of "Big Data"

This book presents articles from the International Conference on Blockchain Technology (IC-BCT) 2019, held in Mumbai, India, and highlights recent advances in the field. It brings together researchers and industry practitioners to show case their ideas linked to business case studies, and provides an opportunity for engineers, researchers, startups and professionals in the field of Blockchain technology to further collaboration.

This book investigates the coordinated power management of multi-tenant data centers that account for a large portion of the data center industry. The authors include discussion of their quick growth and their electricity consumption, which has huge economic and environmental impacts. This book covers the various coordinated management solutions in the existing literature focusing on efficiency, sustainability, and demand response aspects. First, the authors provide a background on the multi-tenant data center covering the stake holders, components, power infrastructure, and energy usage. Then, each power management mechanism is described in terms of motivation, problem formulation, challenges and solution.

Innovations in Database Design, Web Applications, and Information Systems Management presents ideal research in the areas of database theory, systems design, ontologies, and many more. Including examples of the convergence of ideas from various disciplines aimed at improving and developing the theory of information technology and management of information resources, this book is useful for researchers and practitioners in the IT field.

This book provides an opportunity for investigators, government officials, systems scientists, strategists, assurance researchers, owners, operators and maintainers of large, complex and advanced systems and infrastructures to update their knowledge with the state of best practice in the challenging domains whilst networking with the leading representatives, researchers and solution providers. Drawing on 12 years of successful events on information security, digital forensics and cyber-crime, the 13th ICGS3-20 conference aims to provide attendees with an information-packed agenda with representatives from across the industry and the globe. The challenges of complexity, rapid pace of change and risk/opportunity issues associated with modern products, systems, special events and infrastructures. In an era of unprecedented volatile, political and economic environment across the world, computer-based systems face ever more increasing challenges, disputes and responsibilities, and whilst the Internet has created a global platform for the exchange of ideas, goods and services, it has also created boundless opportunities for cyber-crime. As an increasing number of large organizations and individuals use the Internet and its satellite mobile technologies, they are increasingly vulnerable to cyber-crime threats. It is therefore paramount that the security industry raises its game to combat these threats. Whilst there is a huge adoption of technology and smart home devices, comparably, there is a rise of threat vector in the abuse of the technology in domestic violence inflicted through IoT too. All these are an issue of global importance as law enforcement agencies all over the world are struggling to cope.

This book features a collection of high-quality, peer-reviewed research papers presented at the 7th International Conference on Innovations in Computer Science & Engineering (ICICSE 2019), held at Guru Nanak Institutions, Hyderabad, India, on 16-17 August 2019. Written by researchers from academia and industry, the book discusses a wide variety of industrial, engineering, and scientific applications of the emerging techniques in the field of computer science.

As any application running on a computer makes use of the operating system, the potential impact of improving areas such as memory management, process scheduling, file systems, and device drivers is tremendous. The Handbook of Research on Advanced Operating Systems and Kernel Applications: Techniques and Technologies discusses non-distributed operating systems that benefit researchers, academicians, and practitioners desiring to delve into this subject area and learn more on this important field of study. This innovative publication includes an overview of topical issues, methods, and advancements in the field of one-processor operating systems.

This book highlights technical advances in knowledge management and their applications across a diverse range of domains. It explores the applications of knowledge computing methodologies in image processing, pattern recognition, health care and industrial contexts. The chapters also examine the knowledge engineering process involved in information management. Given its interdisciplinary nature, the book covers methods for identifying and acquiring valid, potentially useful knowledge sources. The ideas presented in the respective chapters illustrate how to effectively apply the perspectives of knowledge computing in specialized domains.

This book focuses on recent technical advancements and state-of-the art technologies for analyzing characteristic features and probabilistic modelling of complex social networks and decentralized online network architectures. Such research results in applications related to surveillance and privacy, fraud analysis, cyber forensics, propaganda campaigns, as well as for online social networks such as Facebook. The text illustrates the benefits of using advanced social network analysis methods through application case studies based on practical test results from synthetic and real-world data. This book will appeal to researchers and students working in these areas.

This book provides a general introduction to the most important geophysical exploration methods and their application to forensic sciences. It describes physical principles, campaign procedures and processing, as well as interpretation techniques, while also highlighting new acquisition and data analysis procedures. A large section of the book is devoted to applications, from measurements to the interpretation of data. Further, the book shows how to design and perform a forensic survey, and offers guidance on selecting the best method for the problem at hand, and on selecting the best type of data acquisition and processing. Written in straightforward language and chiefly intended as an introductory text for students in several scientific fields, the book also offers a useful guide for specialists who want to expand their expertise in this fascinating discipline.

This edited volume offers a clear in-depth overview of research covering a variety of issues in social search and recommendation systems. Within the broader context of social network analysis it focuses on important and up-coming topics such as real-time event data collection, frequent-sharing pattern mining, improvement of computer-mediated communication, social tagging information, search system personalization, new detection mechanisms for the identification of online user groups, and many more. The twelve contributed chapters are extended versions of conference papers as well as completely new invited chapters in the field of social search and recommendation systems. This first-of-its kind survey of current methods will be of interest to researchers from both academia and industry working in the field of social networks.

The need for collecting relevant data sources, mining useful knowledge from different forms of data sources and promptly reacting to situation change is ever increasing. Active mining is a collection of activities each solving a part of this need, but collectively achieving the mining objective through the spiral effect of these interleaving three steps. This book is a joint effort from leading and active researchers in Japan with a theme about active mining and a timely report on the forefront of data collection, user-centered mining and user interaction/reaction. It offers a contemporary overview of modern solutions with real-world applications, shares hard-learned experiences, and sheds light on future development of active mining.

This book addresses the challenging topic of modeling adaptive networks, which often manifest inherently complex behavior. Networks by themselves can usually be modeled using a neat, declarative, and conceptually transparent Network-Oriented Modeling approach. In contrast, adaptive networks are networks that change their structure; for example, connections in Mental Networks usually change due to learning, while connections in Social Networks change due to various social dynamics. For adaptive networks, separate procedural specifications are often added for the adaptation process. Accordingly, modelers have to deal with a less transparent, hybrid specification, part of which is often more at a programming level than at a modeling level. This book presents an overall Network-Oriented Modeling approach that makes designing adaptive network models much easier, because the adaptation process, too, is modeled in a neat, declarative, and conceptually transparent Network-Oriented Modeling manner, like the network itself. Thanks to this approach, no procedural, algorithmic, or programming skills are needed to design complex adaptive network models. A dedicated software environment is available to run these adaptive network models from their high-level specifications. Moreover, because adaptive networks are described in a network format as well, the approach can simply be applied iteratively, so that higher-order adaptive networks in which network adaptation itself is adaptive (second-order adaptation), too can be modeled just as easily. For example, this can be applied to model metaplasticity in cognitive neuroscience, or second-order adaptation in biological and social contexts. The book illustrates the usefulness of this approach via numerous examples of complex (higher-order) adaptive network models for a wide variety of biological, mental, and social processes. The book is suitable for multidisciplinary Master's and Ph.D. students without assuming much prior knowledge, although also some elementary mathematical analysis is involved. Given the detailed information provided, it can be used as an introduction to Network-Oriented Modeling for adaptive networks. The material is ideally suited for teaching undergraduate and graduate students with multidisciplinary backgrounds or interests. Lecturers will find additional material such as slides, assignments, and software.

In this book, the editors explain how students enrolled in two digital forensic courses at their institution are exposed to experiential learning opportunities, where the students acquire the knowledge and skills of the subject-matter while also learning how to adapt to the ever-changing digital forensic landscape. Their findings (e.g., forensic examination of different IoT devices) are also presented in the book. Digital forensics is a topic of increasing importance as our society becomes "smarter" with more of the "things" around us been internet- and inter-connected (e.g., Internet of Things (IoT) and smart home devices); thus, the increasing likelihood that we will need to acquire data from these things in a forensically sound manner. This book is of interest to both digital forensic educators and digital forensic practitioners, as well as students seeking to learn about digital forensics.

This book contains some selected papers from the International Conference on Extreme Learning Machine 2015, which was held in Hangzhou, China, December 15-17, 2015. This conference brought together researchers and engineers to share and exchange R&D experience on both theoretical studies and practical applications of the Extreme Learning Machine (ELM) technique and brain learning. This book covers theories, algorithms ad applications of ELM. It gives readers a glance of the most recent advances of ELM.

This volume details several important databases and data mining tools. Data Mining Techniques for the Life Sciences, Second Edition guides readers through archives of macromolecular three-dimensional structures, databases of protein-protein interactions, thermodynamics information on protein and mutant stability, "Kbdock" protein domain structure database, PDB_REDO databank, erroneous sequences, substitution matrices, tools to align RNA sequences, interesting procedures for kinase family/subfamily classifications, new tools to predict protein crystallizability, metabolomics data, drug-target interaction predictions, and a recipe for protein-sequence-based function prediction and its implementation in the latest version of the ANNOTATOR software suite. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, Data Mining Techniques for the Life Sciences, Second Edition aims to ensure successful results in the further study of this vital field.

This book combines the analytic principles of digital business and data science with business practice and big data. The interdisciplinary, contributed volume provides an interface between the main disciplines of engineering and technology and business administration. Written for managers, engineers and researchers who want to understand big data and develop new skills that are necessary in the digital business, it not only discusses the latest research, but also presents case studies demonstrating the successful application of data in the digital business.

This book is open access under a CC BY-NC 4.0 license. This volume presents several case studies highlighting the latest findings in Industry 4.0 projects utilizing S-BPM features. Their potential is explored in detail, while the limits of engineering a company from a communication-centred perspective are also discussed. After a general introduction and an overview of the book in chapter 1, chapter 2 starts by condensing the industrial challenges driven by the German "Industry 4.0" trend to form a concrete vision for future production industries. Subsequently, chapter 3 introduces the basic concepts of S-BPM and its capabilities, in particular for supporting the restructuring of processes. The next three chapters then present various case studies, e.g. at an SME offering the production of atypical, unique and special purpose machinery, equipment and technologically complex units particularly useful in the automotive and electronic industries; and at a further SME producing highly-customized floor cleaning machines. Rounding out the coverage, the last two chapters summarize the achievements and lessons learned with regard to the road ahead. Overall, the book provides a realistic portrait of the status quo based on current findings, and outlines the future activities to be pursued in order to establish stakeholder-centred digital production systems. As such, developers, educators, and practitioners will find both the conceptual background and results from the field reflecting the state-of-the-art in vertical and horizontal process integration.