Drawn from the US National Science Foundation's Symposium on Next Generation of Data Mining and Cyber-Enabled Discovery for Innovation (NGDM 07), Next Generation of Data Mining explores emerging technologies and applications in data mining as well as potential challenges faced by the field.

Gathering perspectives from top experts across different disciplines, the book debates upcoming challenges and outlines computational methods. The contributors look at how ecology, astronomy, social science, medicine, finance, and more can benefit from the next generation of data mining techniques. They examine the algorithms, middleware, infrastructure, and privacy policies associated with ubiquitous, distributed, and high performance data mining. They also discuss the impact of new technologies, such as the semantic web, on data mining and provide recommendations for privacy-preserving mechanisms.

The dramatic increase in the availability of massive, complex data from various sources is creating computing, storage, communication, and human-computer interaction challenges for data mining. Providing a framework to better understand these fundamental issues, this volume surveys promising approaches to data mining problems that span an array of disciplines.

A comprehensive guide to mastering the most advanced Hadoop 3 concepts Key Features Get to grips with the newly introduced features and capabilities of Hadoop 3 Crunch and process data using MapReduce, YARN, and a host of tools within the Hadoop ecosystem Sharpen your Hadoop skills with real-world case studies and code Book DescriptionApache Hadoop is one of the most popular big data solutions for distributed storage and for processing large chunks of data. With Hadoop 3, Apache promises to provide a high-performance, more fault-tolerant, and highly efficient big data processing platform, with a focus on improved scalability and increased efficiency. With this guide, you'll understand advanced concepts of the Hadoop ecosystem tool. You'll learn how Hadoop works internally, study advanced concepts of different ecosystem tools, discover solutions to real-world use cases, and understand how to secure your cluster. It will then walk you through HDFS, YARN, MapReduce, and Hadoop 3 concepts. You'll be able to address common challenges like using Kafka efficiently, designing low latency, reliable message delivery Kafka systems, and handling high data volumes. As you advance, you'll discover how to address major challenges when building an enterprise-grade messaging system, and how to use different stream processing systems along with Kafka to fulfil your enterprise goals. By the end of this book, you'll have a complete understanding of how components in the Hadoop ecosystem are effectively integrated to implement a fast and reliable data pipeline, and you'll be equipped to tackle a range of real-world problems in data pipelines. What you will learn Gain an in-depth understanding of distributed computing using Hadoop 3 Develop enterprise-grade applications using Apache Spark, Flink, and more Build scalable and high-performance Hadoop data pipelines with security, monitoring, and data governance Explore batch data processing patterns and how to model data in Hadoop Master best practices for enterprises using, or planning to use, Hadoop 3 as a data platform Understand security aspects of Hadoop, including authorization and authentication Who this book is forIf you want to become a big data professional by mastering the advanced concepts of Hadoop, this book is for you. You'll also find this book useful if you're a Hadoop professional looking to strengthen your knowledge of the Hadoop ecosystem. Fundamental knowledge of the Java programming language and basics of Hadoop is necessary to get started with this book.

Since the initial work on constrained clustering, there have been numerous advances in methods, applications, and our understanding of the theoretical properties of constraints and constrained clustering algorithms. Bringing these developments together, Constrained Clustering: Advances in Algorithms, Theory, and Applications presents an extensive collection of the latest innovations in clustering data analysis methods that use background knowledge encoded as constraints.

"Algorithms"

The first five chapters of this volume investigate advances in the use of instance-level, pairwise constraints for partitional and hierarchical clustering. The book then explores other types of constraints for clustering, including cluster size balancing, minimum cluster size, and cluster-level relational constraints.

"Theory"

It also describes variations of the traditional clustering under constraints problem as well as approximation algorithms with helpful performance guarantees.

"Applications"

The book ends by applying clustering with constraints to relational data, privacy-preserving data publishing, and video surveillance data. It discusses an interactive visual clustering approach, a distance metric learning approach, existential constraints, and automatically generated constraints.

With contributions from industrial researchers and leading academic experts who pioneered the field, this volume delivers thorough coverage of the capabilities and limitations of constrained clustering methods as well as introduces new types of constraints and clustering algorithms.

Data Mining for Design and Marketing shows how to design and integrate data mining tools into human thinking processes in order to make better business decisions, especially in designing and marketing products and systems. The expert contributors discuss how data mining can identify valuable consumer patterns, which aid marketers and designers in detecting consumers' needs. They also explore visualization tools based on the computational methods of data mining. Discourse analysis, chance discovery, knowledge discovery, formal concept analysis, and an adjacency matrix are just some of the novel approaches covered. The book explains how these methods can be applied to website design, the retrieval of scientific articles from a database, personalized e-commerce support tools, and more. Through the techniques of data mining, this book demonstrates how to effectively design business processes and develop competitive products and services. By embracing data mining tools, businesses can better understand the behavior and needs of their customers.

As it is with building a house, most of the work necessary to build a data warehouse is neither visible nor obvious when looking at the completed product. While it may be easy to plan for a data warehouse that incorporates all the right concepts, taking the steps needed to create a warehouse that is as functional and user-friendly as it is theoretically sound, is not especially easy. That's the challenge that "Building and Maintaining""a Data Warehouse" answers.

Based on a foundation of industry-accepted principles, this work provides an easy-to-follow approach that is cohesive and holistic. By offering the perspective of a successful data warehouse, as well as that of a failed one, this workdetails those factors that must be accomplished and those that are best avoided.

Organized to logically progress from more general to specific information, this valuable guide-

- Presents areas of a data warehouse individually and in sequence, showing how each piece becomes a working part of the whole

- Examines the concepts and principles that are at the foundation of every successful data warehouse

- Explains how to recognize and attend to problematic gaps in an established data warehouse

- Provides the big picture perspective that planners and executives require

Those considering the planning and creation of a data warehouse, as well as those who've already built one will profit greatly from the insights garnered by the author during his years of creating and gathering information on state-of-the-art data warehouses that are accessible, convenient, and reliable.

A quick and reliable way to build proven databases for core business functions
Industry experts raved about The Data Model Resource Book when it was first published in March 1997 because it provided a simple, cost-effective way to design databases for core business functions. Len Silverston has now revised and updated the hugely successful First Edition, while adding a companion volume to take care of more specific requirements of different businesses. Each volume is accompanied by a CD-ROM, which is sold separately. Each CD-ROM provides powerful design templates discussed in the books in a ready-to-use electronic format, allowing companies and individuals to develop the databases they need at a fraction of the cost and a third of the time it would take to build them from scratch.
Updating the data models from the First Edition CD-ROM, this resource allows database developers to quickly load a core set of data models and customize them to support a wide range of business functions.

This research monograph is highly contextual in the present era of spatial/spatio-temporal data explosion. The overall text contains many interesting results that are worth applying in practice, while it is also a source of intriguing and motivating questions for advanced research on spatial data science. The monograph is primarily prepared for graduate students of Computer Science, who wish to employ probabilistic graphical models, especially Bayesian networks (BNs), for applied research on spatial/spatio-temporal data. Students of any other discipline of engineering, science, and technology, will also find this monograph useful. Research students looking for a suitable problem for their MS or PhD thesis will also find this monograph beneficial. The open research problems as discussed with sufficient references in Chapter-8 and Chapter-9 can immensely help graduate researchers to identify topics of their own choice. The various illustrations and proofs presented throughout the monograph may help them to better understand the working principles of the models. The present monograph, containing sufficient description of the parameter learning and inference generation process for each enhanced BN model, can also serve as an algorithmic cookbook for the relevant system developers.

The popularity of enterprise architecture (EA) has increased in the last two decades, in both business and academic domains. Despite the cumulative interest from all sectors, the implementation and practice of EA have been entangled with numerous challenges and complexities. Consequently, some organisations continue to theorise the concept, which has ramifications on practice and ROI. This has led to many studies that have been conducted, to understand the complexities impacting the implementation and practice of EA in organisations. Yet, the trajectory of some convolutions remain a mystery in many quarters. This attributes to the struggle to articulate the value of EA in many environments. Hence many organisations find it difficult to apply EA for strategic management of modern information technology (IT) solutions. Enterprise Architecture for strategic Management of Modern IT Solutions provides guidance on how to employ EA in deploying and managing IT solutions from pragmatic and implementable perspectives. Until now, implementation and practice of EA have been slow, despite its growing popularity and interest from all sectors. This book employs sociotechnical theories such as actor-network theory (ANT) and structuration theory (ST) as lenses to examine and explain why and how challenges and complexities exist and derail the implementation or practice of EA in organisations. This serves to enable practitioners and readers to gain fresh insights on why the challenges exist and how they can be addressed in creating collaborative capabilities for business enhancement, sustainability, and competitiveness. The book provides detailed insights on how to apply EA for organisational purposes, from three main fronts. First, it explains the implications that lack of understanding of EA have on organisational activities and processes. Second, it examines the challenges and complexities that hinder the implementation and practice of EA in organisations. Third, it proposes models and frameworks on how EA can be applied for strategic management of modern IT solutions in organisations. Written for postgraduates, researchers, academics, and professionals in the fields of EA, IT, and information systems, this book provides a valuable resource that will enable and enhance implementation and practice of EA including future studies.

utilizes the Consolidated Serial Homicide Offender Database, one of the largest and most robust open access databases of multiple murders available illustrated with in-depth case studies of SHOs, such as Felix Vail, Michael Sumpter, the Seminole Heights Killer, and the Austin Bomber provides commentary from those who have used these patterning methods in practice, in addition to laying out how to put the current suite of data tools to use within organizations

One of the "six best books for data geeks" - Financial Times With over 200 images and extensive how-to and how-not-to examples, this new edition has everything students and scholars need to understand and create effective data visualisations. Combining 'how to think' instruction with a 'how to produce' mentality, this book takes readers step-by-step through analysing, designing, and curating information into useful, impactful tools of communication. With this book and its extensive collection of online support, readers can: Decide what visualisations work best for their data and their audience using the chart gallery See data visualisation in action and learn the tools to try it themselves Follow online checklists, tutorials, and exercises to build skills and confidence Get advice from the UK's leading data visualisation trainer on everything from getting started to honing the craft.

Most events and activities in today's world are ordinarily captured using photos, videos and other multimedia content. Such content has some limitation of storing data and fetching them effectively. Three-dimensional continuous PC animation is the most proper media to simulate these occasions and activities. This book focuses on futuristic trends and innovations in multimedia systems using big data, IoT and cloud technologies. The authors present recent advancements in multimedia systems as they relate to various application areas such as healthcare services and agriculture-related industries. The authors also discuss human-machine interface design, graphics modelling, rendering/animation, image/graphics techniques/systems and visualization. They then go on to explore multimedia content adaptation for interoperable delivery. Finally, the book covers cultural heritage, philosophical/ethical/societal/international issues, standards-related virtual technology and multimedia uses. This book is intended for computer engineers and computer scientists developing applications for multimedia and virtual reality and professionals working in object design and visualization, transformation, modelling and animation of the real world. Features: Focuses on futuristic trends and innovations in multimedia systems using big data, IoT and cloud technologies Offers opportunity for state-of-the-art approaches, methodologies and systems, and innovative use of multimedia-based emerging technology services in different application areas Discusses human-machine interface design, graphics modelling, rendering/animation, image/graphics techniques/systems and visualization Covers cultural heritage, philosophical/ethical/societal/international issues, standards-related virtual technology and multimedia uses Explores multimedia content adaptation for interoperable delivery and recent advancements in multimedia systems in context to various application areas such as healthcare services and agriculture-related fields Rajeev Tiwari is a Senior Associate Professor in the School of Computer Science at the University of Petroleum and Energy Studies, Dehradun, India. Neelam Duhan is an Associate Professor in the Department of Computer Engineering at J. C. Bose University of Science and Technology, YMCA, Faridabad, India. Mamta Mittal has 18 years of teaching experience, and her research areas include data mining, big data, machine learning, soft computing and data structure. Abhineet Anand is a Professor in the Computer Science and Engineering Department at Chitkara University, Punjab, India. Muhammad Attique Khan is a lecturer of the Computer Science Department at HITEC University, Taxila, Pakistan.

With the massive amount of data produced and stored each year, reliable storage and retrieval of information is more crucial than ever. Robust coding and decoding techniques are critical for correcting errors and maintaining data integrity. Comprising chapters thoughtfully selected from the highly popular Coding and Signal Processing for Magnetic Recording Systems, Advanced Error Control Techniques for Data Storage Systems is a finely focused reference to the state-of-the-art error control and modulation techniques used in storage devices. The book begins with an introduction to error control codes, explaining the theory and basic concepts underlying the codes. Building on these concepts, the discussion turns to modulation codes, paying special attention to run-length limited sequences, followed by maximum transition run (MTR) and spectrum shaping codes. It examines the relationship between constrained codes and error control and correction systems from both code-design and architectural perspectives as well as techniques based on convolution codes. With a focus on increasing data density, the book also explores multi-track systems, soft decision decoding, and iteratively decodable codes such as Low-Density Parity-Check (LDPC) Codes, Turbo codes, and Turbo Product Codes. Advanced Error Control Techniques for Data Storage Systems offers a comprehensive collection of theory and techniques that is ideal for specialists working in the field of data storage systems.

Colonialism and the Modernist Moment in the Early Novels of Jean Rhys explores the postcolonial significance of Rhys's modernist period work, which depicts an urban scene more varied than that found in other canonical representations of the period. Arguing against the view that Rhys comes into her own as a colonial thinker only in the post-WWII period of her career, this study examines the austere insights gained by Rhys's active cultivation of her fringe status vis-a-vis British social life and artistic circles, where her sharp study of the aporias of marginal lives and the violence of imperial ideology is distilled into an artistic statement positing the outcome of the imperial venture as a state of homelessness across the board, for colonized and 'metropolitans' alike. Bringing to view heretofore overlooked emigre populations, or their children, alongside locals, Rhys's urbanites struggle to construct secure lives not simply as a consequence of commodification, alienation, or voluntary expatriation, but also as a consequence of marginalization and migration. This view of Rhys's early work asserts its vital importance to postcolonial studies, an importance that has been overlooked owing to an over hasty critical consensus that only one of her early novels contains significant colonial content. Yet, as this study demonstrates, proper consideration of colonial elements long considered only incidental illuminates a colonial continuum in Rhys's work from her earliest publications.

Until now, almost all books on logical database design focused exclusively on relational design. However, modern database management systems have added powerful features that have driven a movement away from truly normalized database design. Logical Database Design Principles reflects these recent changes. The book begins by covering traditional logical design principles, followed by an analysis of the normalizing and modeling of data. It then examines designing for specific purposes, such as object-oriented databases, online transaction processing (OLTP), and data warehouses. As the text progresses, it moves from the purely logical into some physical design, as determined by how the features of modern databases are implemented. Because this is a logical-design book, the authors attempt to make the examples database-neutral. Because various vendors implement features in different ways, this volume sometimes offers examples from a specific database management system. When necessary, it highlights a specific application, clearly demonstrating the concept being discussed.

The massive volume of data generated in modern applications can overwhelm our ability to conveniently transmit, store, and index it. For many scenarios, building a compact summary of a dataset that is vastly smaller enables flexibility and efficiency in a range of queries over the data, in exchange for some approximation. This comprehensive introduction to data summarization, aimed at practitioners and students, showcases the algorithms, their behavior, and the mathematical underpinnings of their operation. The coverage starts with simple sums and approximate counts, building to more advanced probabilistic structures such as the Bloom Filter, distinct value summaries, sketches, and quantile summaries. Summaries are described for specific types of data, such as geometric data, graphs, and vectors and matrices. The authors offer detailed descriptions of and pseudocode for key algorithms that have been incorporated in systems from companies such as Google, Apple, Microsoft, Netflix and Twitter.

Gets the reader up to date with an introduction to the concept of data science, its reasons and origin Focused on the mechanisms of extracting data along with classifications, architectural concepts, and business intelligence with predictive analysis Provides an overview of recent developments and applications on data linked to engineering and management perspectives Covers how data scientists, analysts, and program managers that are interested in productivity and improving their business can do so by incorporating a data science workflow effectively Presents recent trends, highlights future developments, along with discussing problems and offering suggested solutions

This book presents an improved design for service provisioning and allocation models that are validated through running genome sequence assembly tasks in a hybrid cloud environment. It proposes approaches for addressing scheduling and performance issues in big data analytics and showcases new algorithms for hybrid cloud scheduling. Scientific sectors such as bioinformatics, astronomy, high-energy physics, and Earth science are generating a tremendous flow of data, commonly known as big data. In the context of growing demand for big data analytics, cloud computing offers an ideal platform for processing big data tasks due to its flexible scalability and adaptability. However, there are numerous problems associated with the current service provisioning and allocation models, such as inefficient scheduling algorithms, overloaded memory overheads, excessive node delays and improper error handling of tasks, all of which need to be addressed to enhance the performance of big data analytics.

Written by two very experienced instructors, with more than thirty years of teaching experience between them; Presents material that is grounded in practical applications that are representative of the problems researchers encounter in real life; Teaches readers the core features of modern JavaScript; Covers programming with callbacks and promises; Describes how to build data services and data visualization;

Introduces design of experiments and digital twin concepts in simple lay terms for physicists to understand, adopt and adapt. Free from endless derivations equations are presented and explained strategically, explaining why it is imperative to use them and how they will help in your task at hand. Illustrations and simple explanation help readers visualize and absorb easily, difficult to understand concepts.

Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today's most popular machine learning methods. This book serves as a practitioner's guide to the machine learning process and is meant to help the reader learn to apply the machine learning stack within R, which includes using various R packages such as glmnet, h2o, ranger, xgboost, keras, and others to effectively model and gain insight from their data. The book favors a hands-on approach, providing an intuitive understanding of machine learning concepts through concrete examples and just a little bit of theory. Throughout this book, the reader will be exposed to the entire machine learning process including feature engineering, resampling, hyperparameter tuning, model evaluation, and interpretation. The reader will be exposed to powerful algorithms such as regularized regression, random forests, gradient boosting machines, deep learning, generalized low rank models, and more! By favoring a hands-on approach and using real word data, the reader will gain an intuitive understanding of the architectures and engines that drive these algorithms and packages, understand when and how to tune the various hyperparameters, and be able to interpret model results. By the end of this book, the reader should have a firm grasp of R's machine learning stack and be able to implement a systematic approach for producing high quality modeling results. Features: * Offers a practical and applied introduction to the most popular machine learning methods. * Topics covered include feature engineering, resampling, deep learning and more. * Uses a hands-on approach and real world data.

This book presents state-of-the-art data warehousing research and practice from an integrated business and computer science perspective - the first monograph to do so - and broadens the scope of data mining by discussing it in terms of data warehousing. The material, rooted in database management systems and artificial intelligence, brings the intelligent techniques associated with AI to the entire process of data warehousing, from preparing data and building data warehousing to analyzing data stored in the data warehouses using data mining.

Our world is rapidly becoming an Internet-based world, with tens of millions of homes, millions of businesses, and within a short period of time, possibly hundreds of millions of mobile professionals accessing the literal mother of all networks. One of the key problems affecting many Internet users, ranging from individual professionals to networking managers and administrators, is a lack of knowledge concerning Internet Protocol (IP) addressing.

A detailed reference guide, The ABCs of IP Addressing provides you with the detailed information about the key challenges of IP addressing and designing networks that work efficiently. It takes you from the basics of the binary numbering system to advanced topics in subnetting, network address translation, and configuring workstations, servers and routers. The book contains network tools that prepare you for testing and troubleshooting and highlights the need for techniques that conserve the use of IP addressing as well as the creation of a next generation IP addressing structure. While structured on a chapter by chapter basis, wherever possible each chapter was written to be independent, making the book convenient and easy to use in a hectic work environment.

While you may find it possible to design a network or configure a workstation to operate without a detailed knowledge of IP addressing, the end results are not very efficient. And in the quickly changing global business environment, inefficiency wastes your time and money, both valuable resources for gaining and maintaining the competitive edge. With The ABCs of IP Addressing you get the comprehensive coverage of applications and protocols that help you do your job faster and more effectively.

In recent years, machine learning has gained a lot of interest. Due to the advances in processor technology and the availability of large amounts of data, machine learning techniques have provided astounding results in areas such as object recognition or natural language processing. New approaches, e.g. deep learning, have provided groundbreaking outcomes in fields such as multimedia mining or voice recognition. Machine learning is now used in virtually every domain and deep learning algorithms are present in many devices such as smartphones, cars, drones, healthcare equipment, or smart home devices. The Internet, cloud computing and the Internet of Things produce a tsunami of data and machine learning provides the methods to effectively analyze the data and discover actionable knowledge. This book describes the most common machine learning techniques such as Bayesian models, support vector machines, decision tree induction, regression analysis, and recurrent and convolutional neural networks. It first gives an introduction into the principles of machine learning. It then covers the basic methods including the mathematical foundations. The biggest part of the book provides common machine learning algorithms and their applications. Finally, the book gives an outlook into some of the future developments and possible new research areas of machine learning and artificial intelligence in general. This book is meant to be an introduction into machine learning. It does not require prior knowledge in this area. It covers some of the basic mathematical principle but intends to be understandable even without a background in mathematics. It can be read chapter wise and intends to be comprehensible, even when not starting in the beginning. Finally, it also intends to be a reference book. Key Features: Describes real world problems that can be solved using Machine Learning Provides methods for directly applying Machine Learning techniques to concrete real world problems Demonstrates how to apply Machine Learning techniques using different frameworks such as TensorFlow, MALLET, R

If you're training a machine learning model but aren't sure how to put it into production, this book will get you there. Kubeflow provides a collection of cloud native tools for different stages of a model's lifecycle, from data exploration, feature preparation, and model training to model serving. This guide helps data scientists build production-grade machine learning implementations with Kubeflow and shows data engineers how to make models scalable and reliable. Using examples throughout the book, authors Holden Karau, Trevor Grant, Ilan Filonenko, Richard Liu, and Boris Lublinsky explain how to use Kubeflow to train and serve your machine learning models on top of Kubernetes in the cloud or in a development environment on-premises. Understand Kubeflow's design, core components, and the problems it solves Learn how to set up Kubeflow on a cloud provider or on an in-house cluster Train models using Kubeflow with popular tools including scikit-learn, TensorFlow, and Apache Spark Learn how to add custom stages such as serving and prediction Keep your model up-to-date with Kubeflow Pipelines Understand how to validate machine learning pipelines

A practical guide to the construction of thesauri for use in information retrieval. In recent years, new applications for thesauri have been emerging, for example, in front-end systems, cross-database searching, hypertext systems, expert systems and in natural-language processing. In-house thesauri are still needed for internal special collections. The fourth edition of this work has been fully revised and the bibliography much extended, in particular, to include web addresses.