"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

The successful implementation and deployment of Enterprise Resource Planning hinges on an organization's understanding of the complex and challenging issues involved in the process. And, the further development of information and communication technologies are presenting more challenges to organizations. ERP & Data Warehousing in Organizations: Issues and Challenges attempts to provide the most recent research and findings concerning these issues, in order to provide practical assessments and suggestions for managers in the process of developing such systems.

Whether you are brand new to data mining or working on your tenth predictive analytics project, "Commercial Data Mining" will be there for you as an accessible reference outlining the entire process and related themes. In this book, you'll learn that your organization does not need a huge volume of data or a Fortune 500 budget to generate business using existing information assets. Expert author David Nettleton guides you through the process from beginning to end and covers everything from business objectives to data sources, and selection to analysis and predictive modeling.

"Commercial Data Mining" includes case studies and practical examples from Nettleton's more than 20 years of commercial experience. Real-world cases covering customer loyalty, cross-selling, and audience prediction in industries including insurance, banking, and media illustrate the concepts and techniques explained throughout the book.
Illustrates cost-benefit evaluation of potential projects Includes vendor-agnostic advice on what to look for in off-the-shelf solutions as well as tips on building your own data mining tools Approachable reference can be read from cover to cover by readers of all experience levelsIncludes practical examples and case studies as well as actionable business insights from author's own experience

The objective of this book is to contribute to the development of the intelligent information and database systems with the essentials of current knowledge, experience and know-how. The book contains a selection of 40 chapters based on original research presented as posters during the 8th Asian Conference on Intelligent Information and Database Systems (ACIIDS 2016) held on 14-16 March 2016 in Da Nang, Vietnam. The papers to some extent reflect the achievements of scientific teams from 17 countries in five continents. The volume is divided into six parts: (a) Computational Intelligence in Data Mining and Machine Learning, (b) Ontologies, Social Networks and Recommendation Systems, (c) Web Services, Cloud Computing, Security and Intelligent Internet Systems, (d) Knowledge Management and Language Processing, (e) Image, Video, Motion Analysis and Recognition, and (f) Advanced Computing Applications and Technologies. The book is an excellent resource for researchers, those working in artificial intelligence, multimedia, networks and big data technologies, as well as for students interested in computer science and other related fields.

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.

The main objectives of this book are to expose key aspects that have a relevance when dealing with open data viewed from different perspectives and to provide appealing examples of how open data is implemented worldwide. The concept of open data as we know it today is the result of many different initiatives, both of a legislative and non-legislative nature, and promoted by a wide range of actors. Numerous regulatory antecedents to foster the concept of open data and embed it in national and international policy agendas have been undertaken on both sides of the Atlantic, as well as at a supranational level. The book highlights a number of the efforts made to promote open data in Europe, Asia and the United States. In addition to new insights, practical guidance and multiple disciplinary perspectives on open data, the book also addresses the transformation of current developments towards open data, which may be referred to as the democratisation of data. This book will support open data practitioners as well as open data scholars in their endeavours to promote open data implementation and research. Bastiaan van Loenen is associate professor and director of the Knowledge Centre Open Data at the Faculty of Architecture and The Built Environment of Delft University of Technology in the Netherlands, as is Glenn Vancauwenberghe, who is a post-doctoral researcher, and Joep Crompvoets is a professor at the Public Governance Institute of the KU Leuven in Belgium.

This volume contains nineteen research papers belonging to the areas of computational statistics, data mining, and their applications. Those papers, all written specifically for this volume, are their authors' contributions to honour and celebrate Professor Jacek Koronacki on the occcasion of his 70th birthday. The book's related and often interconnected topics, represent Jacek Koronacki's research interests and their evolution. They also clearly indicate how close the areas of computational statistics and data mining are.

This book examines the methodological foundations of the Big Data-driven world, formulates its concept within the frameworks of modern control methods and theories, and approaches the peculiarities of Control Technologies as a specific sphere of the Big Data-driven world, distinguished in the modern Digital Economy. The book studies the genesis of mathematical and information methods' transition from data analysis & processing to knowledge discovery and predictive analytics in the 21st century. In addition, it analyzes the conditions of development and implementation of Big Data analysis approaches in investigative activities and determines the role and meaning of global networks as platforms for the establishment of legislation and regulations in the Big Data-driven world. The book examines that world through the prism of Legislation Issues, substantiate the scientific and methodological approaches to studying modern mechanisms of terrorism and extremism counteraction in the conditions of new challenges of dissemination and accessibility of socially dangerous information. Systematization of successful experience of the Big Data solutions implementation in the different countries and analyze causal connections of the Digital Economy formation from the positions of new technological challenges is performed. The book's target audience includes scientists, students, PhD and Master students who conduct scientific research on the topic of Big Data not only in the field of IT& data science, but also in connection with legislative regulation aspects of the modern information society. It also includes practitioners and experts, as well as state authorities and representatives of international organizations interested in creating mechanisms for implementing Digital Economy projects in the Big Data-driven world.

This book presents a methodology to model and specify the data aspect of Web services, as it is overlooked by current standards for specifying Web services. The formal specification enables verification of service behavior, and the proposed methodology is based on formal methods and design-by-contract techniques. The Web has evolved from an information sharing medium to a wide-scale environment for sharing capabilities or services. Currently, URLs not only point to documents and images, but are also used to invoke services that potentially change the state of the Web. Major online organizations today, such as Amazon, PayPal and FedEx, provide services for users and consumers. They also allow third-party vendors to resell their services. In both cases, this requires precise and complete specification of service offerings. Several online discussions demonstrate the challenges faced by these organizations and others while describing their data-centric Web services. These challenges surrounding data specification can lead consumers to use a service erroneously. Case studies demonstrate how formal methods, and specifically design-by-contract techniques, can be leveraged to address the lack of formal specification of data when it comes to developing Web applications such as Amazon and PayPal.

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 volume complies a set of Data Mining techniques and new applications in real biomedical scenarios. Chapters focus on innovative data mining techniques, biomedical datasets and streams analysis, and real applications. Written in the highly successful Methods in Molecular Biology series format, chapters are thought to show to Medical Doctors and Engineers the new trends and techniques that are being applied to Clinical Medicine with the arrival of new Information and Communication technologies Authoritative and practical, Data Mining in Clinical Medicine seeks to aid scientists with new approaches and trends in the field.

This book introduces new logic primitives for electronic design automation tools. The author approaches fundamental EDA problems from a different, unconventional perspective, in order to demonstrate the key role of rethinking EDA solutions in overcoming technological limitations of present and future technologies. The author discusses techniques that improve the efficiency of logic representation, manipulation and optimization tasks by taking advantage of majority and biconditional logic primitives. Readers will be enabled to accelerate formal methods by studying core properties of logic circuits and developing new frameworks for logic reasoning engines.

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.

This book offers practical advice on managing enterprise modeling (EM) projects and facilitating participatory EM sessions. Modeling activities often involve groups of people, and models are created in a participatory way. Ensuring that this is done efficiently requires dedicated individuals who know how to organize modeling projects and sessions, how to manage discussions during these sessions, and what aspects influence the success and efficiency of modeling in practice. The book also includes a summary of the theoretical background to EM, although participatory modeling can also be used in conjunction with other methods that are not made for EM, such as those made for goal-oriented requirements engineering and information systems analysis. The first four chapters present an overview of enterprise modeling from various viewpoints (including methods, processes and organizational challenges), providing a background for those that need to refresh their basic knowledge. The next six chapters form the core of the book and detail the roles and competences needed in an EM project, typical stakeholder behaviors and how to handle them, tools and methods for managing participatory modeling and facilitation, and how to train modeling experts for these social aspects of modeling. Lastly, a concluding chapter presents a summary and an outlook on current research in participatory EM. This book is intended for anybody who wants to learn more about how to facilitate participatory modeling in practice and how to set up and carry out EM projects. It does not require any in-depth knowledge about specific EM methods and tools, and can be used by students and lecturers for courses on participatory modeling, and by practitioners wanting to extend their knowledge of social and organizational topics to become an experienced facilitator and EM project manager.

This book discusses the challenges in the convergence of technologies as the Internet of Things (IoT) evolves. These include sensing, computing, information processing, networking, and controlling intelligent technologies. The contributors first provide a survey of various assessment and evaluation approaches available for successful convergence. They then go on to cover several operational ideas to apply. The contributors then discuss the challenges involved bridging gaps in computation and the communication process, hidden networks, intelligent decision making, human-to-machine perception and large-scale IoT environments. The contributors aim to provide the reader an overview of trends in IoT in terms of performability and traffic modeling and efforts that can be spent in assessing the graceful degradation in IoT paradigms. Provides a survey of IoT assessment and evaluation approaches; Covers new and innovative operational ideas that apply to the IoT industry and the industries it affects; Includes chapters from researchers and industry leaders in IoT from around the world.

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.

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 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.

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.

The proceedings from the eighth KMO conference represent the findings of this international meeting which brought together researchers and developers from industry and the academic world to report on the latest scientific and technical advances on knowledge management in organizations. This conference provided an international forum for authors to present and discuss research focused on the role of knowledge management for innovative services in industries, to shed light on recent advances in social and big data computing for KM as well as to identify future directions for researching the role of knowledge management in service innovation and how cloud computing can be used to address many of the issues currently facing KM in academia and industrial sectors.

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.

This book constitutes the refereed post-conference proceedings of the 10th IFIP WG 5.14 International Conference on Computer and Computing Technologies in Agriculture, CCTA 2016, held in Dongying, China, in October 2016. The 55 revised papers presented were carefully reviewed and selected from 128 submissions. They cover a wide range of interesting theories and applications of information technology in agriculture, including intelligent sensing, cloud computing, key technologies of the Internet of Things, precision agriculture, animal husbandry information technology, including Internet + modern animal husbandry, livestock big data platform and cloud computing applications, intelligent breeding equipment, precision production models, water product networking and big data , including fishery IoT, intelligent aquaculture facilities, and big data applications.

"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"

Commercial Web search engines such as Google, Yahoo, and Bing are used every day by millions of people across the globe. With their ever-growing refinement and usage, it has become increasingly difficult for academic researchers to keep up with the collection sizes and other critical research issues related to Web search, which has created a divide between the information retrieval research being done within academia and industry. Such large collections pose a new set of challenges for information retrieval researchers. In this work, Metzler describes highly effective information retrieval models for both smaller, classical data sets, and larger Web collections. In a shift away from heuristic, hand-tuned ranking functions and complex probabilistic models, he presents feature-based retrieval models. The Markov random field model he details goes beyond the traditional yet ill-suited bag of words assumption in two ways. First, the model can easily exploit various types of dependencies that exist between query terms, eliminating the term independence assumption that often accompanies bag of words models. Second, arbitrary textual or non-textual features can be used within the model. As he shows, combining term dependencies and arbitrary features results in a very robust, powerful retrieval model. In addition, he describes several extensions, such as an automatic feature selection algorithm and a query expansion framework. The resulting model and extensions provide a flexible framework for highly effective retrieval across a wide range of tasks and data sets. A Feature-Centric View of Information Retrieval provides graduate students, as well as academic and industrial researchers in the fields of information retrieval and Web search with a modern perspective on information retrieval modeling and Web searches.