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This book comprehensively covers the topic of mining biomedical text, images and visual features towards information retrieval. Biomedical and Health Informatics is an emerging field of research at the intersection of information science, computer science, and health care and brings tremendous opportunities and challenges due to easily available and abundant biomedical data for further analysis. The aim of healthcare informatics is to ensure the high-quality, efficient healthcare, better treatment and quality of life by analyzing biomedical and healthcare data including patient's data, electronic health records (EHRs) and lifestyle. Previously it was a common requirement to have a domain expert to develop a model for biomedical or healthcare; however, recent advancements in representation learning algorithms allows us to automatically to develop the model. Biomedical Image Mining, a novel research area, due to its large amount of biomedical images increasingly generates and stores digitally. These images are mainly in the form of computed tomography (CT), X-ray, nuclear medicine imaging (PET, SPECT), magnetic resonance imaging (MRI) and ultrasound. Patients' biomedical images can be digitized using data mining techniques and may help in answering several important and critical questions related to health care. Image mining in medicine can help to uncover new relationships between data and reveal new useful information that can be helpful for doctors in treating their patients.

CLOUD COMPUTING SOLUTIONS The main purpose of this book is to include all the cloud-related technologies in a single platform, so that researchers, academicians, postgraduate students, and those in the industry can easily understand the cloud-based ecosystems. This book discusses the evolution of cloud computing through grid computing and cluster computing. It will help researchers and practitioners to understand grid and distributed computing cloud infrastructure, virtual machines, virtualization, live migration, scheduling techniques, auditing concept, security and privacy, business models, and case studies through the state-of-the-art cloud computing countermeasures. This book covers the spectrum of cloud computing-related technologies and the wide-ranging contents will differentiate this book from others. The topics treated in the book include: The evolution of cloud computing from grid computing, cluster computing, and distributed systems; Covers cloud computing and virtualization environments; Discusses live migration, database, auditing, and applications as part of the materials related to cloud computing; Provides concepts of cloud storage, cloud strategy planning, and management, cloud security, and privacy issues; Explains complex concepts clearly and covers information for advanced users and beginners. Audience The primary audience for the book includes IT, computer science specialists, researchers, graduate students, designers, experts, and engineers who are occupied with research.

DATA MINING AND MACHINE LEARNING APPLICATIONS The book elaborates in detail on the current needs of data mining and machine learning and promotes mutual understanding among research in different disciplines, thus facilitating research development and collaboration. Data, the latest currency of today’s world, is the new gold. In this new form of gold, the most beautiful jewels are data analytics and machine learning. Data mining and machine learning are considered interdisciplinary fields. Data mining is a subset of data analytics and machine learning involves the use of algorithms that automatically improve through experience based on data. Massive datasets can be classified and clustered to obtain accurate results. The most common technologies used include classification and clustering methods. Accuracy and error rates are calculated for regression and classification and clustering to find actual results through algorithms like support vector machines and neural networks with forward and backward propagation. Applications include fraud detection, image processing, medical diagnosis, weather prediction, e-commerce and so forth. The book features: A review of the state-of-the-art in data mining and machine learning, A review and description of the learning methods in human-computer interaction, Implementation strategies and future research directions used to meet the design and application requirements of several modern and real-time applications for a long time, The scope and implementation of a majority of data mining and machine learning strategies. A discussion of real-time problems. Audience Industry and academic researchers, scientists, and engineers in information technology, data science and machine and deep learning, as well as artificial intelligence more broadly.

COMPUTATION IN BIOINFORMATICS Bioinformatics is a platform between the biology and information technology and this book provides readers with an understanding of the use of bioinformatics tools in new drug design. The discovery of new solutions to pandemics is facilitated through the use of promising bioinformatics techniques and integrated approaches. This book covers a broad spectrum of the bioinformatics field, starting with the basic principles, concepts, and application areas. Also covered is the role of bioinformatics in drug design and discovery, including aspects of molecular modeling. Some of the chapters provide detailed information on bioinformatics related topics, such as silicon design, protein modeling, DNA microarray analysis, DNA-RNA barcoding, and gene sequencing, all of which are currently needed in the industry. Also included are specialized topics, such as bioinformatics in cancer detection, genomics, and proteomics. Moreover, a few chapters explain highly advanced topics, like machine learning and covalent approaches to drug design and discovery, all of which are significant in pharma and biotech research and development. Audience Researchers and engineers in computation biology, information technology, bioinformatics, drug design, biotechnology, pharmaceutical sciences.

The first comprehensive overview of preprocessing, mining, and postprocessing of biological data Molecular biology is undergoing exponential growth in both the volume and complexity of biological data—and knowledge discovery offers the capacity to automate complex search and data analysis tasks. This book presents a vast overview of the most recent developments on techniques and approaches in the field of biological knowledge discovery and data mining (KDD)—providing in-depth fundamental and technical field information on the most important topics encountered. Written by top experts, Biological Knowledge Discovery Handbook: Preprocessing, Mining, and Postprocessing of Biological Data covers the three main phases of knowledge discovery (data preprocessing, data processing—also known as data mining—and data postprocessing) and analyzes both verification systems and discovery systems. BIOLOGICAL DATA PREPROCESSING Part A: Biological Data Management Part B: Biological Data Modeling Part C: Biological Feature Extraction Part D Biological Feature Selection BIOLOGICAL DATA MINING Part E: Regression Analysis of Biological Data Part F Biological Data Clustering Part G: Biological Data Classification Part H: Association Rules Learning from Biological Data Part I: Text Mining and Application to Biological Data Part J: High-Performance Computing for Biological Data Mining Combining sound theory with practical applications in molecular biology, Biological Knowledge Discovery Handbook is ideal for courses in bioinformatics and biological KDD as well as for practitioners and professional researchers in computer science, life science, and mathematics.

Provides the fundamentals, technologies, and best practices in designing, constructing and managing mission critical, energy efficient data centers Organizations in need of high-speed connectivity and nonstop systems operations depend upon data centers for a range of deployment solutions. A data center is a facility used to house computer systems and associated components, such as telecommunications and storage systems. It generally includes multiple power sources, redundant data communications connections, environmental controls (e.g., air conditioning, fire suppression) and security devices. With contributions from an international list of experts, The Data Center Handbook instructs readers to: Prepare strategic plan that includes location plan, site selection, roadmap and capacity planning Design and build «green» data centers, with mission critical and energy-efficient infrastructure Apply best practices to reduce energy consumption and carbon emissions Apply IT technologies such as cloud and virtualization Manage data centers in order to sustain operations with minimum costs Prepare and practice disaster reovery and business continuity plan The book imparts essential knowledge needed to implement data center design and construction, apply IT technologies, and continually improve data center operations.

The concept of digital risk, which has become ubiquitous in the media, sustains a number of myths and beliefs about the digital world. This book explores the opposite view of these ideologies by focusing on digital risks as perceived by actors in their respective contexts.

Perceptions and Analysis of Digital Risks identifies the different types of risks that concern actors and actually impact their daily lives, within education or various socio-professional environments. It provides an analysis of the strategies used by the latter to deal with these risks as they conduct their activities; thus making it possible to characterize the digital cultures and, more broadly, the informational cultures at work.

This book offers many avenues for action in terms of educating the younger generations, training teachers and leaders, and mediating risks.

This book is an introduction to the linguistic concepts of argumentation relevant for argument mining, an important research and development activity which can be viewed as a highly complex form of information retrieval, requiring high-level natural language processing technology. While the first four chapters develop the linguistic and conceptual aspects of argument expression, the last four are devoted to their application to argument mining. These chapters investigate the facets of argument annotation, as well as argument mining system architectures and evaluation. How annotations may be used to develop linguistic data and how to train learning algorithms is outlined. A simple implementation is then proposed. The book ends with an analysis of non-verbal argumentative discourse. Argument Mining is an introductory book for engineers or students of linguistics, artificial intelligence and natural language processing. Most, if not all, the concepts of argumentation crucial for argument mining are carefully introduced and illustrated in a simple manner.

Learn methods of data analysis and their application to real-world data sets This updated second edition serves as an introduction to data mining methods and models, including association rules, clustering, neural networks, logistic regression, and multivariate analysis. The authors apply a unified “white box” approach to data mining methods and models. This approach is designed to walk readers through the operations and nuances of the various methods, using small data sets, so readers can gain an insight into the inner workings of the method under review. Chapters provide readers with hands-on analysis problems, representing an opportunity for readers to apply their newly-acquired data mining expertise to solving real problems using large, real-world data sets. Data Mining and Predictive Analytics, Second Edition: Offers comprehensive coverage of association rules, clustering, neural networks, logistic regression, multivariate analysis, and R statistical programming language Features over 750 chapter exercises, allowing readers to assess their understanding of the new material Provides a detailed case study that brings together the lessons learned in the book Includes access to the companion website, www.dataminingconsultant.com, with exclusive password-protected instructor content Data Mining and Predictive Analytics, Second Edition will appeal to computer science and statistic students, as well as students in MBA programs, and chief executives.

Addresses the impacts of data mining on education and reviews applications in educational research teaching, and learning This book discusses the insights, challenges, issues, expectations, and practical implementation of data mining (DM) within educational mandates. Initial series of chapters offer a general overview of DM, Learning Analytics (LA), and data collection models in the context of educational research, while also defining and discussing data mining’s four guiding principles— prediction, clustering, rule association, and outlier detection. The next series of chapters showcase the pedagogical applications of Educational Data Mining (EDM) and feature case studies drawn from Business, Humanities, Health Sciences, Linguistics, and Physical Sciences education that serve to highlight the successes and some of the limitations of data mining research applications in educational settings. The remaining chapters focus exclusively on EDM’s emerging role in helping to advance educational research—from identifying at-risk students and closing socioeconomic gaps in achievement to aiding in teacher evaluation and facilitating peer conferencing. This book features contributions from international experts in a variety of fields. Includes case studies where data mining techniques have been effectively applied to advance teaching and learning Addresses applications of data mining in educational research, including: social networking and education; policy and legislation in the classroom; and identification of at-risk students Explores Massive Open Online Courses (MOOCs) to study the effectiveness of online networks in promoting learning and understanding the communication patterns among users and students Features supplementary resources including a primer on foundational aspects of educational mining and learning analytics Data Mining and Learning Analytics: Applications in Educational Research is written for both scientists in EDM and educators interested in using and integrating DM and LA to improve education and advance educational research.

Complete guidance for mastering the tools and techniques of the digital revolution With the digital revolution opening up tremendous opportunities in many fields, there is a growing need for skilled professionals who can develop data-intensive systems and extract information and knowledge from them. This book frames for the first time a new systematic approach for tackling the challenges of data-intensive computing, providing decision makers and technical experts alike with practical tools for dealing with our exploding data collections. Emphasizing data-intensive thinking and interdisciplinary collaboration, The Data Bonanza: Improving Knowledge Discovery in Science, Engineering, and Business examines the essential components of knowledge discovery, surveys many of the current research efforts worldwide, and points to new areas for innovation. Complete with a wealth of examples and DISPEL-based methods demonstrating how to gain more from data in real-world systems, the book: Outlines the concepts and rationale for implementing data-intensive computing in organizations Covers from the ground up problem-solving strategies for data analysis in a data-rich world Introduces techniques for data-intensive engineering using the Data-Intensive Systems Process Engineering Language DISPEL Features in-depth case studies in customer relations, environmental hazards, seismology, and more Showcases successful applications in areas ranging from astronomy and the humanities to transport engineering Includes sample program snippets throughout the text as well as additional materials on a companion website The Data Bonanza is a must-have guide for information strategists, data analysts, and engineers in business, research, and government, and for anyone wishing to be on the cutting edge of data mining, machine learning, databases, distributed systems, or large-scale computing.

A hands on guide to web scraping and text mining for both beginners and experienced users of R Introduces fundamental concepts of the main architecture of the web and databases and covers HTTP, HTML, XML, JSON, SQL. Provides basic techniques to query web documents and data sets (XPath and regular expressions). An extensive set of exercises are presented to guide the reader through each technique. Explores both supervised and unsupervised techniques as well as advanced techniques such as data scraping and text management. Case studies are featured throughout along with examples for each technique presented. R code and solutions to exercises featured in the book are provided on a supporting website.

Data as a Service shows how organizations can leverage “data as a service” by providing real-life case studies on the various and innovative architectures and related patterns Comprehensive approach to introducing data as a service in any organization A reusable and flexible SOA based architecture framework Roadmap to introduce ‘big data as a service’ for potential clients Presents a thorough description of each component in the DaaS reference architecture so readers can implement solutions

Fundamentals of Big Data Network Analysis for Research and Industry Hyunjoung Lee, Institute of Green Technology, Yonsei University, Republic of Korea Il Sohn, Material Science and Engineering, Yonsei University, Republic of Korea Presents the methodology of big data analysis using examples from research and industry There are large amounts of data everywhere, and the ability to pick out crucial information is increasingly important. Contrary to popular belief, not all information is useful; big data network analysis assumes that data is not only large, but also meaningful, and this book focuses on the fundamental techniques required to extract essential information from vast datasets. Featuring case studies drawn largely from the iron and steel industries, this book offers practical guidance which will enable readers to easily understand big data network analysis. Particular attention is paid to the methodology of network analysis, offering information on the method of data collection, on research design and analysis, and on the interpretation of results. A variety of programs including UCINET, NetMiner, R, NodeXL, and Gephi for network analysis are covered in detail. Fundamentals of Big Data Network Analysis for Research and Industry looks at big data from a fresh perspective, and provides a new approach to data analysis. This book: Explains the basic concepts in understanding big data and filtering meaningful data Presents big data analysis within the networking perspective Features methodology applicable to research and industry Describes in detail the social relationship between big data and its implications Provides insight into identifying patterns and relationships between seemingly unrelated big data Fundamentals of Big Data Network Analysis for Research and Industry will prove a valuable resource for analysts, research engineers, industrial engineers, marketing professionals, and any individuals dealing with accumulated large data whose interest is to analyze and identify potential relationships among data sets.

Updated new edition of Ralph Kimball's groundbreaking book on dimensional modeling for data warehousing and business intelligence! The first edition of Ralph Kimball's The Data Warehouse Toolkit introduced the industry to dimensional modeling, and now his books are considered the most authoritative guides in this space. This new third edition is a complete library of updated dimensional modeling techniques, the most comprehensive collection ever. It covers new and enhanced star schema dimensional modeling patterns, adds two new chapters on ETL techniques, includes new and expanded business matrices for 12 case studies, and more. Authored by Ralph Kimball and Margy Ross, known worldwide as educators, consultants, and influential thought leaders in data warehousing and business intelligence Begins with fundamental design recommendations and progresses through increasingly complex scenarios Presents unique modeling techniques for business applications such as inventory management, procurement, invoicing, accounting, customer relationship management, big data analytics, and more Draws real-world case studies from a variety of industries, including retail sales, financial services, telecommunications, education, health care, insurance, e-commerce, and more Design dimensional databases that are easy to understand and provide fast query response with The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, 3rd Edition.

Wring more out of the data with a scientific approach to analysis Graph Analysis and Visualization brings graph theory out of the lab and into the real world. Using sophisticated methods and tools that span analysis functions, this guide shows you how to exploit graph and network analytic techniques to enable the discovery of new business insights and opportunities. Published in full color, the book describes the process of creating powerful visualizations using a rich and engaging set of examples from sports, finance, marketing, security, social media, and more. You will find practical guidance toward pattern identification and using various data sources, including Big Data, plus clear instruction on the use of software and programming. The companion website offers data sets, full code examples in Python, and links to all the tools covered in the book. Science has already reaped the benefit of network and graph theory, which has powered breakthroughs in physics, economics, genetics, and more. This book brings those proven techniques into the world of business, finance, strategy, and design, helping extract more information from data and better communicate the results to decision-makers. Study graphical examples of networks using clear and insightful visualizations Analyze specifically-curated, easy-to-use data sets from various industries Learn the software tools and programming languages that extract insights from data Code examples using the popular Python programming language There is a tremendous body of scientific work on network and graph theory, but very little of it directly applies to analyst functions outside of the core sciences – until now. Written for those seeking empirically based, systematic analysis methods and powerful tools that apply outside the lab, Graph Analysis and Visualization is a thorough, authoritative resource.

The professional's one-stop guide to this open-source, Java-based big data framework Professional Hadoop is the complete reference and resource for experienced developers looking to employ Apache Hadoop in real-world settings. Written by an expert team of certified Hadoop developers, committers, and Summit speakers, this book details every key aspect of Hadoop technology to enable optimal processing of large data sets. Designed expressly for the professional developer, this book skips over the basics of database development to get you acquainted with the framework's processes and capabilities right away. The discussion covers each key Hadoop component individually, culminating in a sample application that brings all of the pieces together to illustrate the cooperation and interplay that make Hadoop a major big data solution. Coverage includes everything from storage and security to computing and user experience, with expert guidance on integrating other software and more. Hadoop is quickly reaching significant market usage, and more and more developers are being called upon to develop big data solutions using the Hadoop framework. This book covers the process from beginning to end, providing a crash course for professionals needing to learn and apply Hadoop quickly. Configure storage, UE, and in-memory computing Integrate Hadoop with other programs including Kafka and Storm Master the fundamentals of Apache Big Top and Ignite Build robust data security with expert tips and advice Hadoop's popularity is largely due to its accessibility. Open-source and written in Java, the framework offers almost no barrier to entry for experienced database developers already familiar with the skills and requirements real-world programming entails. Professional Hadoop gives you the practical information and framework-specific skills you need quickly.

Production-targeted Spark guidance with real-world use cases Spark: Big Data Cluster Computing in Production goes beyond general Spark overviews to provide targeted guidance toward using lightning-fast big-data clustering in production. Written by an expert team well-known in the big data community, this book walks you through the challenges in moving from proof-of-concept or demo Spark applications to live Spark in production. Real use cases provide deep insight into common problems, limitations, challenges, and opportunities, while expert tips and tricks help you get the most out of Spark performance. Coverage includes Spark SQL, Tachyon, Kerberos, ML Lib, YARN, and Mesos, with clear, actionable guidance on resource scheduling, db connectors, streaming, security, and much more. Spark has become the tool of choice for many Big Data problems, with more active contributors than any other Apache Software project. General introductory books abound, but this book is the first to provide deep insight and real-world advice on using Spark in production. Specific guidance, expert tips, and invaluable foresight make this guide an incredibly useful resource for real production settings. Review Spark hardware requirements and estimate cluster size Gain insight from real-world production use cases Tighten security, schedule resources, and fine-tune performance Overcome common problems encountered using Spark in production Spark works with other big data tools including MapReduce and Hadoop, and uses languages you already know like Java, Scala, Python, and R. Lightning speed makes Spark too good to pass up, but understanding limitations and challenges in advance goes a long way toward easing actual production implementation. Spark: Big Data Cluster Computing in Production tells you everything you need to know, with real-world production insight and expert guidance, tips, and tricks.

A practical guide to data mining using SQL and Excel Data Analysis Using SQL and Excel, 2nd Edition shows you how to leverage the two most popular tools for data query and analysis—SQL and Excel—to perform sophisticated data analysis without the need for complex and expensive data mining tools. Written by a leading expert on business data mining, this book shows you how to extract useful business information from relational databases. You'll learn the fundamental techniques before moving into the «where» and «why» of each analysis, and then learn how to design and perform these analyses using SQL and Excel. Examples include SQL and Excel code, and the appendix shows how non-standard constructs are implemented in other major databases, including Oracle and IBM DB2/UDB. The companion website includes datasets and Excel spreadsheets, and the book provides hints, warnings, and technical asides to help you every step of the way. Data Analysis Using SQL and Excel, 2nd Edition shows you how to perform a wide range of sophisticated analyses using these simple tools, sparing you the significant expense of proprietary data mining tools like SAS. Understand core analytic techniques that work with SQL and Excel Ensure your analytic approach gets you the results you need Design and perform your analysis using SQL and Excel Data Analysis Using SQL and Excel, 2nd Edition shows you how to best use the tools you already know to achieve expert results.

Best practices and invaluable advice from world-renowned data warehouse experts In this book, leading data warehouse experts from the Kimball Group share best practices for using the upcoming “Business Intelligence release” of SQL Server, referred to as SQL Server 2008 R2. In this new edition, the authors explain how SQL Server 2008 R2 provides a collection of powerful new tools that extend the power of its BI toolset to Excel and SharePoint users and they show how to use SQL Server to build a successful data warehouse that supports the business intelligence requirements that are common to most organizations. Covering the complete suite of data warehousing and BI tools that are part of SQL Server 2008 R2, as well as Microsoft Office, the authors walk you through a full project lifecycle, including design, development, deployment and maintenance. Features more than 50 percent new and revised material that covers the rich new feature set of the SQL Server 2008 R2 release, as well as the Office 2010 release Includes brand new content that focuses on PowerPivot for Excel and SharePoint, Master Data Services, and discusses updated capabilities of SQL Server Analysis, Integration, and Reporting Services Shares detailed case examples that clearly illustrate how to best apply the techniques described in the book The accompanying Web site contains all code samples as well as the sample database used throughout the case studies The Microsoft Data Warehouse Toolkit, Second Edition provides you with the knowledge of how and when to use BI tools such as Analysis Services and Integration Services to accomplish your most essential data warehousing tasks.

Learn the art and science of predictive analytics – techniques that get results Predictive analytics is what translates big data into meaningful, usable business information. Written by a leading expert in the field, this guide examines the science of the underlying algorithms as well as the principles and best practices that govern the art of predictive analytics. It clearly explains the theory behind predictive analytics, teaches the methods, principles, and techniques for conducting predictive analytics projects, and offers tips and tricks that are essential for successful predictive modeling. Hands-on examples and case studies are included. The ability to successfully apply predictive analytics enables businesses to effectively interpret big data; essential for competition today This guide teaches not only the principles of predictive analytics, but also how to apply them to achieve real, pragmatic solutions Explains methods, principles, and techniques for conducting predictive analytics projects from start to finish Illustrates each technique with hands-on examples and includes as series of in-depth case studies that apply predictive analytics to common business scenarios A companion website provides all the data sets used to generate the examples as well as a free trial version of software Applied Predictive Analytics arms data and business analysts and business managers with the tools they need to interpret and capitalize on big data.

The final edition of the incomparable data warehousing and business intelligence reference, updated and expanded The Kimball Group Reader, Remastered Collection is the essential reference for data warehouse and business intelligence design, packed with best practices, design tips, and valuable insight from industry pioneer Ralph Kimball and the Kimball Group. This Remastered Collection represents decades of expert advice and mentoring in data warehousing and business intelligence, and is the final work to be published by the Kimball Group. Organized for quick navigation and easy reference, this book contains nearly 20 years of experience on more than 300 topics, all fully up-to-date and expanded with 65 new articles. The discussion covers the complete data warehouse/business intelligence lifecycle, including project planning, requirements gathering, system architecture, dimensional modeling, ETL, and business intelligence analytics, with each group of articles prefaced by original commentaries explaining their role in the overall Kimball Group methodology. Data warehousing/business intelligence industry's current multi-billion dollar value is due in no small part to the contributions of Ralph Kimball and the Kimball Group. Their publications are the standards on which the industry is built, and nearly all data warehouse hardware and software vendors have adopted their methods in one form or another. This book is a compendium of Kimball Group expertise, and an essential reference for anyone in the field. Learn data warehousing and business intelligence from the field's pioneers Get up to date on best practices and essential design tips Gain valuable knowledge on every stage of the project lifecycle Dig into the Kimball Group methodology with hands-on guidance Ralph Kimball and the Kimball Group have continued to refine their methods and techniques based on thousands of hours of consulting and training. This Remastered Collection of The Kimball Group Reader represents their final body of knowledge, and is nothing less than a vital reference for anyone involved in the field.

Construct a robust end-to-end solution for analyzing and visualizing streaming data Real-time analytics is the hottest topic in data analytics today. In Real-Time Analytics: Techniques to Analyze and Visualize Streaming Data, expert Byron Ellis teaches data analysts technologies to build an effective real-time analytics platform. This platform can then be used to make sense of the constantly changing data that is beginning to outpace traditional batch-based analysis platforms. The author is among a very few leading experts in the field. He has a prestigious background in research, development, analytics, real-time visualization, and Big Data streaming and is uniquely qualified to help you explore this revolutionary field. Moving from a description of the overall analytic architecture of real-time analytics to using specific tools to obtain targeted results, Real-Time Analytics leverages open source and modern commercial tools to construct robust, efficient systems that can provide real-time analysis in a cost-effective manner. The book includes: A deep discussion of streaming data systems and architectures Instructions for analyzing, storing, and delivering streaming data Tips on aggregating data and working with sets Information on data warehousing options and techniques Real-Time Analytics includes in-depth case studies for website analytics, Big Data, visualizing streaming and mobile data, and mining and visualizing operational data flows. The book's «recipe» layout lets readers quickly learn and implement different techniques. All of the code examples presented in the book, along with their related data sets, are available on the companion website.

How to effectively use BigQuery, avoid common mistakes, and execute sophisticated queries against large datasets Google BigQuery Analytics is the perfect guide for business and data analysts who want the latest tips on running complex queries and writing code to communicate with the BigQuery API. The book uses real-world examples to demonstrate current best practices and techniques, and also explains and demonstrates streaming ingestion, transformation via Hadoop in Google Compute engine, AppEngine datastore integration, and using GViz with Tableau to generate charts of query results. In addition to the mechanics of BigQuery, the book also covers the architecture of the underlying Dremel query engine, providing a thorough understanding that leads to better query results. Features a companion website that includes all code and data sets from the book Uses real-world examples to explain everything analysts need to know to effectively use BigQuery Includes web application examples coded in Python

Organizations can make data science a repeatable, predictable tool, which business professionals use to get more value from their data Enterprise data and AI projects are often scattershot, underbaked, siloed, and not adaptable to predictable business changes. As a result, the vast majority fail. These expensive quagmires can be avoided, and this book explains precisely how.  Data science is emerging as a hands-on tool for not just data scientists, but business professionals as well. Managers, directors, IT leaders, and analysts must expand their use of data science capabilities for the organization to stay competitive. Smarter Data Science helps them achieve their enterprise-grade data projects and AI goals. It serves as a guide to building a robust and comprehensive information architecture program that enables sustainable and scalable AI deployments. When an organization manages its data effectively, its data science program becomes a fully scalable function that’s both prescriptive and repeatable. With an understanding of data science principles, practitioners are also empowered to lead their organizations in establishing and deploying viable AI. They employ the tools of machine learning, deep learning, and AI to extract greater value from data for the benefit of the enterprise. By following a ladder framework that promotes prescriptive capabilities, organizations can make data science accessible to a range of team members, democratizing data science throughout the organization. Companies that collect, organize, and analyze data can move forward to additional data science achievements: Improving time-to-value with infused AI models for common use cases Optimizing knowledge work and business processes Utilizing AI-based business intelligence and data visualization Establishing a data topology to support general or highly specialized needs Successfully completing AI projects in a predictable manner Coordinating the use of AI from any compute node. From inner edges to outer edges: cloud, fog, and mist computing When they climb the ladder presented in this book, businesspeople and data scientists alike will be able to improve and foster repeatable capabilities. They will have the knowledge to maximize their AI and data assets for the benefit of their organizations.

A dream come true for those looking to improve their data fluency Analytical data is a powerful tool for growing companies, but what good is it if it hides in the shadows? Bring your data to the forefront with effective visualization and communication approaches, and let Data Fluency: Empowering Your Organization with Effective Communication show you the best tools and strategies for getting the job done right. Learn the best practices of data presentation and the ways that reporting and dashboards can help organizations effectively gauge performance, identify areas for improvement, and communicate results. Topics covered in the book include data reporting and communication, audience and user needs, data presentation tools, layout and styling, and common design failures. Those responsible for analytics, reporting, or BI implementation will find a refreshing take on data and visualization in this resource, as will report, data visualization, and dashboard designers. Conquer the challenge of making valuable data approachable and easy to understand Develop unique skills required to shape data to the needs of different audiences Full color book links to bonus content at juiceanalytics.com Written by well-known and highly esteemed authors in the data presentation community Data Fluency: Empowering Your Organization with Effective Communication focuses on user experience, making reports approachable, and presenting data in a compelling, inspiring way. The book helps to dissolve the disconnect between your data and those who might use it and can help make an impact on the people who are most affected by data. Use Data Fluency today to develop the skills necessary to turn data into effective displays for decision-making.

Your logical, linear guide to the fundamentals of data science programming Data science is exploding—in a good way—with a forecast of 1. 7 megabytes of new information created every second for each human being on the planet by 2020 and 11. 5 million job openings by 2026. It clearly pays dividends to be in the know. This friendly guide charts a path through the fundamentals of data science and then delves into the actual work: linear regression, logical regression, machine learning, neural networks, recommender engines, and cross-validation of models. Data Science Programming All-In-One For Dummies is a compilation of the key data science, machine learning, and deep learning programming languages: Python and R. It helps you decide which programming languages are best for specific data science needs. It also gives you the guidelines to build your own projects to solve problems in real time. Get grounded: the ideal start for new data professionals What lies ahead: learn about specific areas that data is transforming Be meaningful: find out how to tell your data story See clearly: pick up the art of visualization Whether you’re a beginning student or already mid-career, get your copy now and add even more meaning to your life—and everyone else’s!

Organize, plan, and build an exceptional data analytics team within your organization In Minding the Machines: Building and Leading Data Science and Analytics Teams , AI and analytics strategy expert Jeremy Adamson delivers an accessible and insightful roadmap to structuring and leading a successful analytics team. The book explores the tasks, strategies, methods, and frameworks necessary for an organization beginning their first foray into the analytics space or one that is rebooting its team for the umpteenth time in search of success. In this book, you’ll discover: A focus on the three pillars of strategy, process, and people and their role in the iterative and ongoing effort of building an analytics team Repeated emphasis on three guiding principles followed by successful analytics teams: start early, go slow, and fully commit The importance of creating clear goals and objectives when creating a new analytics unit in an organization Perfect for executives, managers, team leads, and other business leaders tasked with structuring and leading a successful analytics team, Minding the Machines is also an indispensable resource for data scientists and analysts who seek to better understand how their individual efforts fit into their team’s overall results.

Take a dive into data lakes  “Data lakes” is the latest buzz word in the world of data storage, management, and analysis.  Data Lakes For Dummies  decodes and demystifies the concept and helps you get a straightforward answer the question: “What exactly is a data lake and do I need one for my business?” Written for an audience of technology decision makers tasked with keeping up with the latest and greatest data options, this book provides the perfect introductory survey of these novel and growing features of the information landscape. It explains how they can help your business, what they can (and can’t) achieve, and what you need to do to create the lake that best suits your particular needs.  With a minimum of jargon, prolific tech author and business intelligence consultant Alan Simon explains how data lakes differ from other data storage paradigms. Once you’ve got the background picture, he maps out ways you can add a data lake to your business systems; migrate existing information and switch on the fresh data supply; clean up the product; and open channels to the best intelligence software for to interpreting what you’ve stored.  Understand and build data lake architecture Store, clean, and synchronize new and existing data Compare the best data lake vendors Structure raw data and produce usable analytics Whatever your business, data lakes are going to form ever more prominent parts of the information universe every business should have access to. Dive into this book to start exploring the deep competitive advantage they make possible—and make sure your business isn’t left standing on the shore.

Make smart business decisions with your data by design!  Take a deep dive to understand how developing your data science dogma can drive your business—ya dig? Every phone, tablet, computer, watch, and camera generates data—we’re overwhelmed with the stuff. That’s why it’s become increasingly important that you know how to derive useful insights from the data you have to understand which piece of data in the sea of data is important and which isn’t (trust us: not as scary as it sounds!), and to rely on said data to make critical business decisions. Enter the world of data science: the practice of using scientific methods, processes, and algorithms to gain knowledge and insights from any type of data.  Data Science For Dummies  provides a comprehensive introduction in that friendly and approachable way you’ve come to know from Dummies. Your new go-to guide breaks down this vast topic into three smaller parts—big data, data science, and data engineering—and then shows you how to combine those areas to produce value and make informed decisions to drive business growth. It’s also filled with real-world examples and applications that you can apply to your situation.  Data Science For Dummies  demonstrates:  How natural language processing works Strategies around data science How to make decisions using probabilities Ways to display your data using a visualization model How to incorporate various programming languages into your strategy Whether you’re a professional or a student,  Data Science For Dummies  will get you caught up on all the latest data trends. Find out how to ask the pressing questions you need your data to answer by picking up your copy today.

Learn to use, and not be used by, data to make more insightful decisions  The availability of data and various forms of AI unlock countless possibilities for business decision makers. But what do you do when you feel pressured to cede your position in the decision-making process altogether?  Decision Intelligence For Dummies  pumps the brakes on the growing trend to take human beings out of the decision loop and walks you through the best way to make data-informed but human-driven decisions. The book shows you how to achieve maximum flexibility by using every available resource, and not just raw data, to make the most insightful decisions possible.  In this timely book, you’ll learn to:  Make data a means to an end, rather than an end in itself, by expanding your decision-making inquiries Find a new path to solid decisions that includes, but isn’t dominated, by quantitative data Measure the results of your new framework to prove its effectiveness and efficiency and expand it to a whole team or company Perfect for business leaders in technology and finance,  Decision Intelligence For Dummies  is ideal for anyone who recognizes that data is not the only powerful tool in your decision-making toolbox. This book shows you how to be guided, and not ruled, by the data.

Unravel the mysteries of blockchains Blockchain technologies are disrupting some of the world’s biggest industries. Blockchain For Dummies provides a fast way to catch up with the essentials of this quickly evolving tech. Written by an author involved in founding and analyzing blockchain solutions, this book serves to help those who need to understand what a blockchain can do (and can't do). This revised edition walks you through how a blockchainsecurely records data across independent networks. It offers a tour of some of the world’s best-known blockchains, including those that power Bitcoin and other cryptocurrencies. It also provides a glance at how blockchain solutions are affecting the worlds of finance, supply chain management, insurance, and governments. Get a clear picture of what a blockchain can do Learn how blockchains rule cryptocurrency and smart contracts Discover current blockchains and how each of them work Test blockchain apps Blockchain has become the critical buzzword in the world of financial technology and transaction security – and now you can make sense of it with the help of this essential guide.

Get ahead of the curve—learn about big data on the blockchain Blockchain came to prominence as the disruptive technology that made cryptocurrencies work. Now, data pros are using blockchain technology for faster real-time analysis, better data security, and more accurate predictions. Blockchain Data Analytics For Dummies is your quick-start guide to harnessing the potential of blockchain. Inside this book, technologists, executives, and data managers will find information and inspiration to adopt blockchain as a big data tool. Blockchain expert Michael G. Solomon shares his insight on what the blockchain is and how this new tech is poised to disrupt data. Set your organization on the cutting edge of analytics, before your competitors get there! Learn how blockchain technologies work and how they can integrate with big data Discover the power and potential of blockchain analytics Establish data models and quickly mine for insights and results Create data visualizations from blockchain analysis Discover how blockchains are disrupting the data world with this exciting title in the trusted For Dummies line!

В курсе изложены фундаментальные основы информатики в области информационных техноло-гий, как составляющие формирования информационного общества. Рассмотрены базовые ин-формационные процессы и их модели. Раскрыты содержание, возможности и области приме-нения базовых и прикладных информационных технологий. Предложена информационная тех-нология разработки систем. Рассмотрены области машинного обучения и нейросетей. Приведе-на инструментальная база с раскрытием программных, технических и методических средств ин-формационных технологий.

В курсе рассматриваются основные подходы и инструменты, позволяющие исследовать бизнес-процессы организации. Курс поможет студентам освоить методологические и методические аспекты моделирования бизнес-процессов, изучить инструменты и понять основные подходы для создания моделей производственных и непроизводственных процессов. Приведены примеры применения популярных методологий на практике для анализа и оптимизации бизнес-процессов. Курс можно рекомендовать желающим эффективно заниматься управлением. Научно обоснованный подход к организации бизнес-процесса на том или ином предприятии позволит руководителям иметь четкое представление о работе предприятия, выявить возникшие проблемы и спрогнозировать будущее развитие или расширение производства. Соответствует актуальным требованиям федерального государственного образовательного стандарта высшего образования. Для студентов, бакалавров, магистрантов, аспирантов и специалистов организаций, изучающих современные проблемы моделирования бизнес-процессов; может быть полезен при исследовании, анализе и оптимизации деятельности компании.

В пособии рассмотрены основные подходы и направления развития систем баз данных. Анализируются классические машинно ориентированные формы представления информации и данных. Рассматриваются типовые модели физической и логической организации данных, архитектура средств доступа к данным. Достаточно подробно представлены возможности SQL как базового языка для работы с реляционными базами данных. Большое внимание уделено проблемам моделирования и проектирования баз данных. Учебное пособие соответствует требованиям Федерального государственного образовательного стандарта высшего образования последнего поколения. Предназначено для студентов вузов, обучающихся по направлению 09.03.03 «Прикладная информатика», и учащихся техникумов и колледжей.

В пособии рассмотрены основные подходы и направления развития систем баз данных. Анализируются классические машинно ориентированные формы представления информации и данных. Рассматриваются типовые модели физической и логической организации данных, архитектура средств доступа к данным. Достаточно подробно представлены возможности SQL как базового языка для работы с реляционными базами данных. Большое внимание уделено проблемам моделирования и проектирования баз данных. Для студентов учебных заведений, реализующих программу среднего профессионального образования по укрупненной группе специальностей 09.02.00 «Информатика и вычислительная техника», а также студентов вузов.

Учебное пособие предназначено для изучения практического применения СУБД SQL и NoSQL-типа при проектировании информационных систем. Проведены аналогии между базами данных SQL и NoSQL-типа. Полученные теоретические знания закрепляются при выполнении цикла лабораторных работ. Работа с SQL СУБД изучается на примере СУБД MariaDB, рассмотрен вопрос использования хорошо зарекомендовавших себя в работе с MySQL приложений (например, phpMyAdmin, MySQL Workbench). Работа с NoSQL СУБД изучается на примере СУБД MongoDB с использованием оболочки Robomongo. Показана возможность использования универсального менеджера баз данных DBeaver для одновременной работы с СУБД MariaDB и MongoDB. Приведены примеры информационных систем на базе обоих типов СУБД SQL и NoSQL. Предназначено для студентов, бакалавров, магистрантов, аспирантов и специалистов в области проектирования информационных систем и баз данных. Пособие также полезно для лиц, занимающихся самообразованием.

Учебное пособие предназначено для изучения практического применения СУБД SQL- и NoSQL-типа при проектировании информационных систем. Проведены аналогии между базами данных SQL- и NoSQL-типа. Полученные теоретические знания закрепляются при выполнении цикла лабораторных работ. Работа с SQL СУБД изучается на примере СУБД MariaDB. Рассмотрен вопрос использования хорошо зарекомендовавших себя в работе с MySQL приложений (например, phpMyAdmin, MySQL Workbench). Работа с NoSQL СУБД изучается на примере СУБД MongoDB с использованием оболочки Robomongo. Показана возможность использования универсального менеджера баз данных DBeaver для одновременной работы с СУБД MariaDB и MongoDB. Приведены примеры информационных систем на базе обоих типов СУБД – SQL и NoSQL. Предназначено для студентов учреждений среднего профессионального образования, обучающихся по укрупненной группе специальностей 09.02.00 «Информатика и вычислительная техника», а также бакалавров, магистрантов, аспирантов и специалистов в области проектирования информационных систем и баз данных, а также для всех, кто интересуется проектированием информационных систем.

В предлагаемом учебнике рассмотрена проблема создания приложения локальной базы данных. Начиная с изучения теоретических основ построения базы данных, которые органично переходят в этапы проектирования, студент подходит к изучению пракгических приемов реализации базы данных в среде DELPHI 7. Учебный материал изложен в наиболее удобной и логичной для изучения последовательности. Плавный переход от одной главы к другой главе позволяет проектировать базу данных с постепенным ее усложнением за счет добавления новых функций и возможностей. Каждая глава снабжена большим количеством примеров и контрольными вопросами для самоподготовки. Учебник предназначен для студентов высших учебных заведений обучающихся по направлению 09.03.01 «Информатика и вычислительная техника», а также может быть рекомендован для студентов средних специальных учебных заведений соответствующих специальностей.

Теоретические основы построения структуры базы данных подробно изложены в книге первой, в данной книге изложена методика автоматизированного построения базы данных с использованием утилиты ERwin. Во второй книге учебника «Базы данных» рассмотрена проблема создания удаленных баз данных, построенных как в двухзвенной, так и в трехзвенной архитектуре «клиент – сервер». Начиная с изучения теории удаленного доступа к данным, подробно рассматривается построение серверной части и клиентской части приложения. За основу выбрана система управления базами данных Interbase, и все примеры выполнены в этой СУБД. В последних главах рассмотрена проблема работы с базой данных многих пользователей, которым назначаются различные привилегии и права доступа к данным. Учебный материал изложен в наиболее удобной и логичной для изучения последовательности. Плавный переход от одной главы к другой позволяет проектировать базу данных с постепенным ее усложнением за счет добавления новых функций и возможностей. Каждая глава снабжена большим количеством примеров и контрольными вопросами для самоподготовки. Учебник предназначен для студентов высших учебных заведений, обучающихся по группе специальностей 09.00.00 «Информатика и вычислительная техника», а также может быть рекомендован для студентов средних специальных учебных заведений соответствующих специальностей.

В учебном пособии рассмотрены базовые вопросы теории проектирования баз данных, использование системы управления базами данных (СУБД) Access для создания баз данных, особенности разработки пользовательских приложений на основе СУБД Microsoft Access, а также архитектура системы баз данных. Соответствует требованиям федеральных государственных образовательных стандартов среднего профессионального образования последнего поколения. Для студентов средних специальных учебных заведений, обучающихся по направлению «Информационные системы».

Курс посвящён основным понятиям, концепциям и технологиям в области компьютерных сетей и систем связи. Представлены история развития коммуникаций и обзор семиуровневой модели взаимодействия открытых систем OSI, даны основные сведения об интерфейсах и протоколах, описаны способы адресации, коммутации и маршрутизации в компьютерных сетях. Также даны основные концепции построения систем передачи информации и систем связи. Особое внимание уделено материалам, связанным с VoIP, компьютерной телефонией, а также системами компьютерно телефонной интеграции CTI, поскольку именно в этих областях наиболее явно проявляются современные тенденции конвергенции компьютерных сетей и систем связи.

Курс посвящён основным понятиям, концепциям и технологиям в области компьютерных сетей и систем связи. Представлены история развития коммуникаций и обзор семиуровневой модели взаимодействия открытых систем OSI, даны основные сведения об интерфейсах и протоколах, описаны способы адресации, коммутации и маршрутизации в компьютерных сетях. Также даны основные концепции построения систем передачи информации и систем связи. Особое внимание уделено материалам, связанным с VoIP, компьютерной телефонией, а также системами компьютерно телефонной интеграции CTI, поскольку именно в этих областях наиболее явно проявляются современные тенденции конвергенции компьютерных сетей и систем связи.

Учебное пособие посвящено вопросам реализации реляционных баз данных. Его целью является освоение базовых принципов проектирования, реализации, сопровождения и обеспечения информационной безопасности баз данных. В состав учебного пособия включены задания для лабораторных работ, контрольные вопросы и тесты для самопроверки. Соответствует требованиям федеральных государственных образовательных стандартов среднего профессионального образования последнего поколения. Для студентов учреждений среднего профессионального и высшего образования всех специальностей и направлений подготовки, интересующихся вопросами проектирования, реализации и безопасности баз данных.

В учебнике рассмотрены и проанализированы: базы данных и СУБД, данные и ЭВМ, концепция баз данных, архитектура СУБД, инфологическая, даталогическая и физическая модели данных, типы даталогических моделей данных, представление данных с помощью модели «сущность – связь», диаграмма «сущность – связь», целостность данных. Дан обзор нотаций, используемых при построении диаграмм «сущность – связь». Подробно рассмотрены: реляционные базы данных, операции с таблицами реляционных баз данных, правила порождения реляционных отношений из модели «сущность – связь. Подробно представлены разделы: средства ускоренного доступа к данным, язык SQL, физическая организация СУБД, „клиент-серверная“ архитектура, обработка распределенных данных и структура сервера базы данных. В учебнике также сформулированы концептуальные основы понятия „знание“, изложены понятия и определения знаний, баз знаний, модели представления знаний. Здесь же даны: принципы построения систем, ориентированных на анализ данных – хранилища данных, модели данных, используемые при построении хранилищ данных. Заканчивается учебник рассмотрением вопросов по защите данных. Для студентов учреждений среднего профессионального образования, обучающихся по укрупненной группе специальностей 09.02.00 «Информатика и вычислительная техника».

Учебное пособие написано в соответствии со стандартами группы специальностей «Информатика и вычислительная техника», дисциплины «Базы данных». Рассмотрены вопросы, связанные с принципами построения, проектирования и эксплуатации баз данных. Соответствует требованиям федеральных государственных образовательных стандартов среднего профессионального образования последнего поколения. Для учащихся учреждений среднего профессионального образования, изучающих дисциплину «Базы данных». Может быть полезно при изучении аналогичной дисциплины в рамках учебной программы высших учебных заведений.

Учебное пособие написано в соответствии со стандартами группы специальностей «Информатика и вычислительная техника», дисциплины «Базы данных». Рассмотрены вопросы, связанные с принципами построения, проектирования и эксплуатации баз данных. Соответствует требованиям федеральных государственных образовательных стандартов высшего образования последнего поколения. Для учащихся учреждений высшего образования, изучающих дисциплину «Базы данных».

Издание направлено на формирование навыков компетентной работы с научно-технической информацией. Учебник состоит из введения и пяти глав. Во введении излагаются общие сведения о функционировании научной информации. В первой главе описаны наиболее авторитетные издательства научной литературы, рассмотрены поисковые возможности издательских платформ, обсуждены преимущества и недостатки модели открытого доступа, затронута проблема недобросовестных издателей. Во второй главе говорится о базах данных научных публикаций и журналов, описаны принципы работы с их инструментарием. Третья глава посвящена сведениям об идентификации объектов библиографического описания. Четвертая глава включает сведения о системах классификации научной информации. В пятой главе описаны библиометрические показатели и принципы их компетентного использования. Каждая глава завершается вопросами и заданиями для самоконтроля, практическими заданиями и списком рекомендуемой литературы. В качестве примеров в учебнике преимущественно использованы тематические ресурсы по наукам о Земле, однако изложенные сведения могут быть полезны и специалистам других дисциплин. Для обучающихся вузов, магистрантов, аспирантов и младших научных сотрудников.

Наш телефон знает о нас больше, чем мы думаем. Он умеет собирать и анализировать информацию о том, как мы передвигаемся по городу, какие посты лайкаем и какими приложениями пользуемся. Он сообщит о пробках и поторопит на работу, чтобы мы не опоздали; подберет музыку под наше настроение и составит список персональных рекомендаций, чем можно занять себя в течение дня. Телефон – больше не устройство, по которому звонят, это уже средство управления окружающим нас миром. Незаметно мы окружили себя такими интерфейсами, которые создают невидимый барьер между человеком и окружающей средой. Планирование, управление, коммуникация, все теперь строится через эти программы и девайсы. Даже человеческие отношения. Но насколько глубока кроличья нора? Каждому предстоит разобраться в этом самому. Эта книга поможет донести основные принципы проектирования и создания таких интерфейсов управления бизнесом, обществом и окружающим нас миром посредством Больших данных. Читайте, наслаждайтесь и помните: сожжение книг противозаконно.