Gain insight into how to govern and consume IBM's unique in-motion and at-rest Big Data analytic capabilitiesA. R. Ammons once said, "A word too much repeated falls out of being", and although the term Big Data sometimes seems to be "too much repeated", it's not about to fall "out of being". That said, it is subject to a lot of hype. The term Big Data is a bit of a misnomer. Truth be told, we're not even big fans of the term--despite the fact that it is so prominently displayed on the cover of this book--because it implies that other data is somehow small (it might be) or that this particular type of data is large in size (it can be, but doesn't have to be). This is Big Data in a nutshell: It is the ability to retain, process, and understand data like never before. It can mean more data than what you are using today; but it can also mean different kinds of data, a venture into the unstructured world where most of today's data resides. The Big Data opportunity. It's a shift, rift, lift, or cliff for your business--this book is going to help you experience the shift and lift, while those that don't work to get beyond the hype end up in a rift or cliff. In this book you will learn how cognitive computing systems, like IBM Watson, fit into the Big Data world. You'll learn how Big Data needs a "ground-to-cloud" architecture, what a Data Refinery looks like, and theimportance of a next generation data platform. Gain an understanding of the concepts of data-in-motion, data-at-rest (technologies like Hadoop play here, as well as others), the role that NoSQL and polyglot play in a leading edge analytics architecture, and more. Get details about the Big Data platform manifesto and why it is a must for any Big Data project. Capturing, storing, refining, transforming, governing, securing, and analyzing data, traditionally or as a service, are important topics alsocovered in this book.

Use the functionalities of Kibana to discover data and build attractive visualizations and dashboards for real-world scenarios About This Book * Perform real-time data analytics and visualizations, on streaming data, using Kibana * Build beautiful visualizations and dashboards with simplicity and ease without any type of coding involved * Learn all the core concepts as well as detailed information about each component used in Kibana Who This Book Is For Whether you are new to the world of data analytics and data visualization or an expert, this book will provide you with the skills required to use Kibana with ease and simplicity for real-time data visualization of streaming data. This book is intended for those professionals who are interested in learning about Kibana,its installations, and how to use it . As Kibana provides a user-friendly web page, no prior experience is required. What You Will Learn * Understand the basic concepts of elasticsearch used in Kibana along with step by step guide to install Kibana in Windows and Ubuntu * Explore the functionality of all the components used in Kibana in detail, such as the Discover, Visualize, Dashboard,and Settings pages * Analyze data using the powerful search capabilities of elasticsearch * Understand the different types of aggregations used in Kibana for visualization * Create and build different types of amazing visualizations and dashboards easily * Create, save, share, embed, and customize the visualizations added to the dashboard * Customize and tweak the advanced settings of Kibana to ensure ease of use In Detail With the increasing interest in data analytics and visualization of large data around the globe, Kibana offers the best features to analyze data and create attractive visualizations and dashboards through simple-to-use web pages. The variety of visualizations provided, combined with the powerful underlying elasticsearch capabilities will help professionals improve their skills with this technology. This book will help you quickly familiarize yourself to Kibana and will also help you to understand the core concepts of this technology to build visualizations easily. Starting with setting up of Kibana and elasticsearch in Windows and Ubuntu, you will then use the Discover page to analyse your data intelligently. Next, you will learn to use the Visualization page to create beautiful visualizations without the need for any coding. Then, you will learn how to use the Dashboard page to create a dashboard and instantly share and embed the dashboards. You will see how to tweak the basic and advanced settings provided in Kibana to manage searches, visualizations, and dashboards. Finally, you will use Kibana to build visualizations and dashboards for real-world scenarios. You will quickly master the functionalities and components used in Kibana to create amazing visualizations based on real-world scenarios. With ample screenshots to guide you through every step, this book will assist you in creating beautiful visualizations with ease. Style and approach This book is a comprehensive step-by-step guide to help you understand Kibana. It's explained in an easy-to-follow style along with supporting images. Every chapter is explained sequentially , covering the basics of each component of Kibana and providing detailed explanations of all the functionalities of Kibana that appeal.

Uwe K. Schneider untersucht den rechtlichen Rahmen einrichtungsubergreifender elektronischer Patientenakten, uber die alle bereits erhobenen Gesundheitsdaten jederzeit am Ort der Behandlung verfugbar gemacht werden koennen. Er veranschaulicht diese Rahmenbedingungen anhand von Regelungsinstrumenten wie der Gewahrung finanzieller Anreize fur die Nutzung solcher Akten oder gar der Verpflichtung hierzu. Dabei bewegt der Autor sich im Spannungsfeld von Daten- und Gesundheitsschutz. Denn diese Akten bieten einerseits die Chance, Qualitat und Wirtschaftlichkeit der Versorgung zu verbessern. Auf der anderen Seite existieren Risiken, wenn hierfur Patientendaten auf externen Servern vorgehalten und uber elektronische Netze ausgetauscht werden. Es droht eine Verfluchtigung von Daten, Verantwortung und Vertrauen im Netz.

Pentaho Data Integration Cookbook Second Edition is written in a cookbook format, presenting examples in the style of recipes. This allows you to go directly to your topic of interest, or follow topics throughout a chapter to gain a thorough in-depth knowledge. Pentaho Data Integration Cookbook Second Edition is designed for developers who are familiar with the basics of Kettle but who wish to move up to the next level. It is also aimed at advanced users that want to learn how to use the new features of PDI as well as and best practices for working with Kettle.

Each chapter of the book quickly introduces a key 'theme' of Data Analysis, before immersing you in the practical aspects of each theme. You'll learn quickly how to perform all aspects of Data Analysis.Practical Data Analysis is a book ideal for home and small business users who want to slice & dice the data they have on hand with minimum hassle.

In 2010, as focal point for information technology management across the government, OMB's Federal Chief Information Officer launched the Federal Data Center Consolidation Initiative to consolidate the growing number of centres. The objectives of this book are to evaluate the extent to which agencies have achieved cost savings to date and identified future savings through their consolidation efforts; identify agencies' notable consolidation successes and challenges in achieving cost savings; and evaluate the extent to which data center optimisation metrics have been established.

An in-depth Oracle Press guide to Oracle Exalytics In-Memory Machine

Written by Oracle ACE Director Mark Rittman, this e-book offers comprehensive coverage of Oracle Exalytics In-Memory Machine, the best-in-class, preintegrated BI platform. After an overview of the product and its capabilities, you'll get complete details on installation, configuration, administration, maintenance, and creation of the in-memory adaptive data mart that powers the "speed‑ of‑thought" dashboards. Expert tips for integrating with both Oracle and non-Oracle enterprise data sources are included. Enable your organization to analyze large amounts of data via rich, visual dashboards using Oracle Exalytics In-Memory Machine and this Oracle Press guide.

Electronic health records (EHRs) play an important role in optimizing the health care provided to active duty servicemembers and veterans. When a servicemember leaves military service by way of discharge, separation, or retirement he or she may become eligible for VA benefits and services including VA health care. Transitioning their health care information from one large health care system (Department of Defense; DOD) to the other (Department of Veterans Affairs; VA) involves coordination of data and information between DOD and VA. Longstanding concern that this exchange be effective has been expressed in many quarters, including Congress. The purpose of this book is to provide a background on the long-standing efforts in sharing health information between DOD and VA. The book also describes changes to the integrated electronic health record system and evaluates the departments' current plans; and determines whether the departments are effectively collaborating on management of the program.

This book consists of a practical, exampleoriented approach that aims to help you learn how to use Clojure for data analysis quickly and efficiently. This book is great for those who have experience with Clojure and need to use it to perform data analysis. This book will also be hugely beneficial for readers with basic experience in data analysis and statistics.

Das Lehrbuch stellt eine grundlegende Einfuhrung in die mathematische Theorie der geordneten Mengen und Verbande dar. Neben wichtigen Begriffen werden allgemeine Vorgehensweisen und Beweistechniken demonstriert, die fur dieses Gebiet typisch sind. Auch werden eine Reihe von Anwendungen diskutiert, insbesondere aus der Informatik, wie logische Schaltungen, Semantik von Programmiersprachen und die Untersuchung von Kausalitat in verteilten Systemen."

Standard tutorial-based approach."Getting Started with Greenplum for Big Data" Analytics is great for data scientists and data analysts with a basic knowledge of Data Warehousing and Business Intelligence platforms who are new to Big Data and who are looking to get a good grounding in how to use the Greenplum Platform. It's assumed that you will have some experience with database design and programming as well as be familiar with analytics tools like R and Weka.

Data Mining and Business Intelligence: A Guide to Productivity provides an overview of data mining technology and how it is applied in a business environment. It describes the corresponding data mining methodologies that are used to solve a variety of business problems which enhance firm-level efficiency in a less technical, more managerial style. The book incorporates the data mining process into the spectrum of complementary technologies that together comprise corporate information systems that promote business intelligence. Business intelligence involves the proliferation of value-added information throughout a given enterprise through the use of various applications that promotes efficiency for the firm.

In diesem Lehrbuch werden die mathematischen Grundlagen exakt und dennoch anschaulich und gut nachvollziehbar vermittelt. Sie werden durchgehend anhand zahlreicher Musterbeispiele illustriert, durch Anwendungen in der Informatik motiviert und durch historische Hintergrunde oder Ausblicke in angrenzende Themengebiete aufgelockert. Am Ende jedes Kapitels befinden sich Kontrollfragen, die das Verstandnis testen und typische Fehler bzw. Missverstandnisse ausraumen. Zusatzlich helfen zahlreiche Aufwarmubungen (mit vollstandigem Losungsweg) und weiterfuhrende Ubungsaufgaben das Erlernte zu festigen und praxisrelevant umzusetzen. Dieses Lehrbuch ist daher auch sehr gut zum Selbststudium geeignet. Erganzend wird in eigenen Abschnitten das Computeralgebrasystem Mathematica vorgestellt und eingesetzt, wodurch der Lehrstoff visualisiert und somit das Verstandnis erleichtert werden kann.

This book provides the business or IT professional with a practical working knowledge of data modelling concepts and best practices, along with how to apply these principles with ER/Studio DA. You will build many ER/Studio DA data models along the way, applying best practices to master these ten objectives: You will know why a data model is needed and which ER/Studio DA models are the most appropriate for each situation; You will be able to read a data model of any size and complexity with the same confidence as reading a book; You will know how to apply all the key features of ER/Studio DA; You will be able to build relational and dimensional conceptual, logical, and physical data models in ER/Studio DA; You will be able to apply techniques such as indexing, transforms, and forward engineering to turn a logical data model into an efficient physical design; You will improve data model quality and impact analysis results by leveraging ER/Studio DAs lineage functionality and compare/merge utility; You will achieve enterprise architecture through ER/Studio DAs repository and portal functionality; You will be able to apply ER/Studio DAs data dictionary features; You will learn ways of sharing the data model through reporting and through exporting the model in a variety of formats; You will leverage ER/Studio DAs naming functionality to improve naming consistency. This book contains four sections: Section I introduces data modelling and the ER/Studio DA landscape. Learn why data modelling is so critical to software development and even more importantly, why data modelling is so critical to understanding the business. You will also learn about the ER/Studio DA environment. By the end of this section, you will have created and saved your first data model in ER/Studio DA and be ready to start modelling in Section II. Section II explains all of the symbols and text on a data model, including entities, attributes, relationships, domains, and keys. By the time you finish this section, you will be able to read a data model of any size or complexity, and create a complete data model in ER/Studio DA. Section III explores the three different levels of models: conceptual, logical, and physical. A conceptual data model (CDM) represents a business need within a defined scope. The logical data model (LDM) represents a detailed business solution, capturing the business requirements without complicating the model with implementation concerns such as software and hardware. The physical data model (PDM) represents a detailed technical solution. The PDM is the logical data model compromised often to improve performance or usability. The PDM makes up for deficiencies in our technology. By the end of this section you will be able to create conceptual, logical, and physical data models in ER/Studio DA. Section IV discusses additional features of ER/Studio DA. These features include data dictionary, data lineage, automating tasks, repository and portal, exporting and reporting, naming standards, and compare and merge functionality.

Neueste Errungenschaften in der Hard-und Software-Entwicklung, wie z. B. Multi-Core-CPUs und DRAM-Kapazitaten von mehreren Terabyte pro Server, fordertendie Einfuhrung einer revolutionaren Technologie: das In-Memory Data Management. Diese Technologie unterstutzt die flexible und extrem schnelle Analyse grosser Mengen von Unternehmensdaten. Professor Hasso Plattner und seine Arbeitsgruppe am Hasso-Plattner-Institut in Potsdam, Deutschland, lehren die entsprechenden Konzepte seit Jahren und sorgen fur ihreSichtbarkeit in der Software-Industrie.

Dieses Buch basiert auf dem ersten Online-Kurs der openHPI E-Learning-Plattform, die im Herbst 2012 mit mehr als 13.000 Lernenden ins Leben gerufen wurde. Das Buch richtet sich an Studierende der Informatik, speziell mit dem Schwerpunkt Software Engineering. sowiean Business-Experten, Entscheider, Software-Entwickler, IT-Experten und IT-Analysten. Themen sind - unter anderem - die physische Datenspeicherung und der Zugang, grundlegende Datenbank-Betreiber, Kompressions-Mechanismen und Algorithmen. Daruber hinaus werden Implikationen fur zukunftige Enterprise-Anwendungen und deren Entwicklung diskutiert. Die Leser lernen, die radikalen Unterschiede und Vorteile der neuen Technologie gegenuber herkommlichen zeilenorientierten und disk-basierten Datenbanken zu verstehen."

Arithmetik ist eine UEbersetzung des vierten Kapitels der legendaren Werkreihe The Art of Computer Programming von Donald E. Knuth in der neuesten Fassung. Donald E. Knuth bietet hier eine umfangreiche Einfuhrung in die Computeralgebra, die den aktuellen Stand der Forschung berucksichtigt. Er versteht es, die Algorithmen didaktisch sehr geschickt und ohne Kompromisse bei der Strenge aufzubereiten. Das Wechselspiel zwischen Entwurf und Analyse von Algorithmen gibt faszinierende Einsichten in das Handwerk des Informatikers.

Practical Applications Of Data Mining Emphasizes Both Theory And Applications Of Data Mining Algorithms. Various Topics Of Data Mining Techniques Are Identified And Described Throughout, Including Clustering, Association Rules, Rough Set Theory, Probability Theory, Neural Networks, Classification, And Fuzzy Logic. Each Of These Techniques Is Explored With A Theoretical Introduction And Its Effectiveness Is Demonstrated With Various Chapter Examples. This Book Will Help Any Database And IT Professional Understand How To Apply Data Mining Techniques To Real-World Problems. Following An Introduction To Data Mining Principles, Practical Applications Of Data Mining Introduces Association Rules To Describe The Generation Of Rules As The First Step In Data Mining. It Covers Classification And Clustering Methods To Show How Data Can Be Classified To Retrieve Information From Data. Statistical Functions And Drough Set Theory Are Discussed To Demonstrate How Statistical And Rough Set Formulas Can Be Used For Data Analytics And Knowlege Discovery. Neural Networks Is An Important Branch In Computational Intelligence. It Is Introduced And Explored In The Text To Investigate The Role Of Neural Network Algorithms In Data Analytics.

Haben Sie den Eindruck in Excel immer die gleichen Schritte zu wiederholen, wie ein "Hamster im Hamsterrad"? Haben Sie noch nie von Zielwertsuche, Teilergebnis, Konsolidierung, Szenariomanager, Solver gehort? Wollen Sie auf Augenhohe mit Ihrer Bank Ihre privaten/betrieblichen Finanzierungen verhandeln und die letzteren optimieren? Im vorliegenden Buch werden die genannten Excel-Techniken Schritt fur Schritt vertieft. Jedem Excel-Kapitel ist ein Finanzwirtschafts-Kapitel zugeordnet, in welchem die Excel-Technik angewendet wird.
Die Auswahl der Excel-Techniken unterliegt dem Prinzip "Eine zusatzliche Excel-Technik muss den Lernaufwand uberproportional in der Anwendung einspielen." Es wird ein klarer Fokus auf die Darlehenskalkulation und Optimierung von Finanzierungen (privat, betrieblich) gesetzt."

This book will provide the business or IT professional with a practical working knowledge of data modelling concepts and best practices, and how to apply these principles with PowerDesigner. You will build many PowerDesigner data models along the way, increasing your skills in first the fundamentals and later in the book the more advanced features of PowerDesigner. The book contains six sections: Section I introduces data modelling along with its purpose and variations. Also included is an explanation of the important role of a data modelling tool, the key features required of any data modelling tool, and an introduction to the essential features of PowerDesigner. Section II explains all of the components on a data model including entities, data elements, relationships, and keys, and describes how to create and manage these objects in PowerDesigner. Also included is a discussion of the importance of quality names and definitions for your objects. Section III dives into the relational and dimensional subject area, logical, and physical data models, and describes how PowerDesigner supports these models and the connections between them. Learn how to get information into and out of PowerDesigner, and improve the quality of your data models with a cross-reference of key PowerDesigner features with the Data Model Scorecard. Section IV contains a PowerDesigner workshop designed to consolidate everything for you. Section V focuses on additional PowerDesigner features (some of which have already been introduced) which make life easier for data modellers. Section VI discusses PowerDesigner topics beyond data modelling, including the XML physical model and the other types of model available in PowerDesigner. It also discusses the role of PowerDesigner in data management, using the DAMA Data Management Body of Knowledge (DAMA-DMBOK) framework.