Smart Data Analytics

Mit Hilfe von Big Data Zusammenhänge erkennen und Potentiale nutzen

Author: Andreas Wierse,Till Riedel

Publisher: Walter de Gruyter GmbH & Co KG

ISBN: 3110463954

Category: Technology & Engineering

Page: 440

View: 1713

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Wenn in Datenbergen wertvolle Geheimnisse schlummern, aus denen Profit erzielt werden soll, dann geht es um Big Data. Doch wie schöpft man aus »großen Daten« echte Werte, wenn man nicht gerade Google ist? Um aus Unternehmens-, Maschinen- oder Sensordaten einen Ertrag zu erzielen, reicht Big Data-Technologie allein nicht aus. Entscheidend sind die übergeordneten Innovations prozesse: die smarte Analyse von Big Data. Erst durch den kompetenten Einsatz der richtigen Werkzeuge und Techniken werden aus Big Data tatsächlich Smart Data. Das Praxishandbuch Smart Data Analytics gibt einen Überblick über die Technologie, die bei der Analyse von großen und heterogenen Datenmengen – inklusive Echtzeitdaten – zum Einsatz kommt. Elf Praxisbeispiele zeigen die konkrete Anwendung in kleinen und mittelständischen Unternehmen. So erfahren Sie, wie Sie Ihr Smart Data Analytics-Projekt in Ihrem eigenen Unternehmen vorbereiten und umsetzen können. Das Buch erläutert neben den organisatorischen Aspekten auch die rechtlichen Rahmenbedingungen. Und es zeigt, wie Sie sowohl den Nutzen bewerten können, der aus den Daten gezogen werden soll, als auch den Aufwand, den Sie dafür betreiben müssen. Denn Smart Data steht für mehr als nur die Untersuchung großer Datenmengen: Smart Data Analytics ist der Schlüssel zu einem smarten Umgang mit Ihren Unternehmensdaten und hilft, bislang unentdecktes Potenzial zu entdecken. Dr. Andreas Wierse studierte Mathematik und promovierte in den Ingenieurwissenschaften im Bereich Visualisierung, seit 2011 unterstützt er mittelständische Unternehmen rund um Big und Smart Data Technologie. Dr. Till Riedel lehrt als Informatiker am KIT und koordiniert im Smart Data Solution Center Baden-Württemberg und Smart Data Innovation Lab Forschung und Innovation auf industriellen Datenschätzen.

Big Data Analytics

Turning Big Data into Big Money

Author: Frank J. Ohlhorst

Publisher: John Wiley & Sons

ISBN: 1118239040

Category: Business & Economics

Page: 176

View: 7888

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Unique insights to implement big data analytics and reap bigreturns to your bottom line Focusing on the business and financial value of big dataanalytics, respected technology journalist Frank J. Ohlhorst shareshis insights on the newly emerging field of big data analytics inBig Data Analytics. This breakthrough book demonstrates theimportance of analytics, defines the processes, highlights thetangible and intangible values and discusses how you can turn abusiness liability into actionable material that can be used toredefine markets, improve profits and identify new businessopportunities. Reveals big data analytics as the next wave for businesseslooking for competitive advantage Takes an in-depth look at the financial value of big dataanalytics Offers tools and best practices for working with big data Once the domain of large on-line retailers such as eBay andAmazon, big data is now accessible by businesses of all sizes andacross industries. From how to mine the data your company collects,to the data that is available on the outside, Big DataAnalytics shows how you can leverage big data into a keycomponent in your business's growth strategy.

Practical Guide to SAP HANA and Big Data Analytics

Author: Dominique Alfermann,Stefan Hartmann

Publisher: Espresso Tutorials GmbH

ISBN: 3960128649

Category:

Page: 235

View: 5150

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In this book written for SAP BI, big data, and IT architects, the authors expertly provide clear recommendations for building modern analytics architectures running on SAP HANA technologies. Explore integration with big data frameworks and predictive analytics components. Obtain the tools you need to assess possible architecture scenarios and get guidelines for choosing the best option for your organization. Know your options for on-premise, in the cloud, and hybrid solutions. Readers will be guided through SAP BW/4HANA and SAP HANA native data warehouse scenarios, as well as field-tested integration options with big data platforms. Explore migration options and architecture best practices. Consider organizational and procedural changes resulting from the move to a new, up-to-date analytics architecture that supports your data-driven or data-informed organization. By using practical examples, tips, and screenshots, this book explores: - SAP HANA and SAP BW/4HANA architecture concepts - Predictive Analytics and Big Data component integration - Recommendations for a sustainable, future-proof analytics solutions - Organizational impact and change management

Big Data Analytics Beyond Hadoop

Real-Time Applications with Storm, Spark, and More Hadoop Alternatives

Author: Vijay Srinivas Agneeswaran

Publisher: FT Press

ISBN: 0133838250

Category: Computers

Page: 200

View: 4510

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Master alternative Big Data technologies that can do what Hadoop can't: real-time analytics and iterative machine learning. When most technical professionals think of Big Data analytics today, they think of Hadoop. But there are many cutting-edge applications that Hadoop isn't well suited for, especially real-time analytics and contexts requiring the use of iterative machine learning algorithms. Fortunately, several powerful new technologies have been developed specifically for use cases such as these. Big Data Analytics Beyond Hadoop is the first guide specifically designed to help you take the next steps beyond Hadoop. Dr. Vijay Srinivas Agneeswaran introduces the breakthrough Berkeley Data Analysis Stack (BDAS) in detail, including its motivation, design, architecture, Mesos cluster management, performance, and more. He presents realistic use cases and up-to-date example code for: Spark, the next generation in-memory computing technology from UC Berkeley Storm, the parallel real-time Big Data analytics technology from Twitter GraphLab, the next-generation graph processing paradigm from CMU and the University of Washington (with comparisons to alternatives such as Pregel and Piccolo) Halo also offers architectural and design guidance and code sketches for scaling machine learning algorithms to Big Data, and then realizing them in real-time. He concludes by previewing emerging trends, including real-time video analytics, SDNs, and even Big Data governance, security, and privacy issues. He identifies intriguing startups and new research possibilities, including BDAS extensions and cutting-edge model-driven analytics. Big Data Analytics Beyond Hadoop is an indispensable resource for everyone who wants to reach the cutting edge of Big Data analytics, and stay there: practitioners, architects, programmers, data scientists, researchers, startup entrepreneurs, and advanced students.

Big Data and Analytics

Strategic and Organizational Impacts

Author: Vincenzo Morabito

Publisher: Springer

ISBN: 3319106651

Category: Business & Economics

Page: 183

View: 7782

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This book presents and discusses the main strategic and organizational challenges posed by Big Data and analytics in a manner relevant to both practitioners and scholars. The first part of the book analyzes strategic issues relating to the growing relevance of Big Data and analytics for competitive advantage, which is also attributable to empowerment of activities such as consumer profiling, market segmentation, and development of new products or services. Detailed consideration is also given to the strategic impact of Big Data and analytics on innovation in domains such as government and education and to Big Data-driven business models. The second part of the book addresses the impact of Big Data and analytics on management and organizations, focusing on challenges for governance, evaluation, and change management, while the concluding part reviews real examples of Big Data and analytics innovation at the global level. The text is supported by informative illustrations and case studies, so that practitioners can use the book as a toolbox to improve understanding and exploit business opportunities related to Big Data and analytics.

Big Data Analytics Made Easy

Author: Y. Lakshmi Prasad

Publisher: Notion Press

ISBN: 1946390720

Category: Computers

Page: 192

View: 3844

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Big Data Analytics Made Easy is a must-read for everybody as it explains the power of Analytics in a simple and logical way along with an end to end code in R. Even if you are a novice in Big Data Analytics, you will still be able to understand the concepts explained in this book. If you are already working in Analytics and dealing with Big Data, you will still find this book useful, as it covers exhaustive Data Mining Techniques, which are considered to be Advanced topics. It covers Machine Learning concepts and provides in-depth knowledge on unsupervised as well as supervised Learning, which is very important for decision-making. The toughest Data Analytics concepts are made simpler, It features examples from all the domains so that the reader gets connected to the book easily. This book is like a personal trainer that will help you master the Art of Data Science.

Real-Time Big Data Analytics: Emerging Architecture

Author: Mike Barlow

Publisher: "O'Reilly Media, Inc."

ISBN: 1449364705

Category: Computers

Page: N.A

View: 8439

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Five or six years ago, analysts working with big datasets made queries and got the results back overnight. The data world was revolutionized a few years ago when Hadoop and other tools made it possible to getthe results from queries in minutes. But the revolution continues. Analysts now demand sub-second, near real-time query results. Fortunately, we have the tools to deliver them. This report examines tools and technologies that are driving real-time big data analytics.

Big Data Analytics

Methods and Applications

Author: Saumyadipta Pyne,B.L.S. Prakasa Rao,S.B. Rao

Publisher: Springer

ISBN: 8132236289

Category: Computers

Page: 276

View: 3076

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This book has a collection of articles written by Big Data experts to describe some of the cutting-edge methods and applications from their respective areas of interest, and provides the reader with a detailed overview of the field of Big Data Analytics as it is practiced today. The chapters cover technical aspects of key areas that generate and use Big Data such as management and finance; medicine and healthcare; genome, cytome and microbiome; graphs and networks; Internet of Things; Big Data standards; bench-marking of systems; and others. In addition to different applications, key algorithmic approaches such as graph partitioning, clustering and finite mixture modelling of high-dimensional data are also covered. The varied collection of themes in this volume introduces the reader to the richness of the emerging field of Big Data Analytics.

BIG DATA ANALYTICS

Author: Parag Kulkarni,Sarang Joshi,,Meta S. Brown

Publisher: PHI Learning Pvt. Ltd.

ISBN: 8120351169

Category: Language Arts & Disciplines

Page: 208

View: 4767

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The book is an unstructured data mining quest, which takes the reader through different features of unstructured data mining while unfolding the practical facets of Big Data. It emphasizes more on machine learning and mining methods required for processing and decision-making. The text begins with the introduction to the subject and explores the concept of data mining methods and models along with the applications. It then goes into detail on other aspects of Big Data analytics, such as clustering, incremental learning, multi-label association and knowledge representation. The readers are also made familiar with business analytics to create value. The book finally ends with a discussion on the areas where research can be explored.

Big Data Analytics

A Practical Guide for Managers

Author: Kim H. Pries,Robert Dunnigan

Publisher: CRC Press

ISBN: 1482234521

Category: Computers

Page: 576

View: 7740

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With this book, managers and decision makers are given the tools to make more informed decisions about big data purchasing initiatives. Big Data Analytics: A Practical Guide for Managers not only supplies descriptions of common tools, but also surveys the various products and vendors that supply the big data market. Comparing and contrasting the different types of analysis commonly conducted with big data, this accessible reference presents clear-cut explanations of the general workings of big data tools. Instead of spending time on HOW to install specific packages, it focuses on the reasons WHY readers would install a given package. The book provides authoritative guidance on a range of tools, including open source and proprietary systems. It details the strengths and weaknesses of incorporating big data analysis into decision-making and explains how to leverage the strengths while mitigating the weaknesses. Describes the benefits of distributed computing in simple terms Includes substantial vendor/tool material, especially for open source decisions Covers prominent software packages, including Hadoop and Oracle Endeca Examines GIS and machine learning applications Considers privacy and surveillance issues The book further explores basic statistical concepts that, when misapplied, can be the source of errors. Time and again, big data is treated as an oracle that discovers results nobody would have imagined. While big data can serve this valuable function, all too often these results are incorrect, yet are still reported unquestioningly. The probability of having erroneous results increases as a larger number of variables are compared unless preventative measures are taken. The approach taken by the authors is to explain these concepts so managers can ask better questions of their analysts and vendors as to the appropriateness of the methods used to arrive at a conclusion. Because the world of science and medicine has been grappling with similar issues in the publication of studies, the authors draw on their efforts and apply them to big data.

Practical Big Data Analytics

Hands-on techniques to implement enterprise analytics and machine learning using Hadoop, Spark, NoSQL and R

Author: Nataraj Dasgupta

Publisher: Packt Publishing Ltd

ISBN: 1783554401

Category: Computers

Page: 412

View: 5750

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Get command of your organizational Big Data using the power of data science and analytics Key Features A perfect companion to boost your Big Data storing, processing, analyzing skills to help you take informed business decisions Work with the best tools such as Apache Hadoop, R, Python, and Spark for NoSQL platforms to perform massive online analyses Get expert tips on statistical inference, machine learning, mathematical modeling, and data visualization for Big Data Book Description Big Data analytics relates to the strategies used by organizations to collect, organize and analyze large amounts of data to uncover valuable business insights that otherwise cannot be analyzed through traditional systems. Crafting an enterprise-scale cost-efficient Big Data and machine learning solution to uncover insights and value from your organization's data is a challenge. Today, with hundreds of new Big Data systems, machine learning packages and BI Tools, selecting the right combination of technologies is an even greater challenge. This book will help you do that. With the help of this guide, you will be able to bridge the gap between the theoretical world of technology with the practical ground reality of building corporate Big Data and data science platforms. You will get hands-on exposure to Hadoop and Spark, build machine learning dashboards using R and R Shiny, create web-based apps using NoSQL databases such as MongoDB and even learn how to write R code for neural networks. By the end of the book, you will have a very clear and concrete understanding of what Big Data analytics means, how it drives revenues for organizations, and how you can develop your own Big Data analytics solution using different tools and methods articulated in this book. What you will learn - Get a 360-degree view into the world of Big Data, data science and machine learning - Broad range of technical and business Big Data analytics topics that caters to the interests of the technical experts as well as corporate IT executives - Get hands-on experience with industry-standard Big Data and machine learning tools such as Hadoop, Spark, MongoDB, KDB+ and R - Create production-grade machine learning BI Dashboards using R and R Shiny with step-by-step instructions - Learn how to combine open-source Big Data, machine learning and BI Tools to create low-cost business analytics applications - Understand corporate strategies for successful Big Data and data science projects - Go beyond general-purpose analytics to develop cutting-edge Big Data applications using emerging technologies Who this book is for The book is intended for existing and aspiring Big Data professionals who wish to become the go-to person in their organization when it comes to Big Data architecture, analytics, and governance. While no prior knowledge of Big Data or related technologies is assumed, it will be helpful to have some programming experience.

Big Data Analytics in Bioinformatics and Healthcare

Author: Wang, Baoying

Publisher: IGI Global

ISBN: 1466666129

Category: Computers

Page: 528

View: 7541

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As technology evolves and electronic data becomes more complex, digital medical record management and analysis becomes a challenge. In order to discover patterns and make relevant predictions based on large data sets, researchers and medical professionals must find new methods to analyze and extract relevant health information. Big Data Analytics in Bioinformatics and Healthcare merges the fields of biology, technology, and medicine in order to present a comprehensive study on the emerging information processing applications necessary in the field of electronic medical record management. Complete with interdisciplinary research resources, this publication is an essential reference source for researchers, practitioners, and students interested in the fields of biological computation, database management, and health information technology, with a special focus on the methodologies and tools to manage massive and complex electronic information.

Big Data Analytics with Java

Author: Rajat Mehta

Publisher: Packt Publishing Ltd

ISBN: 1787282198

Category: Computers

Page: 418

View: 9893

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Learn the basics of analytics on big data using Java, machine learning and other big data tools About This Book Acquire real-world set of tools for building enterprise level data science applications Surpasses the barrier of other languages in data science and learn create useful object-oriented codes Extensive use of Java compliant big data tools like apache spark, Hadoop, etc. Who This Book Is For This book is for Java developers who are looking to perform data analysis in production environment. Those who wish to implement data analysis in their Big data applications will find this book helpful. What You Will Learn Start from simple analytic tasks on big data Get into more complex tasks with predictive analytics on big data using machine learning Learn real time analytic tasks Understand the concepts with examples and case studies Prepare and refine data for analysis Create charts in order to understand the data See various real-world datasets In Detail This book covers case studies such as sentiment analysis on a tweet dataset, recommendations on a movielens dataset, customer segmentation on an ecommerce dataset, and graph analysis on actual flights dataset. This book is an end-to-end guide to implement analytics on big data with Java. Java is the de facto language for major big data environments, including Hadoop. This book will teach you how to perform analytics on big data with production-friendly Java. This book basically divided into two sections. The first part is an introduction that will help the readers get acquainted with big data environments, whereas the second part will contain a hardcore discussion on all the concepts in analytics on big data. It will take you from data analysis and data visualization to the core concepts and advantages of machine learning, real-life usage of regression and classification using Naive Bayes, a deep discussion on the concepts of clustering,and a review of simple neural networks on big data using deepLearning4j or plain Java Spark code. This book is a must-have book for Java developers who want to start learning big data analytics and want to use it in the real world. Style and approach The approach of book is to deliver practical learning modules in manageable content. Each chapter is a self-contained unit of a concept in big data analytics. Book will step by step builds the competency in the area of big data analytics. Examples using real world case studies to give ideas of real applications and how to use the techniques mentioned. The examples and case studies will be shown using both theory and code.

Data Analytics and Big Data

Author: Soraya Sedkaoui

Publisher: John Wiley & Sons

ISBN: 1119528062

Category: Computers

Page: 220

View: 6724

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The main purpose of this book is to investigate, explore and describe approaches and methods to facilitate data understanding through analytics solutions based on its principles, concepts and applications. But analyzing data is also about involving the use of software. For this, and in order to cover some aspect of data analytics, this book uses software (Excel, SPSS, Python, etc) which can help readers to better understand the analytics process in simple terms and supporting useful methods in its application.

Der Einsatz von Methoden aus dem Bereich Big Data Analytics zur Unterstützung betrieblicher Entscheidungen

Author: Daniel Mader

Publisher: GRIN Verlag

ISBN: 3668493677

Category: Computers

Page: 69

View: 4292

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Masterarbeit aus dem Jahr 2016 im Fachbereich Informatik - Technische Informatik, Note: 2,3, FOM Hochschule für Oekonomie & Management gemeinnützige GmbH, Köln, Sprache: Deutsch, Abstract: Das Ziel der vorliegenden Master-Thesis liegt darin, eine Übersicht relevanter einsetzbarer Analysemethoden für Unternehmen zu liefern. Zudem soll die Relevanz von Big Data Analytics für innerbetriebliche Entscheidungsprozesse durch Beispielanwendungen nachgewiesen werden und im Nachgang eine Empfehlung für diverse Unternehmen verfasst werden. Der Begriff Big Data hat unsere Gesellschaft bereits entscheidend verändert und charakterisiert sich durch eine Ära der Massendaten, die aus unterschiedlichsten Quellen stammen können. Daten werden mit zunehmender Geschwindigkeit erzeugt und sind vorwiegend in einer unstrukturierten Form vorhanden. Der Begriff selbst steht für die digitale Verarbeitung dieser Datenflut diversester Art und Herkunft. Einem aktuellen Bericht zur Folge, soll das globale Datenvolumen zwischen den Jahren 2005 und 2020 um den Faktor 300 gewachsen sein, sodass von einem Wert im Bereich von ca. 40.000 Exabytes ausgegangen werden kann, wobei ein Exabyte ca. einer Milliarde Gigabyte entspricht. Das allgemeine Interesse an Big Data ist präsent und wächst stetig. Dabei muss auch das Augenmerk auf eine performante Hard- und Software-Lösung gerichtet werden. Nicht nur das Sammeln der immer mehr werdenden Daten stellt eine Herausforderung für Unternehmen dar, sondern auch die nachträgliche Analyse dieser Daten, die auch als Big Data Analytics bezeichnet wird. Daten haben einen unschätzbaren Wert für Unternehmen und können einen enormen Wettbewerbsvorteil verschaffen. Somit wird Big Data Analytics in Zukunft eine immer stärkere Rolle spielen, um beispielsweise durch ein effektiveres Marketing bessere Umsätze zu generieren. Zunächst gilt es den Begriff Big Data klar zu definieren und somit von dem klassischen Data Warehouse abzutrennen. Im weiteren Verlauf soll definiert werden, welche Methoden sich besonders gut für die Unterstützung betrieblicher Entscheidungen eignen und welchen Mehrwert diese bringen. Anhand von diversen Anwendungsbeispielen soll im Verlauf verdeutlicht werden, in welchen Bereichen Big Data Anwendung findet.

Big Data Analytics

Tools and Technology for Effective Planning

Author: Arun K. Somani,Ganesh Chandra Deka

Publisher: CRC Press

ISBN: 1315391244

Category: Computers

Page: 399

View: 9352

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The proposed book will discuss various aspects of big data Analytics. It will deliberate upon the tools, technology, applications, use cases and research directions in the field. Chapters would be contributed by researchers, scientist and practitioners from various reputed universities and organizations for the benefit of readers.

Big Data Analytics

Third International Conference, BDA 2014, New Delhi, India, December 20-23, 2014. Proceedings

Author: Srinath Srinivasa,Sameep Mehta

Publisher: Springer

ISBN: 3319138200

Category: Computers

Page: 197

View: 5589

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This book constitutes the refereed conference proceedings of the Third International Conference on Big Data Analytics, BDA 2014, held in New Delhi, India, in December 2014. The 11 revised full papers and 6 short papers were carefully reviewed and selected from 35 submissions and cover topics on media analytics; geospatial big data; semantics and data models; search and retrieval; graphics and visualization; application-specific big data.