Cognitive Computing and Big Data Analytics

Author: Judith S. Hurwitz
Publisher: John Wiley & Sons
ISBN: 9781118896631
Release Date: 2015-02-12
Genre: Computers

A comprehensive guide to learning technologies that unlock thevalue in big data Cognitive Computing provides detailed guidance towardbuilding a new class of systems that learn from experience andderive insights to unlock the value of big data. This book helpstechnologists understand cognitive computing's underlyingtechnologies, from knowledge representation techniques and naturallanguage processing algorithms to dynamic learning approaches basedon accumulated evidence, rather than reprogramming. Detailed caseexamples from the financial, healthcare, and manufacturing walkreaders step-by-step through the design and testing of cognitivesystems, and expert perspectives from organizations such asCleveland Clinic, Memorial Sloan-Kettering, as well as commercialvendors that are creating solutions. These organizations provideinsight into the real-world implementation of cognitive computingsystems. The IBM Watson cognitive computing platform is describedin a detailed chapter because of its significance in helping todefine this emerging market. In addition, the book includesimplementations of emerging projects from Qualcomm, Hitachi, Googleand Amazon. Today's cognitive computing solutions build on establishedconcepts from artificial intelligence, natural language processing,ontologies, and leverage advances in big data management andanalytics. They foreshadow an intelligent infrastructure thatenables a new generation of customer and context-aware smartapplications in all industries. Cognitive Computing is a comprehensive guide to thesubject, providing both the theoretical and practical guidancetechnologists need. Discover how cognitive computing evolved from promise toreality Learn the elements that make up a cognitive computingsystem Understand the groundbreaking hardware and softwaretechnologies behind cognitive computing Learn to evaluate your own application portfolio to find thebest candidates for pilot projects Leverage cognitive computing capabilities to transform theorganization Cognitive systems are rightly being hailed as the new era ofcomputing. Learn how these technologies enable emerging firms tocompete with entrenched giants, and forward-thinking establishedfirms to disrupt their industries. Professionals who currently workwith big data and analytics will see how cognitive computing buildson their foundation, and creates new opportunities. CognitiveComputing provides complete guidance to this new level ofhuman-machine interaction.

Big Data Analytics for Cloud IoT and Cognitive Computing

Author: Kai Hwang
Publisher: John Wiley & Sons
ISBN: 9781119247029
Release Date: 2017-08-14
Genre: Computers

The definitive guide to successfully integrating social, mobile, Big-Data analytics, cloud and IoT principles and technologies The main goal of this book is to spur the development of effective big-data computing operations on smart clouds that are fully supported by IoT sensing, machine learning and analytics systems. To that end, the authors draw upon their original research and proven track record in the field to describe a practical approach integrating big-data theories, cloud design principles, Internet of Things (IoT) sensing, machine learning, data analytics and Hadoop and Spark programming. Part 1 focuses on data science, the roles of clouds and IoT devices and frameworks for big-data computing. Big data analytics and cognitive machine learning, as well as cloud architecture, IoT and cognitive systems are explored, and mobile cloud-IoT-interaction frameworks are illustrated with concrete system design examples. Part 2 is devoted to the principles of and algorithms for machine learning, data analytics and deep learning in big data applications. Part 3 concentrates on cloud programming software libraries from MapReduce to Hadoop, Spark and TensorFlow and describes business, educational, healthcare and social media applications for those tools. The first book describing a practical approach to integrating social, mobile, analytics, cloud and IoT (SMACT) principles and technologies Covers theory and computing techniques and technologies, making it suitable for use in both computer science and electrical engineering programs Offers an extremely well-informed vision of future intelligent and cognitive computing environments integrating SMACT technologies Fully illustrated throughout with examples, figures and approximately 150 problems to support and reinforce learning Features a companion website with an instructor manual and PowerPoint slides www.wiley.com/go/hwangIOT Big-Data Analytics for Cloud, IoT and Cognitive Computing satisfies the demand among university faculty and students for cutting-edge information on emerging intelligent and cognitive computing systems and technologies. Professionals working in data science, cloud computing and IoT applications will also find this book to be an extremely useful working resource.

Cognitive Computing for Big Data Systems Over IoT

Author: Arun Kumar Sangaiah
Publisher: Springer
ISBN: 9783319706887
Release Date: 2017-12-30
Genre: Computers

This book brings a high level of fluidity to analytics and addresses recent trends, innovative ideas, challenges and cognitive computing solutions in big data and the Internet of Things (IoT). It explores domain knowledge, data science reasoning and cognitive methods in the context of the IoT, extending current data science approaches by incorporating insights from experts as well as a notion of artificial intelligence, and performing inferences on the knowledge The book provides a comprehensive overview of the constituent paradigms underlying cognitive computing methods, which illustrate the increased focus on big data in IoT problems as they evolve. It includes novel, in-depth fundamental research contributions from a methodological/application in data science accomplishing sustainable solution for the future perspective. Mainly focusing on the design of the best cognitive embedded data science technologies to process and analyze the large amount of data collected through the IoT, and aid better decision making, the book discusses adapting decision-making approaches under cognitive computing paradigms to demonstrate how the proposed procedures as well as big data and IoT problems can be handled in practice. This book is a valuable resource for scientists, professionals, researchers, and academicians dealing with the new challenges and advances in the specific areas of cognitive computing and data science approaches.

Smart Data Analytics

Author: Andreas Wierse
Publisher: Walter de Gruyter GmbH & Co KG
ISBN: 9783110461916
Release Date: 2017-06-26
Genre: Technology & Engineering

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 in der Praxis

Author: Jonas Freiknecht
Publisher: Carl Hanser Verlag GmbH Co KG
ISBN: 9783446441774
Release Date: 2014-10-01
Genre: Computers

BIG DATA IN DER PRAXIS // - Für Analysten, BI-Verantwortliche, Data-Scientists, Consultants - Auf der DVD finden Sie: 18 fertige Projekte, die im Buch Schritt für Schritt entwickelt werden; Videotutorials u.a. zur Installation von Hadoop, Hive, HBase (Gesamtdauer: 80 Min.); Testdatensätze für die Wissensdatenbank Dieses Buch bringt Ihnen das Thema Big Data auf sehr praktische Art und Weise nahe. Sie lernen Technologien, Tools und Methoden kennen, entwickeln Beispiel-Lösungen und bekommen aufgezeigt, wie Sie bestehende Systeme vorausschauend auf die mit dem Big Data-Trend einhergehenden Herausforderungen vorbereiten. Dazu werden Sie neben den bekannten Apache-Projekten wie Hadoop, Hive und HBase auch einige weniger bekannte Frameworks wie Apache UIMA oder Apache OpenNLP kennenlernen, um gezielt die Verarbeitung unstrukturierter Daten zu behandeln. Alle hier verwendeten Software-Komponenten stehen im vollen Umfang kostenlos im Internet zur Verfügung. Gemeinsam mit dem Autor werden Sie ganz konkret Schritt für Schritt viele kleinere Projekte aufbauen bis hin zu einer fertigen und funktionstüchtigen Implementierung. Ziel des Buches ist es, Sie auf den Effekt und den Mehrwert der neuen Möglichkeiten aufmerksam zu machen, sodass Sie diese konstruktiv in Ihr Unternehmen tragen können und für sich und Ihre Kollegen somit ein Bewusstsein für den Wert Ihrer Daten schaffen. AUS DEM INHALT // Einführung rund um Big Data // Hadoop installieren, konfigurieren & bedienen // HDFS, Map-Reduce & YARN: Daten speichern und verarbeiten // Hadoop-Ecosystem: Überblick über dessen Komponenten // Einführung in NoSQL // HBase installieren, einrichten & auf Daten zugreifen // Data-Warehousing mit Apache Hive // HiveQL als Abfragesprache, Hive Security, Hive & JDBC // Datenimport aus relationalen Datenbanken mit Sqoop // Big Data-Visualisierung: Diagrammarten, Tipps & Trends // Visualisierungs-Frameworks im Vergleich // D3.js: Entwicklung einiger Beispieldiagramme // Entwicklung einer abschließenden Big Data-Analyse-Lösung // Troubleshooting für die Arbeit mit Hadoop, Hive & HBase

Machine Learning for Decision Makers

Author: Patanjali Kashyap
Publisher: Apress
ISBN: 9781484229880
Release Date: 2018-01-04
Genre: Computers

Take a deep dive into the concepts of machine learning as they apply to contemporary business and management. You will learn how machine learning techniques are used to solve fundamental and complex problems in society and industry. Machine Learning for Decision Makers serves as an excellent resource for establishing the relationship of machine learning with IoT, big data, and cognitive and cloud computing to give you an overview of how these modern areas of computing relate to each other. This book introduces a collection of the most important concepts of machine learning and sets them in context with other vital technologies that decision makers need to know about. These concepts span the process from envisioning the problem to applying machine-learning techniques to your particular situation. This discussion also provides an insight to help deploy the results to improve decision-making. The book uses case studies and jargon busting to help you grasp the theory of machine learning quickly. You'll soon gain the big picture of machine learning and how it fits with other cutting-edge IT services. This knowledge will give you confidence in your decisions for the future of your business. What You Will Learn Discover the machine learning, big data, and cloud and cognitive computing technology stack Gain insights into machine learning concepts and practices Understand business and enterprise decision-making using machine learning Absorb machine-learning best practices Who This Book Is For Managers tasked with making key decisions who want to learn how and when machine learning and related technologies can help them.

Data Science f r Dummies

Author: Lillian Pierson
Publisher: John Wiley & Sons
ISBN: 9783527806751
Release Date: 2016-04-22
Genre: Mathematics

Daten, Daten, Daten? Sie haben schon Kenntnisse in Excel und Statistik, wissen aber noch nicht, wie all die Datensätze helfen sollen, bessere Entscheidungen zu treffen? Von Lillian Pierson bekommen Sie das dafür notwendige Handwerkszeug: Bauen Sie Ihre Kenntnisse in Statistik, Programmierung und Visualisierung aus. Nutzen Sie Python, R, SQL, Excel und KNIME. Zahlreiche Beispiele veranschaulichen die vorgestellten Methoden und Techniken. So können Sie die Erkenntnisse dieses Buches auf Ihre Daten übertragen und aus deren Analyse unmittelbare Schlüsse und Konsequenzen ziehen.

Big Data

Author: Viktor Mayer-Schönberger
Publisher: Redline Wirtschaft
ISBN: 9783864144592
Release Date: 2013-10-08
Genre: Political Science

Ob Kaufverhalten, Grippewellen oder welche Farbe am ehesten verrät, ob ein Gebrauchtwagen in einem guten Zustand ist – noch nie gab es eine solche Menge an Daten und noch nie bot sich die Chance, durch Recherche und Kombination in der Daten¬flut blitzschnell Zusammenhänge zu entschlüsseln. Big Data bedeutet nichts weniger als eine Revolution für Gesellschaft, Wirtschaft und Politik. Es wird die Weise, wie wir über Gesundheit, Erziehung, Innovation und vieles mehr denken, völlig umkrempeln. Und Vorhersagen möglich machen, die bisher undenkbar waren. Die Experten Viktor Mayer-Schönberger und Kenneth Cukier beschreiben in ihrem Buch, was Big Data ist, welche Möglichkeiten sich eröffnen, vor welchen Umwälzungen wir alle stehen – und verschweigen auch die dunkle Seite wie das Ausspähen von persönlichen Daten und den drohenden Verlust der Privatsphäre nicht.

big data work

Author: Thomas H. Davenport
Publisher: Vahlen
ISBN: 9783800648153
Release Date: 2014-10-15
Genre: Fiction

Big Data in Unternehmen. Dieses neue Buch gibt Managern ein umfassendes Verständnis dafür, welche Bedeutung Big Data für Unternehmen zukünftig haben wird und wie Big Data tatsächlich genutzt werden kann. Am Ende jedes Kapitels aktivieren Fragen, selbst nach Lösungen für eine erfolgreiche Implementierung und Nutzung von Big Data im eigenen Unternehmen zu suchen. Die Schwerpunkte - Warum Big Data für Sie und Ihr Unternehmen wichtig ist - Wie Big Data Ihre Arbeit, Ihr Unternehmen und Ihre Branche verändern - - wird - Entwicklung einer Big Data-Strategie - Der menschliche Aspekt von Big Data - Technologien für Big Data - Wie Sie erfolgreich mit Big Data arbeiten - Was Sie von Start-ups und Online-Unternehmen lernen können - Was Sie von großen Unternehmen lernen können: Big Data und Analytics 3.0 Der Experte Thomas H. Davenport ist Professor für Informationstechnologie und -management am Babson College und Forschungswissenschaftler am MIT Center for Digital Business. Zudem ist er Mitbegründer und Forschungsdirektor am International Institute for Analytics und Senior Berater von Deloitte Analytics.

Applied Heath Care Analytics

Author: Mark Albert
Publisher: Advances and Opportunities wit
ISBN: 9813142545
Release Date: 2017-06-30
Genre: Computers

The healthcare systems in the US and globally are undergoing a period of rapid transformation. Medical technology breakthroughs, economic pressures and demographic trends are driving that transformation, but key enablers and catalysts for those changes are advancements in Analytics, Data Science, Cognitive Computing, and Machine Learning. Massive volumes of data are created during regular healthcare administration, delivery, and research operations; additionally, outside the medical community people produce data as part of their daily activities and social interactions that can be mined for medical use. How can this data be put to use in an ethical way respecting privacy and security to achieve the goal of high quality, accessible and affordable Healthcare? Advanced analytics and cognitive computing are a big part of the answer. In Applied Heath Care Analytics, the authors provide a concise yet comprehensive review of the key enabling and explain how those technologies are becoming the backbone of the Healthcare of tomorrow.

Handbook of Big Data Technologies

Author: Albert Y. Zomaya
Publisher: Springer
ISBN: 9783319493404
Release Date: 2017-02-25
Genre: Computers

This handbook offers comprehensive coverage of recent advancements in Big Data technologies and related paradigms. Chapters are authored by international leading experts in the field, and have been reviewed and revised for maximum reader value. The volume consists of twenty-five chapters organized into four main parts. Part one covers the fundamental concepts of Big Data technologies including data curation mechanisms, data models, storage models, programming models and programming platforms. It also dives into the details of implementing Big SQL query engines and big stream processing systems. Part Two focuses on the semantic aspects of Big Data management including data integration and exploratory ad hoc analysis in addition to structured querying and pattern matching techniques. Part Three presents a comprehensive overview of large scale graph processing. It covers the most recent research in large scale graph processing platforms, introducing several scalable graph querying and mining mechanisms in domains such as social networks. Part Four details novel applications that have been made possible by the rapid emergence of Big Data technologies such as Internet-of-Things (IOT), Cognitive Computing and SCADA Systems. All parts of the book discuss open research problems, including potential opportunities, that have arisen from the rapid progress of Big Data technologies and the associated increasing requirements of application domains. Designed for researchers, IT professionals and graduate students, this book is a timely contribution to the growing Big Data field. Big Data has been recognized as one of leading emerging technologies that will have a major contribution and impact on the various fields of science and varies aspect of the human society over the coming decades. Therefore, the content in this book will be an essential tool to help readers understand the development and future of the field.

Distributed Computing in Big Data Analytics

Author: Sourav Mazumder
Publisher: Springer
ISBN: 9783319598345
Release Date: 2017-08-29
Genre: Computers

Big data technologies are used to achieve any type of analytics in a fast and predictable way, thus enabling better human and machine level decision making. Principles of distributed computing are the keys to big data technologies and analytics. The mechanisms related to data storage, data access, data transfer, visualization and predictive modeling using distributed processing in multiple low cost machines are the key considerations that make big data analytics possible within stipulated cost and time practical for consumption by human and machines. However, the current literature available in big data analytics needs a holistic perspective to highlight the relation between big data analytics and distributed processing for ease of understanding and practitioner use. This book fills the literature gap by addressing key aspects of distributed processing in big data analytics. The chapters tackle the essential concepts and patterns of distributed computing widely used in big data analytics. This book discusses also covers the main technologies which support distributed processing. Finally, this book provides insight into applications of big data analytics, highlighting how principles of distributed computing are used in those situations. Practitioners and researchers alike will find this book a valuable tool for their work, helping them to select the appropriate technologies, while understanding the inherent strengths and drawbacks of those technologies.