Data-Driven Modeling & Scientific Computation

Methods for Complex Systems & Big Data

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Author: J. Nathan Kutz

Publisher: Oxford University Press

ISBN: 0199660336

Category: Computers

Page: 638

View: 2403

Combining scientific computing methods and algorithms with modern data analysis techniques, including basic applications of compressive sensing and machine learning, this book develops techniques that allow for the integration of the dynamics of complex systems and big data. MATLAB is used throughout for mathematical solution strategies.

Data-Driven Computational Methods

Parameter and Operator Estimations

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Author: John Harlim

Publisher: Cambridge University Press

ISBN: 1108472478

Category: Computers

Page: 169

View: 713

Describes computational methods for parametric and nonparametric modeling of stochastic dynamics. Aimed at graduate students, and suitable for self-study.

Strategic Data-Based Wisdom in the Big Data Era

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Author: Girard, John

Publisher: IGI Global

ISBN: 1466681233

Category: Business & Economics

Page: 312

View: 9206

The ability to uncover, share, and utilize knowledge is one of the most vital components to the success of any organization. While new technologies and techniques of knowledge dissemination are promising, there is still a struggle to derive and circulate meaningful information from large data sets. Strategic Data-Based Wisdom in the Big Data Era combines the latest empirical research findings, best practices, and applicable theoretical frameworks surrounding data analytics and knowledge acquisition. Providing a multi-disciplinary perspective of the subject area, this book is an essential reference source for professionals and researchers working in the field of knowledge management who would like to improve their understanding of the strategic role of data-based wisdom in different types of work communities and environments.

Big Data

Die Revolution, die unser Leben verändern wird

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Author: Viktor Mayer-Schönberger,Viktor; Cukier Mayer-Schönberger

Publisher: Redline Wirtschaft

ISBN: 3864144590

Category: Political Science

Page: 288

View: 9616

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 in Complex Systems

Challenges and Opportunities

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Author: Aboul Ella Hassanien,Ahmad Taher Azar,Vaclav Snasael,Janusz Kacprzyk,Jemal H. Abawajy

Publisher: Springer

ISBN: 331911056X

Category: Computers

Page: 499

View: 3981

This volume provides challenges and Opportunities with updated, in-depth material on the application of Big data to complex systems in order to find solutions for the challenges and problems facing big data sets applications. Much data today is not natively in structured format; for example, tweets and blogs are weakly structured pieces of text, while images and video are structured for storage and display, but not for semantic content and search. Therefore transforming such content into a structured format for later analysis is a major challenge. Data analysis, organization, retrieval, and modeling are other foundational challenges treated in this book. The material of this book will be useful for researchers and practitioners in the field of big data as well as advanced undergraduate and graduate students. Each of the 17 chapters in the book opens with a chapter abstract and key terms list. The chapters are organized along the lines of problem description, related works, and analysis of the results and comparisons are provided whenever feasible.

Harness Oil and Gas Big Data with Analytics

Optimize Exploration and Production with Data-Driven Models

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Author: Keith R. Holdaway

Publisher: John Wiley & Sons

ISBN: 1118910893

Category: Business & Economics

Page: 384

View: 5080

Use big data analytics to efficiently drive oil and gasexploration and production Harness Oil and Gas Big Data with Analytics provides acomplete view of big data and analytics techniques as they areapplied to the oil and gas industry. Including a compendium ofspecific case studies, the book underscores the acute need foroptimization in the oil and gas exploration and production stagesand shows how data analytics can provide such optimization. Thisspans exploration, development, production and rejuvenation of oiland gas assets. The book serves as a guide for fully leveraging data,statistical, and quantitative analysis, exploratory and predictivemodeling, and fact-based management to drive decision making in oiland gas operations. This comprehensive resource delves into thethree major issues that face the oil and gas industry during theexploration and production stages: Data management, including storing massive quantities of datain a manner conducive to analysis and effectively retrieving,backing up, and purging data Quantification of uncertainty, including a look at thestatistical and data analytics methods for making predictions anddetermining the certainty of those predictions Risk assessment, including predictive analysis of thelikelihood that known risks are realized and how to properly dealwith unknown risks Covering the major issues facing the oil and gas industry in theexploration and production stages, Harness Big Data withAnalytics reveals how to model big data to realize efficienciesand business benefits.

Strategic Engineering for Cloud Computing and Big Data Analytics

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Author: Amin Hosseinian-Far,Muthu Ramachandran,Dilshad Sarwar

Publisher: Springer

ISBN: 3319524917

Category: Technology & Engineering

Page: 226

View: 2610

This book demonstrates the use of a wide range of strategic engineering concepts, theories and applied case studies to improve the safety, security and sustainability of complex and large-scale engineering and computer systems. It first details the concepts of system design, life cycle, impact assessment and security to show how these ideas can be brought to bear on the modeling, analysis and design of information systems with a focused view on cloud-computing systems and big data analytics. This informative book is a valuable resource for graduate students, researchers and industry-based practitioners working in engineering, information and business systems as well as strategy.

Computational Methods for Large Sparse Power Systems Analysis

An Object Oriented Approach

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Author: S. A. Soman,S. A. Khaparde,Shubha Pandit

Publisher: Springer Science & Business Media

ISBN: 9780792375913

Category: Computers

Page: 333

View: 8944

Computational methods in Power Systems require significant inputs from diverse disciplines, such as data base structures, numerical analysis etc. Strategic decisions in sparsity exploitation and algorithm design influence large-scale simulation and high-speed computations. Selection of programming paradigm shapes the design, its modularity and reusability. This has a far reaching effect on software maintenance. Computational Methods for Large Sparse Power Systems Analysis: An Object Oriented Approach provides a unified object oriented (OO) treatment for power system analysis. Sparsity exploitation techniques in OO paradigm are emphasized to facilitate large scale and fast computing. Specific applications like large-scale load flow, short circuit analysis, state estimation and optimal power flow are discussed within this framework. A chapter on modeling and computational issues in power system dynamics is also included. Motivational examples and illustrations are included throughout the book. A library of C++ classes provided along with this book has classes for transmission lines, transformers, substation etc. A CD-ROM with C++ programs is also included. It contains load flow, short circuit analysis and network topology processor applications. Power system data is provided and systems up to 150 buses can be studied. Other Special Features: This book is the first of its kind, covering power system applications designed with an OO perspective. Chapters on object orientation for modeling of power system computations, data structure, large sparse linear system solver, sparse QR decomposition in an OO framework are special features of this book.

Datenanalyse mit Python

Auswertung von Daten mit Pandas, NumPy und IPython

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Author: Wes McKinney

Publisher: O'Reilly

ISBN: 3960102143

Category: Computers

Page: 542

View: 6375

Erfahren Sie alles über das Manipulieren, Bereinigen, Verarbeiten und Aufbereiten von Datensätzen mit Python: Aktualisiert auf Python 3.6, zeigt Ihnen dieses konsequent praxisbezogene Buch anhand konkreter Fallbeispiele, wie Sie eine Vielzahl von typischen Datenanalyse-Problemen effektiv lösen. Gleichzeitig lernen Sie die neuesten Versionen von pandas, NumPy, IPython und Jupyter kennen.Geschrieben von Wes McKinney, dem Begründer des pandas-Projekts, bietet Datenanalyse mit Python einen praktischen Einstieg in die Data-Science-Tools von Python. Das Buch eignet sich sowohl für Datenanalysten, für die Python Neuland ist, als auch für Python-Programmierer, die sich in Data Science und Scientific Computing einarbeiten wollen. Daten und zugehöriges Material des Buchs sind auf GitHub verfügbar.Aus dem Inhalt:Nutzen Sie die IPython-Shell und Jupyter Notebook für das explorative ComputingLernen Sie Grundfunktionen und fortgeschrittene Features von NumPy kennenSetzen Sie die Datenanalyse-Tools der pandasBibliothek einVerwenden Sie flexible Werkzeuge zum Laden, Bereinigen, Transformieren, Zusammenführen und Umformen von DatenErstellen Sie interformative Visualisierungen mit matplotlibWenden Sie die GroupBy-Mechanismen von pandas an, um Datensätzen zurechtzuschneiden, umzugestalten und zusammenzufassenAnalysieren und manipulieren Sie verschiedenste Zeitreihen-DatenFür diese aktualisierte 2. Auflage wurde der gesamte Code an Python 3.6 und die neuesten Versionen der pandas-Bibliothek angepasst. Neu in dieser Auflage: Informationen zu fortgeschrittenen pandas-Tools sowie eine kurze Einführung in statsmodels und scikit-learn.

Smart Data Analytics

Mit Hilfe von Big Data Zusammenhänge erkennen und Potentiale nutzen

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Author: Andreas Wierse,Till Riedel

Publisher: Walter de Gruyter GmbH & Co KG

ISBN: 3110461919

Category: Technology & Engineering

Page: 440

View: 3963

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.

Data Science

Third International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2017, Changsha, China, September 22–24, 2017, Proceedings

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Author: Beiji Zou,Min Li,Hongzhi Wang,Xianhua Song,Wei Xie,Zeguang Lu

Publisher: Springer

ISBN: 9811063850

Category: Computers

Page: 769

View: 8422

This two volume set (CCIS 727 and 728) constitutes the refereed proceedings of the Third International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2017 (originally ICYCSEE) held in Changsha, China, in September 2017. The 112 revised full papers presented in these two volumes were carefully reviewed and selected from 987 submissions. The papers cover a wide range of topics related to Basic Theory and Techniques for Data Science including Mathematical Issues in Data Science, Computational Theory for Data Science, Big Data Management and Applications, Data Quality and Data Preparation, Evaluation and Measurement in Data Science, Data Visualization, Big Data Mining and Knowledge Management, Infrastructure for Data Science, Machine Learning for Data Science, Data Security and Privacy, Applications of Data Science, Case Study of Data Science, Multimedia Data Management and Analysis, Data-driven Scientific Research, Data-driven Bioinformatics, Data-driven Healthcare, Data-driven Management, Data-driven eGovernment, Data-driven Smart City/Planet, Data Marketing and Economics, Social Media and Recommendation Systems, Data-driven Security, Data-driven Business Model Innovation, Social and/or organizational impacts of Data Science.

Big Data

Algorithms, Analytics, and Applications

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Author: Kuan-Ching Li,Hai Jiang,Laurence T. Yang,Alfredo Cuzzocrea

Publisher: CRC Press

ISBN: 1482240564

Category: Computers

Page: 498

View: 4954

As today’s organizations are capturing exponentially larger amounts of data than ever, now is the time for organizations to rethink how they digest that data. Through advanced algorithms and analytics techniques, organizations can harness this data, discover hidden patterns, and use the newly acquired knowledge to achieve competitive advantages. Presenting the contributions of leading experts in their respective fields, Big Data: Algorithms, Analytics, and Applications bridges the gap between the vastness of Big Data and the appropriate computational methods for scientific and social discovery. It covers fundamental issues about Big Data, including efficient algorithmic methods to process data, better analytical strategies to digest data, and representative applications in diverse fields, such as medicine, science, and engineering. The book is organized into five main sections: Big Data Management—considers the research issues related to the management of Big Data, including indexing and scalability aspects Big Data Processing—addresses the problem of processing Big Data across a wide range of resource-intensive computational settings Big Data Stream Techniques and Algorithms—explores research issues regarding the management and mining of Big Data in streaming environments Big Data Privacy—focuses on models, techniques, and algorithms for preserving Big Data privacy Big Data Applications—illustrates practical applications of Big Data across several domains, including finance, multimedia tools, biometrics, and satellite Big Data processing Overall, the book reports on state-of-the-art studies and achievements in algorithms, analytics, and applications of Big Data. It provides readers with the basis for further efforts in this challenging scientific field that will play a leading role in next-generation database, data warehousing, data mining, and cloud computing research. It also explores related applications in diverse sectors, covering technologies for media/data communication, elastic media/data storage, cross-network media/data fusion, and SaaS.

Business Analytics with Management Science Models and Methods

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Author: Arben Asllani

Publisher: FT Press

ISBN: 0133760669

Category: Business & Economics

Page: 400

View: 1961

Master decision modeling and analytics through realistic examples, intuitive explanations, and tested Excel templates. Business Analytics with Management Science has been designed to help students, practitioners and managers use business analytics to improve decision-making systems. Unlike previous books, it emphasizes the application of practical management science techniques in business analytics. Drawing on 20+ years of teaching and consulting experience, Dr. Arben Asllani introduces decision analytics through realistic examples and intuitive explanations – not complex formulae and theoretical definitions. Throughout, Asllani helps practitioners focus more on the crucial input-output aspects of decision making – and less upon internal model complexities that can usually be "delegated" to software.

Networking and Information Technology Research and Development

Supplement to the President's Budget for Fiscal Year 2006

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Author: N.A

Publisher: DIANE Publishing

ISBN: 1437904874

Category:

Page: 24

View: 4780

This annual report on the multi-agency Networking and Information Technology R&D (NITRD) Program describes activities funded by Federal NITRD agencies in the areas of advanced networking and information technologies and offers a brief technical outline of the 2006 budget request for the NITRD Program in the following major research areas: high-end computing applications and infrastructure; high-end computing R&D; large-scale networking; human-computer interaction and information management; high-confidence software and systems; software design and productivity; and social, economic, and workforce implications of IT and IT workforce development.

Statistik-Workshop für Programmierer

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Author: Allen B. Downey

Publisher: O'Reilly Germany

ISBN: 3868993436

Category: Computers

Page: 160

View: 6808

Wenn Sie programmieren können, beherrschen Sie bereits Techniken, um aus Daten Wissen zu extrahieren. Diese kompakte Einführung in die Statistik zeigt Ihnen, wie Sie rechnergestützt, anstatt auf mathematischem Weg Datenanalysen mit Python durchführen können. Praktischer Programmier-Workshop statt grauer Theorie: Das Buch führt Sie anhand eines durchgängigen Fallbeispiels durch eine vollständige Datenanalyse -- von der Datensammlung über die Berechnung statistischer Kennwerte und Identifikation von Mustern bis hin zum Testen statistischer Hypothesen. Gleichzeitig werden Sie mit statistischen Verteilungen, den Regeln der Wahrscheinlichkeitsrechnung, Visualisierungsmöglichkeiten und vielen anderen Arbeitstechniken und Konzepten vertraut gemacht. Statistik-Konzepte zum Ausprobieren: Entwickeln Sie über das Schreiben und Testen von Code ein Verständnis für die Grundlagen von Wahrscheinlichkeitsrechnung und Statistik: Überprüfen Sie das Verhalten statistischer Merkmale durch Zufallsexperimente, zum Beispiel indem Sie Stichproben aus unterschiedlichen Verteilungen ziehen. Nutzen Sie Simulationen, um Konzepte zu verstehen, die auf mathematischem Weg nur schwer zugänglich sind. Lernen Sie etwas über Themen, die in Einführungen üblicherweise nicht vermittelt werden, beispielsweise über die Bayessche Schätzung. Nutzen Sie Python zur Bereinigung und Aufbereitung von Rohdaten aus nahezu beliebigen Quellen. Beantworten Sie mit den Mitteln der Inferenzstatistik Fragestellungen zu realen Daten.

Intelligent Data Engineering and Automated Learning -- IDEAL 2013

14th International Conference, IDEAL 2013, Hefei, China, October 20-23, 2013, Proceedings

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Author: Hujun Yin,Ke Tang,Yang Gao,Frank Klawonn,Minho Lee,Bin Li,Thomas Weise,Xin Yao

Publisher: Springer

ISBN: 3642412785

Category: Computers

Page: 639

View: 2913

This book constitutes the refereed proceedings of the 14th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2013, held in Hefei, China, in October 2013. The 76 revised full papers presented were carefully reviewed and selected from more than 130 submissions. These papers provided a valuable collection of latest research outcomes in data engineering and automated learning, from methodologies, frameworks and techniques to applications. In addition to various topics such as evolutionary algorithms, neural networks, probabilistic modelling, swarm intelligent, multi-objective optimisation, and practical applications in regression, classification, clustering, biological data processing, text processing, video analysis, including a number of special sessions on emerging topics such as adaptation and learning multi-agent systems, big data, swarm intelligence and data mining, and combining learning and optimisation in intelligent data engineering.

High Performance Computing for Computational Science - VECPAR 2004

6th International Conference, Valencia, Spain, June 28-30, 2004, Revised Selected and Invited Papers

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Author: Michel Daydé,Jack Dongarra,Vincente Hernández,José M.L.M. Palma

Publisher: Springer Science & Business Media

ISBN: 9783540254249

Category: Computers

Page: 732

View: 1630

This book constitutes the thoroughly refereed post-proceedings of the 6th International Conference on High Performance Computing for Computational Science, VECPAR 2004, held in Valencia, Spain, in June 2004. The 48 revised full papers presented together with 5 invited papers were carefully selected during two rounds of reviewing and improvement from initially 130 contributions. The papers are organized in topical sections on large-scale computations, data management and data mining, GRID computing infrastructure, cluster computing, parallel and distributed computing, and computational linear and non-linear algebra.

Service Computing: Concept, Method and Technology

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Author: Zhaohui Wu

Publisher: Academic Press

ISBN: 0128025972

Category: Computers

Page: 356

View: 5931

Service computing is a cross-disciplinary field that covers science and technology, and represents a promising direction for distributed computing and software development methodologies. It aims to bridge the gap between business services and IT services by supporting the whole lifecycle of services innovation. Over the last ten years applications in industry and academic research have produced considerable progress and success Service Computing: Concept, Method and Technology presents the concept of service computing and a proposed reference architecture for service computing research before proceeding to introduce two underlying technologies: Web services and service-oriented architecture. It also presents the authors’ latest research findings on hot topics such as service discovery, recommendation, composition, verification, service trust, dynamic configuration and big data service. Some new models and methods are proposed including three service discovery methods based on semantics and skyline technologies, two service recommendation methods using graph mining and QoS prediction, two service composition methods with graph planning and one service verification method using π calculus and so on. Moreover, this book introduces JTang, an underlying platform supporting service computing, which is a product of the authors’ last ten years of research and development. Systematically reviews all the research on service computing Introduces state-of-art research works on service computing and provides a road map for future directions Bridges the gap between service computing theory and practice Provides guidance for both industry and academia

Hierarchische Matrizen

Algorithmen und Analysis

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Author: Wolfgang Hackbusch

Publisher: Springer Science & Business Media

ISBN: 3642002218

Category: Mathematics

Page: 451

View: 4485

Bei der Diskretisierung von Randwertaufgaben und Integralgleichungen entstehen große, eventuell auch voll besetzte Matrizen. In dem Band stellt der Autor eine neuartige Methode dar, die es erstmals erlaubt, solche Matrizen nicht nur effizient zu speichern, sondern auch alle Matrixoperationen einschließlich der Matrixinversion bzw. der Dreieckszerlegung approximativ durchzuführen. Anwendung findet diese Technik nicht nur bei der Lösung großer Gleichungssysteme, sondern auch bei Matrixgleichungen und der Berechnung von Matrixfunktionen.

Cognitive Computing for Big Data Systems Over IoT

Frameworks, Tools and Applications

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Author: Arun Kumar Sangaiah,Arunkumar Thangavelu,Venkatesan Meenakshi Sundaram

Publisher: Springer

ISBN: 3319706888

Category: Computers

Page: 375

View: 2584

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.