Автор: Raj & Chandra Deka Название: Cloud Infrastructures For Big Data Analytics ISBN: 1466658649 ISBN-13(EAN): 9781466658646 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 50312.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Clouds are being positioned as the next-generation consolidated, centralised, yet federated IT infrastructure for hosting all kinds of IT platforms and for deploying, maintaining, and managing a wider variety of personal, as well as professional, applications and services.Cloud Infrastructures for Big Data Analytics focuses exclusively on the topic of cloud-sponsored big data analytics for creating flexible and futuristic organisations. This book helps researchers and practitioners, as well as business entrepreneurs, to make informed decisions and consider appropriate action to simplify and streamline the arduous journey towards smarter enterprises.
Описание: Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner(R), Third Edition presents an applied approach to data mining and predictive analytics with clear exposition, hands-on exercises, and real-life case studies.
Автор: C.S.R. Prabhu; Aneesh Sreevallabh Chivukula; Adity Название: Big Data Analytics: Systems, Algorithms, Applications ISBN: 9811500932 ISBN-13(EAN): 9789811500930 Издательство: Springer Рейтинг: Цена: 9083.00 р. Наличие на складе: Поставка под заказ.
Описание:
This book provides a comprehensive survey of techniques, technologies and applications of Big Data and its analysis. The Big Data phenomenon is increasingly impacting all sectors of business and industry, producing an emerging new information ecosystem. On the applications front, the book offers detailed descriptions of various application areas for Big Data Analytics in the important domains of Social Semantic Web Mining, Banking and Financial Services, Capital Markets, Insurance, Advertisement, Recommendation Systems, Bio-Informatics, the IoT and Fog Computing, before delving into issues of security and privacy.
With regard to machine learning techniques, the book presents all the standard algorithms for learning – including supervised, semi-supervised and unsupervised techniques such as clustering and reinforcement learning techniques to perform collective Deep Learning. Multi-layered and nonlinear learning for Big Data are also covered.
In turn, the book highlights real-life case studies on successful implementations of Big Data Analytics at large IT companies such as Google, Facebook, LinkedIn and Microsoft. Multi-sectorial case studies on domain-based companies such as Deutsche Bank, the power provider Opower, Delta Airlines and a Chinese City Transportation application represent a valuable addition.
Given its comprehensive coverage of Big Data Analytics, the book offers a unique resource for undergraduate and graduate students, researchers, educators and IT professionals alike.
Автор: Thuraisingham, Bhavani Parveen, Pallabi Masud, Mohammad Mehedy Khan, Latifur Название: Big data analytics with applications in insider threat detection ISBN: 0367657422 ISBN-13(EAN): 9780367657420 Издательство: Taylor&Francis Рейтинг: Цена: 6889.00 р. Наличие на складе: Поставка под заказ.
Описание: Antivirus software uses algorithms to detect viruses Reactively adaptive malware deploys those algorithms to outwit antivirus defenses and to go undetected. This book provides details of the tools, the types of malware the tools will detect, implementation of the tools in a cloud framework, and the applications for insider threat detection.
Автор: C.S.R. Prabhu; Aneesh Sreevallabh Chivukula; Adity Название: Big Data Analytics: Systems, Algorithms, Applications ISBN: 9811500967 ISBN-13(EAN): 9789811500961 Издательство: Springer Рейтинг: Цена: 9083.00 р. Наличие на складе: Поставка под заказ.
Описание: This book provides a comprehensive survey of techniques, technologies and applications of Big Data and its analysis.
Автор: Abu-Salih Bilal, Wongthongtham Pornpit, Zhu Dengya Название: Social Big Data Analytics: Practices, Techniques, and Applications ISBN: 9813366540 ISBN-13(EAN): 9789813366541 Издательство: Springer Рейтинг: Цена: 19564.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Chapter 1: Big data technologies
Big data is no more "all just hype" but widely applied in nearly all aspects of our business, governments, and organizations with the technology stack of AI. Its influences are far beyond a simple technique innovation but involves all rears in the world. This chapter will first have historical review of big data; followed by discussion of characteristics of big data, i.e. the 3V's to up 10V's of big data. The chapter then introduces technology stacks for an organization to build a big data application, from infrastructure/platform/ecosystem to constructional units/components; following by several successful examples. Finally, we provide some big data online resources for reference.
Chapter 2: Credibility and influence in social big data
Online Social Networks (OSNs) are a fertile medium through which users can express their sentiments and share their opinions, experiences and knowledge of several topics. There is a deficiency of assessment mechanisms that incorporate domain-based trustworthiness. In OSNs, determining users' influence in a particular domain has been driven by its significance in a broad range of applications such as personalized recommendation systems, opinion analysis, expertise retrieval, to name a few. This chapter presents a comprehensive framework that aims to infer value from BSD by measuring the domain-based trustworthiness of OSN users, addressing the main features of big data, and incorporating semantic analysis and the temporal factor.
Chapter 3: Semantic data discovery from social big data
The challenge of managing and extracting useful knowledge from social media data sources has attracted much attention from academia and industry. Social big data is an important big data island; thus, social data analytics are intended to make sense of data and to obtain value from data. Social big data provides a wealth of information that businesses, political governments, organisations, etc. can mine and analyse to exploit value in a variety of areas. This chapter discusses the development of an approach that aims to semantically analyse social content, thus enriching social data with semantic conceptual representation for domain-based discovery.
Chapter 4: Predictive analytics using social big data and machine learning
Previous works in the area of topic distillation and discovery lack an appropriate and applicable technical solution that can handle the complex task of obtaining an accurate interpretation of the contextual social content. This is evident through the inadequacy of these endeavours in addressing the topics of microblogging short messages like tweets, and their inability to classify and predict the messages' actual and precise domains of interest at the user level. Hence, this chapter intends to address this problem by presenting solutions to domain-based classification and prediction of social big data at the user and tweet levels incorporating comprehensive knowledge discovery tools and well-known machine learning algorithms.
Chapter 5: Affective design in the era of big social data
In today's competitive market, product designers not only need to optimize functional qualities when developing a new product, but also they need to optimize the affective qualities of the product. The reason is that products with high affective qualities is more likely to attract more potential consumers to buy. In the past, affective design is generally conducted based on the limited amount of customer survey data which is collected from marketing questionnaires and consumer interviews. Since the data amount is limited, the affective design cannot fully reflect the current or even the recent situation of the marketplaces. Thanks to the advanced computing and web technologies, big data from social media or product reviews in w
Описание: Introduction to Seeing Cities Through Big Data - Research, Methods, and Applications in Urban Informatics.- Big Data and Urban Informatics: Innovations and Challenges to Urban Planning and Knowledge Discovery.- Analytics of user-generated content.- Using User-Generated Content to Understand Cities.- Developing an Interactive Mobile Volunteered Geographic Information Platform to Integrate Environmental Big Data and Citizen Science in Urban Management.- CyberGIS-enabled Urban Sensing from Volunteered Citizen Participation Using Mobile Devices.- Challenges and opportunities of urban Big Data.- The Potential for Big Data to Improve Neighborhood-Level Census Data.- Big Data and Survey Research: Supplement or Substitute?.- Big Spatio-temporal Network Data Analytics for Smart Cities: Research Needs.- A review of heteroscedasticity treatment with Gaussian Processes and Quantile Regression meta-models.- Changing organizational and educational perspectives with urban Big Data.- Urban Informatics: Critical Data and Technology Considerations.- Emerging Urban Digital Infomediaries and Civic Hacking in an Era of Big Data and Open Data Initiatives.- How Should Urban Planners Be Trained to Handle Big Data?.- Energy Planning in Big data Era: A Theme Study of the Residential Sector.- Urban data management.- Using an online spatial analytics workbench for understanding housing affordability in Sydney.- A Big Data Mashing Tool for Measuring Transit System Performance.- Developing a Comprehensive U.S. Transit Accessibility Database.- Seeing Chinese Cities through Big Data and Statistics.- Urban knowledge discovery applied to different urban contexts.- Planning for the Change: Mapping Sea Level Rise and Storm Inundation in Sherman Island Using 3Di Hydrodynamic Model and LiDAR.- The Impact of Land-Use Variables on Free-floating Carsharing Vehicle Rental Choice and Parking Duration.- Dynamic Agent Based Simulation of an Urban Disaster Using Synthetic Big Data.- Estimation of Urban Transport Accessibility at the Spatial Resolution of an Individual Traveler.- Modeling Taxi Demand and Supply in New York City Using Large-Scale Taxi GPS Data.- Detecting stop episodes from GPS trajectories with gaps.- Emergencies and Crisis.- Using Social Media and Satellite Data for Damage Assessment in Urban Areas During Emergencies.- Health and well-being.- 'Big Data': Pedestrian Volume Using Google Street View Images.- Learning from Outdoor Webcams: Surveillance of Physical Activity Across Environments.- Mapping Urban Soundscapes via Citygram.- Social equity and data democracy.- Big Data and Smart (Equitable) Cities.- Big Data, Small Apps: Premises and Products of the Civic Hackathon.
Описание: With the revised second edition of this hands-on guide, up-and-coming data scientists will learn how to use the Agile Data Science development methodology to build data applications with Python, Apache Spark, Kafka, and other tools.
Описание: Offers the latest, innovative architectures and frameworks and explores a variety of applications within various industries. Offering an international perspective, the applications discussed within this anthology feature global representation.
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