Structured Search for Big Data

The WWW era made billions of people dramatically dependent on the progress of data technologies, out of which Internet search and Big Data are arguably the most notable. Structured Search paradigm connects them via a fundamental concept of key-objects evolving out of keywords as the units of search. The key-object data model and KeySQL revamp the data independence principle making it applicable for Big Data and complement NoSQL with full-blown structured querying functionality. The ultimate goal is extracting Big Information from the Big Data. As a Big Data Consultant, Mikhail Gilula combines academic background with 20 years of industry experience in the database and data warehousing technologies working as a Sr. Data Architect for Teradata, Alcatel-Lucent, and PayPal, among others. He has authored three books, including The Set Model for Database and Information Systems and holds four US Patents in Structured Search and Data Integration. Conceptualizes structured search as a technology for querying multiple data sources in an independent and scalable manner. Explains how NoSQL and KeySQL complement each other and serve different needs with respect to big data Shows the place of structured search in the internet evolution and describes its implementations including the real-time structured internet search

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  • Author : Mikhail Gilula
  • Publisher : Morgan Kaufmann
  • Pages : 114 pages
  • ISBN : 012804652X
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKStructured Search for Big Data

Structured Search for Big Data

Structured Search for Big Data
  • Author : Mikhail Gilula
  • Publisher : Morgan Kaufmann
  • Release : 26 August 2015
GET THIS BOOKStructured Search for Big Data

The WWW era made billions of people dramatically dependent on the progress of data technologies, out of which Internet search and Big Data are arguably the most notable. Structured Search paradigm connects them via a fundamental concept of key-objects evolving out of keywords as the units of search. The key-object data model and KeySQL revamp the data independence principle making it applicable for Big Data and complement NoSQL with full-blown structured querying functionality. The ultimate goal is extracting Big Information

Big Data

Big Data
  • Author : Min Chen,Shiwen Mao,Yin Zhang,Victor C.M. Leung
  • Publisher : Springer
  • Release : 05 May 2014
GET THIS BOOKBig Data

This Springer Brief provides a comprehensive overview of the background and recent developments of big data. The value chain of big data is divided into four phases: data generation, data acquisition, data storage and data analysis. For each phase, the book introduces the general background, discusses technical challenges and reviews the latest advances. Technologies under discussion include cloud computing, Internet of Things, data centers, Hadoop and more. The authors also explore several representative applications of big data such as enterprise

Big Data Computing

Big Data Computing
  • Author : Rajendra Akerkar
  • Publisher : CRC Press
  • Release : 05 December 2013
GET THIS BOOKBig Data Computing

Due to market forces and technological evolution, Big Data computing is developing at an increasing rate. A wide variety of novel approaches and tools have emerged to tackle the challenges of Big Data, creating both more opportunities and more challenges for students and professionals in the field of data computation and analysis. Presenting a mix of industry cases and theory, Big Data Computing discusses the technical and practical issues related to Big Data in intelligent information management. Emphasizing the adoption

Big Data For Dummies

Big Data For Dummies
  • Author : Judith S. Hurwitz,Alan Nugent,Fern Halper,Marcia Kaufman
  • Publisher : John Wiley & Sons
  • Release : 02 April 2013
GET THIS BOOKBig Data For Dummies

Find the right big data solution for your business ororganization Big data management is one of the major challenges facingbusiness, industry, and not-for-profit organizations. Data setssuch as customer transactions for a mega-retailer, weather patternsmonitored by meteorologists, or social network activity can quicklyoutpace the capacity of traditional data management tools. If youneed to develop or manage big data solutions, you'll appreciate howthese four experts define, explain, and guide you through this newand often confusing concept. You'll learn what it is, why

Computational and Statistical Methods for Analysing Big Data with Applications

Computational and Statistical Methods for Analysing Big Data with Applications
  • Author : Shen Liu,James Mcgree,Zongyuan Ge,Yang Xie
  • Publisher : Academic Press
  • Release : 20 November 2015
GET THIS BOOKComputational and Statistical Methods for Analysing Big Data with Applications

Due to the scale and complexity of data sets currently being collected in areas such as health, transportation, environmental science, engineering, information technology, business and finance, modern quantitative analysts are seeking improved and appropriate computational and statistical methods to explore, model and draw inferences from big data. This book aims to introduce suitable approaches for such endeavours, providing applications and case studies for the purpose of demonstration. Computational and Statistical Methods for Analysing Big Data with Applications starts with an

R for Data Science

R for Data Science
  • Author : Hadley Wickham,Garrett Grolemund
  • Publisher : "O'Reilly Media, Inc."
  • Release : 12 December 2016
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"This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience"--

Big Data Analytics for Cyber-Physical Systems

Big Data Analytics for Cyber-Physical Systems
  • Author : Guido Dartmann,Houbing Song,Anke Schmeink
  • Publisher : Elsevier
  • Release : 15 July 2019
GET THIS BOOKBig Data Analytics for Cyber-Physical Systems

Big Data Analytics in Cyber-Physical Systems: Machine Learning for the Internet of Things examines sensor signal processing, IoT gateways, optimization and decision-making, intelligent mobility, and implementation of machine learning algorithms in embedded systems. This book focuses on the interaction between IoT technology and the mathematical tools used to evaluate the extracted data of those systems. Each chapter provides the reader with a broad list of data analytics and machine learning methods for multiple IoT applications. Additionally, this volume addresses the

Mobility Patterns, Big Data and Transport Analytics

Mobility Patterns, Big Data and Transport Analytics
  • Author : Constantinos Antoniou,Loukas Dimitriou,Francisco Pereira
  • Publisher : Elsevier
  • Release : 27 November 2018
GET THIS BOOKMobility Patterns, Big Data and Transport Analytics

Mobility Patterns, Big Data and Transport Analytics provides a guide to the new analytical framework and its relation to big data, focusing on capturing, predicting, visualizing and controlling mobility patterns - a key aspect of transportation modeling. The book features prominent international experts who provide overviews on new analytical frameworks, applications and concepts in mobility analysis and transportation systems. Users will find a detailed, mobility ‘structural’ analysis and a look at the extensive behavioral characteristics of transport, observability requirements and

Principles of Big Data

Principles of Big Data
  • Author : Jules J. Berman
  • Publisher : Newnes
  • Release : 20 May 2013
GET THIS BOOKPrinciples of Big Data

Principles of Big Data helps readers avoid the common mistakes that endanger all Big Data projects. By stressing simple, fundamental concepts, this book teaches readers how to organize large volumes of complex data, and how to achieve data permanence when the content of the data is constantly changing. General methods for data verification and validation, as specifically applied to Big Data resources, are stressed throughout the book. The book demonstrates how adept analysts can find relationships among data objects held

Social Networks and Questions of Big Data. Graph search for communities with corresponding keywords

Social Networks and Questions of Big Data. Graph search for communities with corresponding keywords
  • Author : Andrea Attwenger
  • Publisher : GRIN Verlag
  • Release : 27 June 2017
GET THIS BOOKSocial Networks and Questions of Big Data. Graph search for communities with corresponding keywords

Seminar paper from the year 2017 in the subject Computer Science - Internet, New Technologies, grade: 1.3, LMU Munich (Institut für Informatik), course: Recent Developments in Data Science, language: English, abstract: This essay deals with a graph search for communities with corresponding keywords. The era of big data and world-spanning social networks has highlighted the necessity of ways to make sense of this vast amount of information. Data can be arranged in a graph of connected vertices, therefore giving it a

Big Data, Analytics, and the Future of Marketing & Sales

Big Data, Analytics, and the Future of Marketing & Sales
  • Author : McKinsey Chief McKinsey Chief Marketing & Sales Officer Forum,Mckinsey Chief Marketing,Sales Officer Forum
  • Publisher : Unknown Publisher
  • Release : 16 August 2014
GET THIS BOOKBig Data, Analytics, and the Future of Marketing & Sales

Big Data is the biggest game-changing opportunity for marketing and sales since the Internet went mainstream almost 20 years ago. The data big bang has unleashed torrents of terabytes about everything from customer behaviors to weather patterns to demographic consumer shifts in emerging markets. This collection of articles, videos, interviews, and slideshares highlights the most important lessons for companies looking to turn data into above-market growth: Using analytics to identify valuable business opportunities from the data to drive decisions and improve

Big Data

Big Data
  • Author : Nasir Raheem
  • Publisher : CRC Press
  • Release : 21 February 2019
GET THIS BOOKBig Data

Big Data: A Tutorial-Based Approach explores the tools and techniques used to bring about the marriage of structured and unstructured data. It focuses on Hadoop Distributed Storage and MapReduce Processing by implementing (i) Tools and Techniques of Hadoop Eco System, (ii) Hadoop Distributed File System Infrastructure, and (iii) efficient MapReduce processing. The book includes Use Cases and Tutorials to provide an integrated approach that answers the ‘What’, ‘How’, and ‘Why’ of Big Data. Features Identifies the primary drivers of Big