• Title Knowledge Graphs and Big Data Processing
• Author(s) Valentina Janev, Damien Graux, Hajira Jabeen, Emanuel Sallinger
• Publisher: Springer; 1st ed. (July 16, 2020); eBook (Creative Commons Licensed)
• License(s): CC BY 4.0
• Hardcover/Paperback 224 pages
• eBook PDF (212 pages) and ePub
• Language: English
• ASIN: N/A
• ISBN-10: 3030531988
• ISBN-13: 978-3030531980
• Share This:  

Book Description

Data Analytics involves applying algorithmic processes to derive insights. Nowadays it is used in many industries to allow organizations and companies to make better decisions as well as to verify or disprove existing theories or models. The term data analytics is often used interchangeably with intelligence, statistics, reasoning, data mining, knowledge discovery, and others.

The goal of this book is to introduce some of the definitions, methods, tools, frameworks, and solutions for big data processing, starting from the process of information extraction and knowledge representation, via knowledge processing and analytics to visualization, sense-making, and practical applications.

Each chapter in this book addresses some pertinent aspect of the data processing chain, with a specific focus on understanding Enterprise Knowledge Graphs, Semantic Big Data Architectures, and Smart Data Analytics solutions.

• N/A

• Amazon

• Big Data
• Data Analysis and Data Mining
• Graph Theory
• Data Science

• Knowledge Graphs and Big Data Processing (Valentina Janev, et al)
• The Mirror Site (1) - PDF

• Engineering Agile Big-Data Systems (Kevin Feeney, et al)

  This book outlines an approach to dealing with problems in software and data engineering, describing a methodology for aligning these processes throughout product lifecycles. It discusses tools which can be used to achieve these goals.

• Algorithms for Big Data (Hannah Bast, et al)

  This open access book surveys the progress in addressing selected challenges related to the growth of big data in combination with increasingly complicated hardware. Tackles problems such as transportation systems, energy supply, medicine.

• Big Data in Context: Legal, Social and Technological Insights

  This book sheds new light on a selection of big data scenarios from an interdisciplinary perspective. it provides a comprehensive overview of and introduction to the emerging challenges regarding big data.

• Modelling and Simulation for Big Data Applications

  Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations.

• Designing Event-Driven Systems (Ben Stopford)

  Concepts and Patterns for Streaming Services with Apache Kafka: this book explains how service-based architectures and stream processing tools such as Apache Kafka can help you build business-critical systems.

• Making Sense of Stream Processing: Behind Apache Kafka

  This book shows you how stream processing can make your data storage and processing systems more flexible and less complex. It explains how these projects can help you reorient your database architecture around streams and materialized views.

• Big Data Processing with Apache Spark (Srini Penchikala)

  Learn about the Apache Spark framework and develop Spark programs for use cases in big-data analysis. It covers all the libraries that are part of Spark ecosystem, which includes Spark Core, Spark SQL, Spark Streaming, Spark MLlib, and Spark GraphX.

• The Internals of Apache Spark (Jacek Laskowski)

  This book introduces Apache Spark, the open source cluster computing system that makes data analytics fast to write and fast to run. With Spark, you can tackle big datasets quickly through simple APIs in Python, Java, and Scala.

• The Data Engineer's Guide to Apache Spark (Databricks)

  This book is for data engineers looking to leverage the immense growth of Apache Spark to build faster and more reliable data pipelines. It leverages Spark's amazing speed, scalability, simplicity, and versatility to build practical Big Data solutions.

• BIG CPU, BIG DATA: Solving the World's Toughest Problems

  The goal of this book is to teach you how to write parallel programs that take full advantage of the vast processing power of modern multicore computers, compute clusters, and graphics processing unit (GPU) accelerators.

• The Big Data Agenda: Data Ethics and Critical Data Studies

  This book highlights that the capacity for gathering, analysing, and utilising vast amounts of digital (user) data raises significant ethical issues. Specific case studies explore how big data have been used in academic work.

• Artificial Intelligence for Big Data (Anand Deshpande, et al)

  You will learn to use machine learning algorithms such as k-means, SVM, RBF, and regression to perform advanced data analysis. You will understand the current status of machine and deep learning techniques to work on genetic and neuro-fuzzy algorithms.

• Big Data Analytics with Hadoop 3 (Sridhar Alla)

  This book shows you how to combine Hadoop with a host of other big data tools to build powerful analytics solutions, by providing insights into the software as well as its benefits with the help of practical examples.

• Understanding Big Data: Analytics for Hadoop and Streaming Data

  In this book, the three defining characteristics of Big Data - volume, variety, and velocity, are discussed. Industry use cases are also included in this practical guide, to deliver a robust, secure, highly available, enterprise-class Big Data platform.