Data in all domains is getting bigger. How can you work with it efficiently? Recently updated for Spark 1.3, 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. This edition includes new information on Spark SQL, Spark Streaming, setup, and Maven coordinates. Written by the developers of Spark, this book will have data scientists and engineers up and running in no time. You’ll learn how to express parallel jobs with just a few lines of code, and cover applications from simple batch jobs to stream processing and machine learning. Quickly dive into Spark capabilities such as distributed datasets, in-memory caching, and the interactive shell Leverage Spark’s powerful built-in libraries, including Spark SQL, Spark Streaming, and MLlib Use one programming paradigm instead of mixing and matching tools like Hive, Hadoop, Mahout, and Storm Learn how to deploy interactive, batch, and streaming applications Connect to data sources including HDFS, Hive, JSON, and S3 Master advanced topics like data partitioning and shared variables
Over insightful 90 recipes to get lightning-fast analytics with Apache Spark About This Book Use Apache Spark for data processing with these hands-on recipes Implement end-to-end, large-scale data analysis better than ever before Work with powerful libraries such as MLLib, SciPy, NumPy, and Pandas to gain insights from your data Who This Book Is For This book is for novice and intermediate level data science professionals and data analysts who want to solve data science problems with a distributed computing framework. Basic experience with data science implementation tasks is expected. Data science professionals looking to skill up and gain an edge in the field will find this book helpful. What You Will Learn Explore the topics of data mining, text mining, Natural Language Processing, information retrieval, and machine learning. Solve real-world analytical problems with large data sets. Address data science challenges with analytical tools on a distributed system like Spark (apt for iterative algorithms), which offers in-memory processing and more flexibility for data analysis at scale. Get hands-on experience with algorithms like Classification, regression, and recommendation on real datasets using Spark MLLib package. Learn about numerical and scientific computing using NumPy and SciPy on Spark. Use Predictive Model Markup Language (PMML) in Spark for statistical data mining models. In Detail Spark has emerged as the most promising big data analytics engine for data science professionals. The true power and value of Apache Spark lies in its ability to execute data science tasks with speed and accuracy. Spark's selling point is that it combines ETL, batch analytics, real-time stream analysis, machine learning, graph processing, and visualizations. It lets you tackle the complexities that come with raw unstructured data sets with ease. This guide will get you comfortable and confident performing data science tasks with Spark. You will learn about implementations including distributed deep learning, numerical computing, and scalable machine learning. You will be shown effective solutions to problematic concepts in data science using Spark's data science libraries such as MLLib, Pandas, NumPy, SciPy, and more. These simple and efficient recipes will show you how to implement algorithms and optimize your work. Style and approach This book contains a comprehensive range of recipes designed to help you learn the fundamentals and tackle the difficulties of data science. This book outlines practical steps to produce powerful insights into Big Data through a recipe-based approach.
The book presents high quality papers presented at the International Conference on Computational Intelligence in Data Mining (ICCIDM 2016) organized by School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar, Odisha, India during December 10 – 11, 2016. The book disseminates the knowledge about innovative, active research directions in the field of data mining, machine and computational intelligence, along with current issues and applications of related topics. The volume aims to explicate and address the difficulties and challenges that of seamless integration of the two core disciplines of computer science.
Release on 2017-07-18 | by Ying Tan,Hideyuki Takagi,Yuhui Shi
Second International Conference, DMBD 2017, Fukuoka, Japan, July 27 – August 1, 2017, Proceedings
Author: Ying Tan,Hideyuki Takagi,Yuhui Shi
This book constitutes the refereed proceedings of the Second International Conference on Data Mining and Big Data, DMBD 2017, held in Fukuoka, Japan, in July/August 2017. The 53 papers presented in this volume were carefully reviewed and selected from 96 submissions. They were organized in topical sections named: association analysis; clustering; prediction; classification; schedule and sequence analysis; big data; data analysis; data mining; text mining; deep learning; high performance computing; knowledge base and its framework; and fuzzy control.
Release on 2018-07-12 | by Bhimasankaram Pochiraju,Sridhar Seshadri
An Introduction to the Methodology and its Applications
Author: Bhimasankaram Pochiraju,Sridhar Seshadri
Category: Business & Economics
This comprehensive volume is the first of its kind which serves as a textbook for long duration business analytics courses and as a perfect guide to the practitioner. This is an edited volume. The chapters are written by experts from the Indian School of Business, top US universities and industry. Every chapter has a business orientation. Typically, each chapter begins with business problems that are transformed into data questions; methodology is developed to solve the data question. Data analysis is done using widely used software, the output and results are clearly explained at each stage of development. Finally, the solution is transformed into a business solution. The editors have taken extreme care to ensure continuity across the chapters. The book has three parts: A) Methodology, B) Applications and C) Case Studies. In part A, the methodology is developed in detail. In part B, these methodologies are applied to solve business problems in various verticals. Part C contains case studies illustrating applications in a single instance. There is an appendix that develops the pre-requisites for the main text.
Advances in Biometric Systems, Soft Computing, Image Processing, and Data Analytics
Author: Chiranji Lal Chowdhary
Pubpsher: CRC Press
Category: Business & Economics
This volume helps to fill the gap between data analytics, image processing, and soft computing practices. Soft computing methods are used to focus on data analytics and image processing to develop good intelligent systems. To this end, readers of this volume will find quality research that presents the current trends, advanced methods, and hybridized techniques relating to data analytics and intelligent systems. The book also features case studies related to medical diagnosis with the use of image processing and soft computing algorithms in particular models. Providing extensive coverage of biometric systems, soft computing, image processing, artificial intelligence, and data analytics, the chapter authors discuss the latest research issues, present solutions to research problems, and look at comparative analysis with earlier results. Topics include some of the most important challenges and discoveries in intelligent systems today, such as computer vision concepts and image identification, data analysis and computational paradigms, deep learning techniques, face and speaker recognition systems, and more.
Volume 1 - Advanced Intelligent Systems for Education and Intelligent Learning System
Author: Mostafa Ezziyyani
Pubpsher: Springer Nature
Category: Technology & Engineering
This book contains the latest researches on advanced intelligent systems applied in the field of education presented during the second edition of the International Conference on Advanced Intelligent Systems for Sustainable Development (AI2SD’2019) held on July 08–11, 2019, in Marrakech, Morocco. The book proposes new approaches and innovative strategies for the manipulation of data and big data collected from the educational environment, exploiting the analysis tools, algorithms of artificial intelligence, and machine learning techniques, in order to extract results, which allow improving the performance and effectiveness of the education field, which is a strategic lever for sustainable development. The book deals with concepts, strategies, and approaches developed on various current axes of scientific research in the field of education, such as smart e-learning, smart education (smart classroom, smart assessment and smart teaching and learning technologies), massive open online courses (MOOC), courseware design, and development for smart learning, cloud learning, and mobile learning. The book is intended for all actors in the educational sector, namely students, professors, academic researchers, and stakeholders. It constitutes a large-scale forum for the exchange of ideas, approaches, and innovative techniques between these actors on the development and innovation of the field of education with the revolution 4.0. The authors of each chapter report the state of the art of the various topics addressed and present results of their own research, laboratory experiments, and successful applications. The purpose of this session is to share the idea of advanced intelligent systems with appropriate tools and techniques for modeling, management, and decision support in the field of education.
Covers XGBoost, LightGBM, Spark NLP, Distributed Deep Learning with Keras, and More
Author: Butch Quinto
Access real-world documentation and examples for the Spark platform for building large-scale, enterprise-grade machine learning applications. The past decade has seen an astonishing series of advances in machine learning. These breakthroughs are disrupting our everyday life and making an impact across every industry. Next-Generation Machine Learning with Spark provides a gentle introduction to Spark and Spark MLlib and advances to more powerful, third-party machine learning algorithms and libraries beyond what is available in the standard Spark MLlib library. By the end of this book, you will be able to apply your knowledge to real-world use cases through dozens of practical examples and insightful explanations. What You Will Learn Be introduced to machine learning, Spark, and Spark MLlib 2.4.x Achieve lightning-fast gradient boosting on Spark with the XGBoost4J-Spark and LightGBM libraries Detect anomalies with the Isolation Forest algorithm for Spark Use the Spark NLP and Stanford CoreNLP libraries that support multiple languages Optimize your ML workload with the Alluxio in-memory data accelerator for Spark Use GraphX and GraphFrames for Graph Analysis Perform image recognition using convolutional neural networks Utilize the Keras framework and distributed deep learning libraries with Spark Who This Book Is For Data scientists and machine learning engineers who want to take their knowledge to the next level and use Spark and more powerful, next-generation algorithms and libraries beyond what is available in the standard Spark MLlib library; also serves as a primer for aspiring data scientists and engineers who need an introduction to machine learning, Spark, and Spark MLlib.