Mode of access: World Wide Web.

System requirements: Adobe Acrobat Reader.

Part of: Synthesis digital library of engineering and computer science.

Includes bibliographical references (pages 79-111).

1. Introduction -- 1.1 Analytics: a definition -- 1.2 Analytics at your service -- 1.3 Classification of analytics applications -- 1.3.1 The Watson DeepQA system -- 1.3.2 Functional flow of analytics applications -- 1.4 Intended audience --

2. Overview of analytics exemplars -- 2.1 Exemplar models -- 2.2 Regression analysis -- 2.3 Clustering -- 2.4 Nearest neighbor search -- 2.5 Association rule mining -- 2.6 Recommender systems -- 2.7 Support vector machines -- 2.8 Neural networks -- 2.9 Decision tree learning -- 2.10 Time series processing -- 2.11 Text analytics -- 2.12 Monte Carlo methods -- 2.13 Mathematical programming -- 2.14 On-line analytical processing -- 2.15 Graph analytics --

3. Accelerating analytics -- 3.1 Characterizing analytics exemplars -- 3.1.1 Computational patterns -- 3.1.2 Runtime characteristics -- 3.2 Implications on acceleration -- 3.2.1 System acceleration opportunities --

4. Accelerating analytics in practice: case studies -- 4.1 Text analytics -- 4.2 Deep learning -- 4.3 Computational finance -- 4.4 OLAP/business intelligence -- 4.5 Graph analytics --

5. Architectural desiderata for analytics -- 5.1 Accelerators for analytics workloads -- 5.2 Bringing it all together: building an analytics system --

A. Examples of industrial sectors and associated analytical solutions -- Bibliography -- Authors' biographies.

Abstract freely available; full-text restricted to subscribers or individual document purchasers.

Compendex

INSPEC

Google scholar

Google book search

This book aims to achieve the following goals: (1) to provide a high-level survey of key analytics models and algorithms without going into mathematical details; (2) to analyze the usage patterns of these models; and (3) to discuss opportunities for accelerating analytics workloads using software, hardware, and system approaches. The book first describes 14 key analytics models (exemplars) that span data mining, machine learning, and data management domains. For each analytics exemplar, we summarize its computational and runtime patterns and apply the information to evaluate parallelization and acceleration alternatives for that exemplar. Using case studies from important application domains such as deep learning, text analytics, and business intelligence (BI), we demonstrate how various software and hardware acceleration strategies are implemented in practice. This book is intended for both experienced professionals and students who are interested in understanding core algorithms behind analytics workloads. It is designed to serve as a guide for addressing various open problems in accelerating analytics workloads, e.g., new architectural features for supporting analytics workloads, impact on programming models and runtime systems, and designing analytics systems.

Also available in print.

Title from PDF title page (viewed on November 24, 2015).