Information Granularity, Big Data, and Computational Intelligence

The recent pursuits emerging in the realm of big data processing, interpretation, collection and organization have emerged in numerous sectors including business, industry and government organizations. Data sets such as customer transactions for a mega-retailer, weather monitoring, intelligence gathering, quickly outpace the capacities of traditional techniques and tools of data analysis. The 3V (volume, variability and velocity) challenges led to the emergence of new techniques and tools in data visualization, acquisition, and serialization. Soft Computing being regarded as a plethora of technologies of fuzzy sets (or Granular Computing), neurocomputing and evolutionary optimization brings forward a number of unique features that might be instrumental to the development of concepts and algorithms to deal with big data.

This carefully edited volume provides the reader with an updated, in-depth material on the emerging principles, conceptual underpinnings, algorithms and practice of Computational Intelligence in the realization of concepts and implementation of big data architectures, analysis, and interpretation as well as data analytics. The book is aimed at a broad audience of researchers and practitioners including those active in various disciplines in which big data, their analysis and optimization are of genuine relevance. One focal point is the systematic exposure of the concepts, design methodology, and detailed algorithms. In general, the volume adheres to the top-down strategy starting with the concepts and motivation and then proceeding with the detailed design that materializes in specific algorithms and representative applications. The material is self-contained and provides the reader with all necessary prerequisites and augments some parts with a step-by-step explanation of more advanced concepts supported by a significant amount of illustrative numeric material and some application scenarios to motivate the reader and make some abstract concepts more tangible.

Table of Contents

Chapter 1 Nearest Neighbor Queries on Big Data
Chapter 2 Information Mining for Big Information
Chapter 3 Information Granules Problem: An Efficient Solution of Real-Time Fuzzy Regression Analysis
Chapter 4 How to Understand Connections Based on Big Data: From Cliques to Flexible Granules
Chapter 5 Graph-Based Framework for Evaluating the Feasibility of Transition to Maintainomics
Chapter 6 Incrementally Mining Frequent Patterns from Large Database
Chapter 7 Improved Latent Semantic Indexing-Based Data Mining Methods and an Application to Big Data Analysis of CRM
Chapter 8 The Property of Different Granule and Granular Methods Based on Quotient Space
Chapter 9 Towards an Optimal Task-Driven Information Granulation
Chapter 10 Unified Framework for Construction of Rule Based Classification Systems
Chapter 11 Multi-granular Evaluation Model Through Fuzzy Random Regression to Improve Information Granularity
Chapter 12 Building Fuzzy Robust Regression Model Based on Granularity and Possibility Distribution
Chapter 13 The Role of Cloud Computing Architecture in Big Data
Chapter 14 Big Data Storage Techniques for Spatial Databases: Implications of Big Data Architecture on Spatial Query Processing
Chapter 15 The Web KnowARR Framework: Orchestrating Computational Intelligence with Graph Databases
Chapter 16 Customer Relationship Management and Big Data Mining
Chapter 17 Performance Competition for ISCIFCM and DPEI Models Under Uncontrolled Circumstances
Chapter 18 Rough Set Model Based Knowledge Acquisition of Market Movements from Economic Data
Chapter 19 Deep Neural Network Modeling for Big Data Weather Forecasting
Chapter 20 Current Knowledge and Future Challenge for Visibility Forecasting by Computational Intelligence
Chapter 21 Application of Computational Intelligence on Analysis of Air Quality Monitoring Big Data