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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 gath
About this series The series "Studies in Big Data'(SBD)publishes new developments and advances in the various areas of Big Data-quickly and with a high quality. The intent is to cover the theory, research, development, and applications of Big Data as embedded in the fields of engineering, computer science, physics, economics and life sciences. The books of the series refer to the analysis and understanding of large, complex, and/or distributed data sets generated from recent digital sources coming from sensors or other physical instruments as well as simulations, crowd sourcing, social networks or other internet transactions, such as emails or video click streams and other. The series contains monographs, lecture notes and edited volumes in Big Data spanning the areas of computational intelligence incl. neural networks, evolutionary computation, soft computing, fuzzy systems, as well as artificial intelligence, data mining, modern statistics and Operations research, as well as self-organizing systems. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution, which enable both wide and rapid dissemination of research output Witold pedrycz. Shyi-Ming chen Editors Information granularity Big Data, and Computational Intelligence 空 Springer editors Witold pedrycz Shyi-Ming Chen Department of Electrical and Computer Department of Computer Science Engineering and Information engineerin University of Alberta National taiwan University of Science Edmonton. aB and Technology anada Taipei Taiwan ISSN2197-6503 Issn 2197-6511 (electronic ISBN978-3-319-08253-0 ISBN978-3-319-08254-7( eBook) DOI10.1007/978-3-319-08254-7 Springer Cham Heidelberg New York Dordrecht London Library of Congress Control Number: 2014944331 o Springer International Publishing Switzerland 2015 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. Exempted from this legal reservation are brief excerpts in connection with reviews or scholarly analysis or material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Duplication of this publication or parts thereof is permitted only under the provisions of the Copyright Law of the Publishers location, in its current version, and permission for use must always be obtained from Springer. Permissions for use may be obtained through RightsLink at the Copyright Clearance Center. Violations are liable to prosecution under the respective Copyright Law The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein Printed on acid-free paper SpringerispartofSpringerScience+businessMedia( Preface The recent pursuits emerging in big data processing, interpretation, collection, and organization have emerged in numerous sectors including business, industry, and not-for-profit organizations Data sets such as customer transactions for a mega- retailer, weather monitoring, intelligence gathering can quickly outpace the capacity of traditional techniques and tools of data analysis. We have been wit nessing an emergence of new techniques and tools including NoSQL databases MapReduce, Natural Language Processing, Machine Learning, visualization, acquisition, and serialization It becomes imperative to fully become aware what happens when big data grows up: how they are being applied and where they start playing a crucial role We also need to become fully become aware of implications and requirements imposed on the existing techniques and various methods under development Soft Computing regarded as a plethora of technologies of fuzzy sets (or Granular Computing, in general), 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. In particular, setting up a suitable and fully legitimate level of abstraction by forming semantically meaningful information granules is of paramount relevance. In light of their sheer volume, big data may call for distributed processing, where results of intensive data mining realized locally are afterwards reconciled leading to information granules of higher type Neurocomputing operating at information granules leads to more tractable learning tasks. Evolutionary computing delivers an essential framework supporting global optimization In light of the inherent human-centric facet of Granular Computing the prin ciples and practice of Computational Intelligence have been poised to play a vital role in the analysis, design, and interpretation of the architectures and functioning of mechanisms of big data Our ultimate objectives of this edited volume is to provide the reader with an updated, in-depth material on the emerging principles, conceptual underpinnings, algorithms, and practice of Computational Intelligence in the realization of con cepts and implementation of big data architectures, analysis, and interpretation as well as data analytics Preface An overall concise characterization of the objectives of the edited volume is expressed by highlighting several focal points Systematic exposure of the concepts, design methodology, and detailed algo rithms. 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 Individual chapters with clearly delineated agenda and well-defined focus and additional reading material available via carefully selected references A wealth of carefully structured and organized illustrative material. The volume includes a series of brief illustrative numeric experiments, detailed schemes, and more advanced problems. They make the material more readable and appealing Self-containment. Given the emerging character of the area of big data, our ultimate intent is to deliver a material that is self-contained and provides the reader with all necessary prerequisites and, if necessary, 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 sce- narios to motivate the reader and make some abstract concepts more tangible The area of big data is highly diversified and this volume offers a quite repre sentative view of the area. The contributions published here can be organized into hree main parts. The first part, Fundamentals, which comprises chapters"Nearest Neighbor Queries on Big Data?"Building Fuzzy robust Regression Model Based on granularity and Possibility Distribution "is focused on the methodological issues covering a broad spectrum of the approaches and detailed algorithmic pursuits including essential topics of forming cliques in big data, exploiting robust regression and its variants, constructing and optimizing rule-based models, Latent Semantic Indexing, information granulation, and Nearest Neighbor Querying. Part II entitled Architectures consisting of chapters "The Role of Cloud Computing Architectures in Big Datato"The Web Know ARR Framework: Orchestrating Computational Intelligence with Graph Databases" is aimed at looking at the dedicated computing architectures such as cloud computing and the use of data storage techniques Part IIi (case studies) includes chapters "Customer Relationship management and big data Mining" to"Application of Computational Intelligence on Analysis of Air Quality Monitoring Big Datawhich offer a suite of studies serving as a testimony to a wealth of promising applications including among others Customer Relationship management, market movements, weather forecasting, and air quality monitoring Given the theme of this project, this book is aimed at a broad audience of researchers and practitioners Owing to the nature of the material being covered and the way it is organized, one can project with high confidence that it will appeal to the well-established communities including those active in various disciplines il which big data, their analysis, and optimization are of genuine relevance. Those involved in data mining, data analysis, management, various branches of engi neering, and economics will benefit from the exposure to the subject matter Preface Considering a way in which the edited volume is structured this book could serve as a highly useful reference material for graduate students and senior undergraduate students in courses such as those on intelligent system, data mining, pattern recognition, decision-making, Internet engineering, Computational Intel- ligence, management, operations research, and knowledge-based systems We would like to take this opportunity to express our sincere thanks to the authors for sharing the results of their innovative research and delivering their insights into the area The reviewers deserve our thanks for their constructive and timely input. We greatly appreciate a continuous support and encouragement coming from the Editor-in-Chief, Prof Janusz Kacprzyk whose leadership and vision makes this book series a unique vehicle to disseminate the most recent highly relevant and far-reaching publications in the domain of computational Intelligence and its various applications We hope that the readers will find this volume of genuine interest and the research reported here will help foster further progress in research, education, and numerous practical endeavors Witold pedryc Shyi-Ming che Contents Parti fundamentals Nearest Neighbor Queries on Big Data Georgios Chatzimilioudis, andreas Konstantinidis and Demetrios Zeinalipour-Y azti Information Mining for Big Information 23 Yuichi goto Information Granules problem: An efficient solution of Real-Time Fuzzy Regression Al 39 Azizul Azhar ramli, Junzo Watada and witold pedrycz How to Understand Connections Based on Big data: From Cliques to Flexible granules 63 Ali jalal-Kamali.M. Shahriar Hossain and Vladik Kreinovich Graph-Based Framework for Evaluating the Feasibility of transition to maintainomics Bo Xing Incrementally Mining Frequent Patterns from Large Database Yue-Shi lee and show-Jane Yen Improved Latent Semantic Indexing- Based Data Mining Methods and an Application to Big Data Analysis of CRM 141 Jianxiong Yang and Junzo Watada The Property of Different Granule and Granular Methods Based on Quotient Space 171 Yan-ping Zhang, Ling Zhang and Chenchu Xu Towards an Optimal Task-Driven Information Granulation.... 191 Alexander kyjov Contents Unified Framework for Construction of rule based Classification Systems 209 Han liu, Alexander egov and Frederic Stahl Multi-granular Evaluation Model Through Fuzzy Random Regression to Improve Information granularity 231 Nureize Arbaiy and JunZo Watada Building Fuzzy Robust Regression Model Based on Granularity and possibility distribution ..247 Yoshiyuki Yabuuchi and Junzo watada Parti Architectures The role of Cloud Computing Architecture in Big Data .....275 Mehdi bahrami and mukesh singhal Big Data Storage Techniques for Spatial Databases: Implications of Big Data Architecture on Spatial Query Processing 297 Roger Frye and Mark McKenney The Web KnowARR Framework: Orchestrating Computational Intelligence with Graph databases 325 Edy portmann and patrick Kaltenrieder Part III Case Studies Customer Relationship Management and Big Data Mining 349 Yi Hui liang Performance Competition for IsCIFCM and dPEI Models Under Uncontrolled Circumstances 361 Jui Fang Chang Rough Set Model Based Knowledge Acquisition of Market Movements from Economic data 375 Yoshiyuki Matsumoto and Junzo watada Deep Neural Network Modeling for Big Data Weather Forecasting .. 389 Jameson,K liu y anxing hu. yulin he pak wai chan and lucas lai


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