Kuan-Ching Li - Big Data: Algorithms, Analytics, and Applications

Computer Science & Engineering / Data Mining and Knowledge DiscoveryChapman & Hall/CRCBig Data SeriesThe collection presented in the book covers fundamental and realistic issues BIG DA about Big Data, including efficient algorithmic methods to process data, bet-ter analytical strategies to digest data, and representative applications in diversefields.... This book is required understanding for anyone working in a major fieldof science, engineering, business, and financing. Jack Dongarra, University of Tennessee BIG DATA The editors have assembled an impressive book consisting of 22 chapters writ- Algorithms, Analytics, ten by 57 authors from 12 countries across America, Europe, and Asia.... Thisbook has great potential to provide fundamental insight and privacy to individu- and Applications als, long-lasting value to organizations, and security and sustainability to the cy-berphysicalsocial ecosystem .... D. Frank Hsu, Fordham University These editors are active researchers and have done a lot of work in the area ofBig Data. They assembled a group of outstanding chapter authors.... Each sec- T Edited by Kuan-Ching Li tion contains several case studies to demonstrate how the related issues areaddressed....

I highly recommend this timely and valuable book. I believe that itwill benefit many readers and contribute to the further development of Big Data A Hai JianG Laurence T. Yang Alfredo Cuzzocrea research. Dr. Yi Pan, Georgia State University Presenting the contributions of leading experts in their respective fields, BigData: Algorithms, Analytics, and Applications bridges the gap between the vastness of big data and the appropriate computational methods for scientific and social discovery. It covers fundamental issues about Big Data, including ef and Cuzzocrea Li, ficient algorithmic methods to process data, better analytical strategies to digest data, and representative applications in diverse fields such as medicine, science, JianG, Yang, and engineering. Overall, the book reports on state-of-the-art studies and achievements in algo rithms, analytics, and applications of Big Data.

It provides readers with the basis for further efforts in this challenging scientific field that will play a leading role in next-generation database, data warehousing, data mining, and cloud computing research. K23331 w w w . c r c p r e s s . c o m K23331_cover.indd 1 1/6/15 10:49 AM BIG DATA Algorithms, Analytics, and Applications Chapman & Hall/CRCBig Data SeriesSERIES EDITORSanjay RankaAIMS AND SCOPE This series aims to present new research and applications in Big Data, along with the computa tional tools and techniques currently in development. The inclusion of concrete examples and applications is highly encouraged. PUBLISHED TITLESBIG DATA : ALGORITHMS, ANALYTICS, AND APPLICATIONSKuan-Ching Li, Hai Jiang, Laurence T. PUBLISHED TITLES BIG DATA : ALGORITHMS, ANALYTICS, AND APPLICATIONS Kuan-Ching Li, Hai Jiang, Laurence T.

Yang, and Alfredo CuzzocreaChapman & Hall/CRCBig Data Series BIG DATA Algorithms, Analytics, and Applications Edited by Kuan-Ching Li Providence University Taiwan Hai Jiang Arkansas State University USA Laurence T. Yang St. Francis Xavier University Canada Alfredo Cuzzocrea ICAR -CNR & University of Calabria Italy CRC Press Taylor & Francis Group 6000 Broken Sound Parkway NW, Suite 300 Boca Raton, FL 33487-2742 2015 by Taylor & Francis Group, LLC CRC Press is an imprint of Taylor & Francis Group, an Informa business No claim to original U.S. Government works Version Date: 20141210 International Standard Book Number-13: 978-1-4822-4056-6 (eBook - PDF) This book contains information obtained from authentic and highly regarded sources. Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use. The authors and publishers have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained.

If any copyright material has not been acknowledged please write and let us know so we may rectify in any future reprint. Except as permitted under U.S. Copyright Law, no part of this book may be reprinted, reproduced, transmitted, or utilized in any form by any electronic, mechanical, or other means, now known or hereafter invented, including photocopy-ing, microfilming, and recording, or in any information storage or retrieval system, without written permission from the publishers. For permission to photocopy or use material electronically from this work, please access www.copyright.com (http:// www.copyright.com/) or contact the Copyright Clearance Center, Inc. (CCC), 222 Rosewood Drive, Danvers, MA 01923, 978-750-8400. CCC is a not-for-profit organization that provides licenses and registration for a variety of users.

For organizations that have been granted a photocopy license by the CCC, a separate system of payment has been arranged. Trademark Notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. Visit the Taylor & Francis Web site athttp://www.taylorandfrancis.comand the CRC Press Web site athttp://www.crcpress.com Contents Foreword by Jack Dongarra, ix Foreword by Dr. Yi Pan, xi Foreword by D. Frank Hsu, xiii Preface, xv Editors, xxix Contributors, xxxiii Section i Big Data Management HiSHam moHamed and StpHane marcHand-maillet cHapter 2 Scalability and Cost Evaluation of Incremental Data Processing Using Amazons Hadoop Service Xing Wu, Yan liu, and ian gorton aleXander tHomaSian cHapter 4 Multiple Sequence Alignment and Clustering with Dot Matrices, Entropy, and Genetic Algorithms JoHn tSiligaridiS Section ii Big Data Processing cHapter 5 Approaches for High-Performance Big Data Processing: Applications and Challenges ouidad acHaHbar, moHamed riduan abid, moHamed bakHouYa, cHaker el amrani, Jaafar gaber, moHammed eSSaaidi, and tarek a. el gHazaWi cHapter 6 The Art of Scheduling for Big Data Science florin pop and Valentin criStea vvi Contents cHapter 7 TimeSpace Scheduling in the MapReduce Framework zHuo tang, ling Qi, lingang Jiang, kenli li, and keQin li cHapter 8 GEMS: Graph Database Engine for Multithreaded Systems aleSSandro morari, Vito gioVanni caStellana, oreSte Villa, JeSSe WeaVer, greg WilliamS, daVid Haglin, antonino tumeo, and JoHn feo cHapter 9 KSC-net: Community Detection for Big Data Networks ragHVendra mall and JoHan a.k.

SuYkenS cHapter 10 Making Big Data Transparent to the Software Developers Community 175 Yu Wu, JeSSica kropczYnSki, and JoHn m. carroll Section iii Big Data Stream Techniques and Algorithms cHapter 11 Key Technologies for Big Data Stream Computing daWei Sun, guangYan zHang, Weimin zHeng, and keQin li cHapter 12 Streaming Algorithms for Big Data Processing on Multicore Architecture 215 marat zHanikeeV cHapter 13 Organic Streams: A Unified Framework for Personal Big Data Integration and Organization Towards Social Sharing and Individualized Sustainable Use Xiaokang zHou and Qun Jin cHapter 14 Managing Big Trajectory Data: Online Processing of Positional Streams koStaS patroumpaS and timoS SelliS Section iV Big Data Privacy cHapter 15 Personal Data Protection Aspects of Big Data paolo balboni cHapter 16 Privacy-Preserving Big Data Management: The Case of OLAP 301 alfredo cuzzocrea Contents