000			14796cam a22003737i 4500
001			18977898
003			BD-SySUS
005			20230829163222.0
008			160216s2016 maua b 001 0 eng d
010			_a 2016933655
020			_a9780128053942 _celectronic bk
020			_a0128053941
035			_a(OCoLC)ocn947816755
040			_aBTCTA _beng _cBTCTA _erda _dYDX _dOCLCO _dGSU _dISU _dU3G _dBUF _dDLC
050	0	0	_aQA76.9.B45 _bB5565 2016
082	0	4	_a005.7 _223
245	0	0	_aBig data : _bprinciples and paradigms / _cedited by Rajkumar Buyya, The University of Melbourne and Manjrasoft Pty Ltd, Australia, Rodrigo N. Calheiros, The University of Melbourne, Australia, Amir Vahid Dastjerdi, The University of Melbourne, Australia.
264		1	_aCambridge, MA : _bElsevier/Morgan Kaufmann, _c[2016]
300			_axxv, 468 pages : _billustrations ; _c24 cm
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
504			_aIncludes bibliographical references and index.
505	0	0	_aMachine generated contents note: _gch. 1 _tBDA = ML + CC -- _g1.1. _tIntroduction -- _g1.2. _tA Historical Review of Big Data -- _g1.2.1. _tThe Origin of Big Data -- _g1.2.2. _tDebates of Big Data Implication -- _g1.3. _tHistorical Interpretation of Big Data -- _g1.3.1. _tMethodology for Defining Big Data -- _g1.3.2. _tDifferent Attributes of Definitions -- _g1.3.3. _tSummary of 7 Types Definitions of Big Data -- _g1.3.4. _tMotivations Behind the Definitions -- _g1.4. _tDefining Big Data From 3Vs to 32Vs -- _g1.4.1. _tData Domain -- _g1.4.2. _tBusiness Intelligent (BI) Domain -- _g1.4.3. _tStatistics Domain -- _g1.4.4. _t32 Vs Definition and Big Data Venn Diagram -- _g1.5. _tBig Data Analytics and Machine Learning -- _g1.5.1. _tBig Data Analytics -- _g1.5.2. _tMachine Learning -- _g1.6. _tBig Data Analytics and Cloud Computing -- _g1.7. _tHadoop, HDFS, MapReduce, Spark, and Flink -- _g1.7.1. _tGoogle File System (GFS) and HDFS -- _g1.7.2. _tMapReduce -- _g1.7.3. _tThe Origin of the Hadoop Project -- _g1.7.4. _tSpark and Spark Stack -- _g1.7.5. _tFlink and Other Data Process Engines -- _g1.7.6. _tSummary of Hadoop and Its Ecosystems -- _g1.8. _tML +CC -> BDA and Guidelines -- _g1.9. _tConclusion -- _tReferences -- _gch. 2 _tReal-lime Analytics -- _g2.1. _tIntroduction -- _g2.2. _tComputing Abstractions for Real-Time Analytics -- _g2.3. _tCharacteristics of Real-Time Systems -- _g2.3.1. _tLow Latency -- _g2.3.2. _tHigh Availability -- _g2.3.3. _tHorizontal Scalability -- _g2.4. _tReal-Time Processing for Big Data -- Concepts and Platforms -- _g2.4.1. _tEvent -- _g2.4.2. _tEvent Processing -- _g2.4.3. _tEvent Stream Processing and Data Stream Processing -- _g2.4.4. _tComplex Event Processing -- _g2.4.5. _tEvent Type -- _g2.4.6. _tEvent Pattern -- _g2.5. _tData Stream Processing Platforms -- _g2.5.1. _tSpark -- _g2.5.2. _tStorm -- _g2.5.3. _tKafka -- _g2.5.4. _tFlume -- _g2.5.5. _tAmazon Kinesis -- _g2.6. _tData Stream Analytics Platforms -- _g2.6.1. _tQuery-Based EPSs -- _g2.6.2. _tRule-Oriented EPSs -- _g2.6.3. _tProgrammatic EPSs -- _g2.7. _tData Analysis and Analytic Techniques -- _g2.7.1. _tData Analysis in General -- _g2.7.2. _tData Analysis for Stream Applications -- _g2.8. _tFinance Domain Requirements and a Case Study -- _g2.8.1. _tReal-Time Analytics in Finance Domain -- _g2.8.2. _tSelected Scenarios -- _g2.8.3. _tCEP Application as a Case Study -- _g2.9. _tFuture Research Challenges -- _tReferences -- _gch. 3 _tBig Data Analytics for Social Media -- _g3.1. _tIntroduction -- _g3.2. _tNLP and Its Applications -- _g3.2.1. _tLanguage Detection -- _g3.2.2. _tNamed Entity Recognition -- _g3.3. _tText Mining -- _g3.3.1. _tSentiment Analysis -- _g3.3.2. _tTrending Topics -- _g3.3.3. _tRecommender Systems -- _g3.4. _tAnomaly Detection -- _tAcknowledgments -- _tReferences -- _gch. 4 _tDeep Learning and Its Parallelization -- _g4.1. _tIntroduction -- _g4.1.1. _tApplication Background -- _g4.1.2. _tPerformance Demands for Deep Learning -- _g4.1.3. _tExisting Parallel Frameworks of Deep Learning -- _g4.2. _tConcepts and Categories of Deep Learning -- _g4.2.1. _tDeep Learning -- _g4.2.2. _tMainstream Deep Learning Models -- _g4.3. _tParallel Optimization for Deep Learning -- _g4.3.1. _tConvolutional Architecture for Fast Feature Embedding -- _g4.3.2. _tDistBelief -- _g4.3.3. _tDeep Learning Based on Multi-GPUs -- _g4.4. _tDiscussions -- _g4.4.1. _tGrand Challenges of Deep Learning in Big Data -- _g4.4.2. _tFuture Directions -- _tReferences -- _gch. 5 _tCharacterization and Traversal of Large Real-World Networks -- _g5.1. _tIntroduction -- _g5.2. _tBackground -- _g5.3. _tCharacterization and Measurement -- _g5.4. _tEfficient Complex Network Traversal -- _g5.4.1. _tHPC Traversal of Large Networks -- _g5.4.2. _tAlgorithms for Accelerating AS-BFS on GPU -- _g5.4.3. _tPerformance Study of AS-BFS on GPU's -- _g5.5. _tk-Core-Based Partitioning for Heterogeneous Graph Processing -- _g5.5.1. _tGraph Partitioning for Heterogeneous Computing -- _g5.5.2. _tk-Core-Based Complex-Network Unbalanced Bisection -- _g5.6. _tFuture Directions -- _g5.7. _tConclusions -- _tAcknowledgments -- _tReferences -- _gch. 6 _tDatabase Techniques for Big Data -- _g6.1. _tIntroduction -- _g6.2. _tBackground -- _g6.2.1. _tNavigational Data Models -- _g6.2.2. _tRelational Data Models -- _g6.3. _tNoSQL Movement -- _g6.4. _tNoSQL Solutions for Big Data Management -- _g6.5. _tNoSQL Data Models -- _g6.5.1. _tKey-Value Stores -- _g6.5.2. _tColumn-Based Stores -- _g6.5.3. _tGraph-Based Stores -- _g6.5.4. _tDocument-Based Stores -- _g6.6. _tFuture Directions -- _g6.7. _tConclusions -- _tReferences -- _gch. 7 _tResource Management in Big Data Processing Systems -- _g7.1. _tIntroduction -- _g7.2. _tTypes of Resource Management -- _g7.2.1. _tCPU and Memory Resource Management -- _g7.2.2. _tStorage Resource Management -- _g7.2.3. _tNetwork Resource Management -- _g7.3. _tBig Data Processing Systems and Platforms -- _g7.3.1. _tHadoop -- _g7.3.2. _tDryad -- _g7.3.3. _tPregel -- _g7.3.4. _tStorm -- _g7.3.5. _tSpark -- _g7.3.6. _tSummary -- _g7.4. _tSingle-Resource Management in the Cloud -- _g7.4.1. _tDesired Resource Allocation Properties -- _g7.4.2. _tProblems for Existing Fairness Policies -- _g7.4.3. _tLong-Term Resource Allocation Policy -- _g7.4.4. _tExperimental Evaluation -- _g7.5. _tMultiresource Management in the Cloud -- _g7.5.1. _tResource Allocation Model -- _g7.5.2. _tMultiresource Fair Sharing Issues -- _g7.5.3. _tReciprocal Resource Fairness -- _g7.5.4. _tExperimental Evaluation -- _g7.6. _tRelated Work on Resource Management -- _g7.6.1. _tResource Utilization Optimization -- _g7.6.2. _tPower and Energy Cost Saving Optimization -- _g7.6.3. _tMonetary Cost Optimization -- _g7.6.4. _tFairness Optimization -- _g7.7. _tOpen Problems -- _g7.7.1. _tSLA Guarantee for Applications -- _g7.7.2. _tVarious Computation Models and Systems -- _g7.7.3. _tExploiting Emerging Hardware -- _g7.8. _tSummary -- _tReferences -- _gch. 8 _tLocal Resource Consumption Shaping: A Case for MapReduce -- _g8.1. _tIntroduction -- _g8.2. _tMotivation -- _g8.2.1. _tPitfalls of Fair Resource Sharing -- _g8.3. _tLocal Resource Shaper -- _g8.3.1. _tDesign Philosophy -- _g8.3.2. _tSplitter -- _g8.3.3. _tThe Interleave MapReduce Scheduler -- _g8.4. _tEvaluation -- _g8.4.1. _tExperiments With Hadoop 1.x -- _g8.4.2. _tExperiments With Hadoop 2.x -- _g8.5. _tRelated Work -- _g8.6. _tConclusions -- _tAppendix CPU Utilization With Different Slot Configurations and LRS -- _tReferences -- _gch. 9 _tSystem Optimization for Big Data Processing -- _g9.1. _tIntroduction -- _g9.2. _tBasic Framework of the Hadoop Ecosystem -- _g9.3. _tParallel Computation Framework: MapReduce -- _g9.3.1. _tImprovements of MapReduce Framework -- _g9.3.2. _tOptimization for Task Scheduling and Load Balancing of MapReduce -- _g9.4. _tJob Scheduling of Hadoop -- _g9.4.1. _tBuilt-In Scheduling Algorithms of Hadoop -- _g9.4.2. _tImprovement of the Hadoop Job Scheduling Algorithm -- _g9.4.3. _tImprovement of the Hadoop Job Management Framework -- _g9.5. _tPerformance Optimization of HDFS -- _g9.5.1. _tSmall File Performance Optimization -- _g9.5.2. _tHDFS Security Optimization -- _g9.6. _tPerformance Optimization of HBase -- _g9.6.1. _tHBase Framework, Storage, and Application Optimization -- _g9.6.2. _tLoad Balancing of HBase -- _g9.6.3. _tOptimization of HBase Configuration -- _g9.7. _tPerformance Enhancement of Hadoop System -- _g9.7.1. _tEfficiency Optimization of Hadoop -- _g9.7.2. _tAvailability Optimization of Hadoop -- _g9.8. _tConclusions and Future Directions -- _tReferences -- _gch. 10 _tPacking Algorithms for Big Data Replay on Multicore -- _g10.1. _tIntroduction -- _g10.2. _tPerformance Bottlenecks -- _g10.2.1. _tHadoop/MapReduce Performance Bottlenecks -- _g10.2.2. _tPerformance Bottlenecks Under Parallel Loads -- _g10.2.3. _tParameter Spaces for Storage and Shared Memory -- _g10.2.4. _tMain Storage Performance -- _g10.2.5. _tShared Memory Performance -- _g10.3. _tThe Big Data Replay Method -- _g10.3.1. _tThe Replay Method -- _g10.3.2. _tJobs as Sketches on a Timeline -- _g10.3.3. _tPerformance Bottlenecks Under Replay -- _g10.4. _tPacking Algorithms -- _g10.4.1. _tShared Memory Performance Tricks -- _g10.4.2. _tBig Data Replay at Scale -- _g10.4.3. _tPractical Packing Models -- _g10.5. _tPerformance Analysis -- _g10.5.1. _tHotspot Distributions -- _g10.5.2. _tModeling Methodology -- _g10.5.3. _tProcessing Overhead Versus Bottlenecks -- _g10.5.4. _tControl Grain for Drop Versus Drag Models -- _g10.6. _tSummary and Future Directions -- _tReferences -- _gch. 11 _tSpatial Privacy Challenges in Social Networks -- _g11.1. _tIntroduction -- _g11.2. _tBackground -- _g11.3. _tSpatial Aspects of Social Networks -- _g11.4. _tCloud-Based Big Data Infrastructure -- _g11.5. _tSpatial Privacy Case Studies -- _g11.6. _tConclusions -- _tAcknowledgments -- _tReferences -- _gch. 12 _tSecurity and Privacy in Big Data -- _g12.1. _tIntroduction -- _g12.2. _tSecure Queries Over Encrypted Big Data -- _g12.2.1. _tSystem Model -- _g12.2.2. _tThreat Model and Attack Model -- _g12.2.3. _tSecure Query Scheme in Clouds -- _g12.2.4. _tSecurity Definition of Index-Based Secure Query Techniques -- _g12.2.5. _tImplementations of Index-Based Secure Query Techniques -- _g12.3. _tOther Big Data Security -- _g12.3.1. _tDigital Watermarking -- _g12.3.2. _tSelf-Adaptive Risk Access Control -- _g12.4. _tPrivacy on Correlated Big Data -- _g12.4.1. _tCorrelated Data in Big Data -- _g12.4.2. _tAnonymity -- _g12.4.3. _tDifferential Privacy -- _g12.5. _tFuture Directions -- _g12.6. _tConclusions -- _tReferences -- _gch. 13 _tLocation Inferring in Internet of Things and Big Data -- _g13.1. _tIntroduction -- _g13.2. _tDevice-Based Sensing Using Big Data -- _g13.2.1. _tIntroduction -- _g13.2.2. _tApproach Overview -- _g13.2.3. _tTrajectories Matching -- _g13.2.4. _tEstablishing the Mapping Between Floor Plan and RSS Readings -- _g13.2.5. _tUser Localization -- _g13.2.6. _tGraph Matching Based Tracking -- _g13.2.7. _tEvaluation -- _g13.3. _tDevice-Free Sensing Using Big Data -- _g13.3.1. _tCustomer Behavior Identification -- _g13.3.2. _tHuman Object Estimation
505	0	0	_aNote continued: _g13.4. _tConclusion -- _tAcknowledgements -- _tReferences -- _gch. 14 _tA Framework for Mining Thai Public Opinions -- _g14.1. _tIntroduction -- _g14.2. _tXDOM -- _g14.2.1. _tData Sources -- _g14.2.2. _tDOM System Architecture -- _g14.2.3. _tMapReduce Framework -- _g14.2.4. _tSentiment Analysis -- _g14.2.5. _tClustering-Based Summarization Framework -- _g14.2.6. _tInfluencer Analysis -- _g14.2.7. _tAsKDOM: Mobile Application -- _g14.3. _tImplementation -- _g14.3.1. _tServer -- _g14.3.2. _tCore Service -- _g14.3.3. _tI/O -- _g14.4. _tValidation -- _g14.4.1. _tValidation Parameter -- _g14.4.2. _tValidation method -- _g14.4.3. _tValidation results -- _g14.5. _tCase Studies -- _g14.5.1. _tPolitical Opinion: #prayforthailand -- _g14.5.2. _tBangkok Traffic Congestion Ranking -- _g14.6. _tSummary and Conclusions -- _tAcknowledgments -- _tReferences -- _gch. 15 _tA Case Study in Big Data Analytics: Exploring Twitter Sentiment Analysis and the Weather -- _g15.1. _tBackground -- _g15.2. _tBig Data System Components -- _g15.2.1. _tSystem Back-End Architecture -- _g15.2.2. _tSystem Front-End Architecture -- _g15.2.3. _tSoftware Stack -- _g15.3. _tMachine-Learning Methodology -- _g15.3.1. _tTweets Sentiment Analysis -- _g15.3.2. _tWeather and Emotion Correlation Analysis -- _g15.4. _tSystem Implementation -- _g15.4.1. _tHome Page -- _g15.4.2. _tSentiment Pages -- _g15.4.3. _tWeather Pages -- _g15.5. _tKey Findings -- _g15.5.1. _tTime Series -- _g15.5.2. _tAnalysis with Hourly Weather Data -- _g15.5.3. _tAnalysis with Daily Weather Data -- _g15.5.4. _tDBSCAN Cluster Algorithm -- _g15.5.5. _tStraightforward Weather Impact on Emotion -- _g15.6. _tSummary and Conclusions -- _tAcknowledgments -- _tReferences -- _gch. 16 _tDynamic Uncertainty-Based Analytics for Caching Performance Improvements in Mobile Broadband Wireless Networks -- _g16.1. _tIntroduction -- _g16.1.1. _tBig Data Concerns -- _g16.1.2. _tKey Focus Areas -- _g16.2. _tBackground -- _g16.2.1. _tCellular Network and VoD -- _g16.2.2. _tMarkov Processes -- _g16.3. _tRelated Work -- _g16.4. _tVoD Architecture -- _g16.5. _tOverview -- _g16.6. _tData Generation -- _g16.7. _tEdge and Core Components -- _g16.8. _tINCA Caching Algorithm -- _g16.9. _tQoE Estimation -- _g16.10. _tTheoretical Framework -- _g16.11. _tExperiments and Results -- _g16.11.1. _tCache Hits With Nu, Nc, Nm and k -- _g16.11.2. _tQoE Impact With Prefetch Bandwidth -- _g16.11.3. _tUser Satisfaction With Prefetch Bandwidth -- _g16.12. _tSynthetic Dataset -- _g16.12.1. _tINCA Hit Gain -- _g16.12.2. _tQoE Performance -- _g16.12.3. _tSatisfied Users -- _g16.13. _tConclusions and Future Directions -- _tReferences -- _gch. 17 _tBig Data Analytics on a Smart Grid: Mining PMU Data for Event and Anomaly Detection -- _g17.1. _tIntroduction -- _g17.2. _tSmart Grid With PMUs and PDCs -- _g17.3. _tImproving Traditional Workflow -- _g17.4. _tCharacterizing Normal Operation -- _g17.5. _tIdentifying Unusual Phenomena -- _g17.6. _tIdentifying Known Events -- _g17.7. _tRelated Efforts -- _g17.8. _tConclusion and Future Directions -- _tAcknowledgments -- _tReferences -- _gch. 18 _teScience and Big Data Workflows in Clouds: A Taxonomy and Survey -- _g18.1. _tIntroduction -- _g18.2. _tBackground -- _g18.2.1. _tHistory -- _g18.2.2. _tGrid-Based eScience -- _g18.2.3. _tCloud Computing -- _g18.3. _tTaxonomy and Review of eScience Services in the Cloud -- _g18.3.1. _tInfrastructure -- _g18.3.2. _tOwnership -- _g18.3.3. _tApplication -- _g18.3.4. _tProcessing Tools -- _g18.3.5. _tStorage -- _g18.3.6. _tSecurity -- _g18.3.7. _tService Models -- _g18.3.8. _tCollaboration -- _g18.4. _tResource Provisioning for eScience Workflows in Clouds -- _g18.4.1. _tMotivation -- _g18.4.2. _tOur Solution -- _g18.5. _tOpen Problems -- _g18.6. _tSummary -- _tReferences.
650		0	_aBig data. _963678
650		1	_aBig data. _963679
650		7	_aBig data. _2fast _0(OCoLC)fst01892965 _963680
700	1		_aBuyya, Rajkumar, _d1970- _eeditor. _963681
700	1		_aCalheiros, Rodrigo N., _eeditor. _963682
700	1		_aDastjerdi, Amir Vahid, _eeditor. _963683
856			_3ELSEVIER _uhttps://www.sciencedirect.com/book/9780128053942/big-data
942			_2ddc _cEBK
999			_c85023 _d85023