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Big data: principles and paradigms

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Format:	Buch
Sprache:	English
Veröffentlicht:	Amsterdam ; Boston ; Heidelberg ; London ; New York ; Oxford ; Paris ; San Diego ; San Francisco ; Singapore ; Sydney ; Tokyo Morgan Kaufmann [2016]
Schlagworte:	Big Data
Online-Zugang:	Inhaltsverzeichnis
Beschreibung:	xxv, 468 Seiten Illustrationen
ISBN:	9780128053942

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adam_text	Contents List of Contributors.......................................................................xv About the Editors.........................................................................xix Preface...................................................................................xxi Acknowledgments...........................................................................xxv PARTI BIS DATA SCIENCE_____________________________________________________________________ CHAPTER 1 BDA=ML · CC.......................................................................3 1.1 Introduction................................................................3 1.2 A Historical Review of Big Data............................................4 1.2.1 The Origin of Big Data...............................................4 1.2.2 Debates of Big Data Implication......................................5 1.3 Historical Interpretation of Big Data......................................7 1.3.1 Methodology for Defining Big Data.....................................7 1.3.2 Different Attributes of Definitions...................................7 1.3.3 Summary of 7 Types Definitions of Big Data...........................10 1.3.4 Motivations Behind the Definitions...................................10 1.4 Defining Big Data From 3Vs to 32Vs.........................................11 1.4.1 Data Domain..........................................................11 1.4.2 Business Intelligent (BI) Domain.....................................11 1.4.3 Statistics Domain....................................................13 1.4.4 32 Vs Definition and Big Data Venn Diagram...........................13 1.5 Big Data Analytics and Machine Learning....................................14 1.5.1 Big Data Analytics...................................................14 1.5.2 Machine Learning.....................................................15 1.6 Big Data Analytics and Cloud Computing.....................................18 1.7 Hadoop, HDFS, MapReduce, Spark, and Flink..................................18 1.7.1 Google File System (GFS) and HDFS....................................20 1.7.2 MapReduce............................................................24 1.7.3 The Origin of the Hadoop Project.....................................25 1.7.4 Spark and Spark Stack................................................27 1.7.5 Flink and Other Data Process Engines.................................27 1.7.6 Summary of Hadoop and Its Ecosystems.................................32 1.8 ML 4-CC BDA and Guidelines.................................................34 1.9 Conclusion.................................................................35 References.................................................................35 V vi Contents CHAPTER 2 Real-Time Analytics 39 2.1 Introduction.................................................................39 2.2 Computing Abstractions for Real-Time Analytics..............................40 2.3 Characteristics of Real-Time Systems........................................41 2.3.1 Low Latency............................................................42 2.3.2 High Availability......................................................42 2.3.3 Horizontal Scalability.................................................43 2.4 Real-Time Processing for Big Data — Concepts and Platforms..................43 2.4.1 Event................................................................ 43 2.4.2 Event Processing.......................................................44 2.4.3 Event Stream Processing and Data Stream Processing.....................44 2.4.4 Complex Event Processing...............................................44 2.4.5 Event Type.............................................................45 2.4.6 Event Pattern..........................................................45 2.5 Data Stream Processing Platforms............................................45 2.5.1 Spark..................................................................46 2.5.2 Storm................................................................ 47 2.5.3 Kafka..................................................................47 2.5.4 Flume..................................................................48 2.5.5 Amazon Kinesis.........................................................48 2.6 Data Stream Analytics Platforms.............................................48 2.6.1 Query-Based EPSs.......................................................48 2.6.2 Rule-Oriented EPSs.....................................................49 2.6.3 Programmatic EPSs......................................................50 2.7 Data Analysis and Analytic Techniques.......................................53 2.7.1 Data Analysis in General...............................................53 2.7.2 Data Analysis for Stream Applications..................................53 2.8 Finance Domain Requirements and a Case Study................................54 2.8.1 Real-Time Analytics in Finance Domain..................................54 2.8.2 Selected Scenarios.....................................................55 2.8.3 CEP Application as a Case Study........................................55 2.9 Future Research Challenges..................................................58 References...................................................................59 CHAPTER 3 Big Data Analytics for Social Media............................................... 63 3.1 Introduction.................................................................63 3.2 NLP and Its Applications....................................................63 3.2.1 Language Detection.....................................................64 3.2.2 Named Entity Recognition...............................................68 3.3 Text Mining.................................................................72 Contents vil 3.3.1 Sentiment Analysis..................................................72 3.3.2 Trending Topics.....................................................77 3.3.3 Recommender Systems................................................ 81 3.4 Anomaly Detection........................................................85 Acknowledgments...........................................................88 References................................................................89 CHAPTER 4 Deep Learning and Us Parallelization......................................95 4.1 Introduction..............................................................95 4.1.1 Application Background..............................................95 4.1.2 Performance Demands for Deep Learning...............................96 4.1.3 Existing Parallel Frameworks of Deep Learning.......................96 4.2 Concepts and Categories of Deep Learning.................................96 4.2.1 Deep Learning.......................................................96 4.2.2 Mainstream Deep Learning Models.....................................99 4.3 Parallel Optimization for Deep Learning.................................104 4.3.1 Convolutional Architecture for Fast Feature Embedding...........................................................104 4.3.2 DistBelief.........................................................Ill 4.3.3 Deep Learning Based on Multi-GPUs..................................112 4.4 Discussions.............................................................115 4.4.1 Grand Challenges of Deep Learning in Big Data......................115 4.4.2 Future Directions..................................................116 References...............................................................117 CHAPTER 5 Characterization and Traversal of Large Real-World Networks.................................................................119 5.1 Introduction.............................................................119 5.2 Background..............................................................120 5.3 Characterization and Measurement........................................121 5.4 Efficient Complex Network Traversal.....................................124 5.4.1 HPC Traversal of Large Networks....................................124 5.4.2 Algorithms for Accelerating AS-BFS on GPU..........................125 5.4.3 Performance Study of AS-BFS on GPU’s...............................126 5.5 ¿-Core-Based Partitioning for Heterogeneous Graph Processing............128 5.5.1 Graph Partitioning for Heterogeneous Computing.....................129 5.5.2 ¿-Core-Based Complex-Network Unbalanced Bisection..................129 5.6 Future Directions.......................................................133 5.7 Conclusions..,..........................................................133 Acknowledgments..........................................................134 References...............................................................134 viii Contents PART II BIG DATA INFRASTRUCTURES AND PLATFORMS________________________________________ CHAPTER 6 Database Techniques for Big Data.........................................139 6.1 Introduction........................................................139 6.2 Background..........................................................139 6.2.1 Navigational Data Models.......................................139 6.2.2 Relational Data Models.........................................140 6.3 NoSQL Movement.................................................... 143 6.4 NoSQL Solutions for Big Data Management.............................144 6.5 NoSQL Data Models...................................................150 6.5.1 Key-Value Stores...............................................150 6.5.2 Column-Based Stores............................................151 6.5.3 Graph-Based Stores.............................................153 6.5.4 Document-Based Stores..........................................154 6.6 Future Directions...................................................156 6.7 Conclusions.........................................................157 References...........................................................157 CHAPTER 7 Resource Management in Big Data Processing Systems.......................i6i 7.1 Introduction........................................................161 7.2 Types of Resource Management........................................162 7.2.1 CPU and Memory Resource Management.............................162 7.2.2 Storage Resource Management....................................163 7.2.3 Network Resource Management.................................. 163 7.3 Big Data Processing Systems and Platforms...........................163 7.3.1 Hadoop.........................................................163 7.3.2 Dryad..........................................................164 7.3.3 Pregel.........................................................164 7.3.4 Storm..........................................................164 7.3.5 Spark..........................................................165 7.3.6 Summary........................................................165 7.4 Single-Resource Management in the Cloud.............................166 7.4.1 Desired Resource Allocation Properties.........................166 7.4.2 Problems for Existing Fairness Policies........................167 7.4.3 Long-Term Resource Allocation Policy...........................168 7.4.4 Experimental Evaluation........................................170 7.5 Multiresource Management in the Cloud...............................171 7.5.1 Resource Allocation Model......................................172 7.5.2 Multiresource Fair Sharing Issues..............................174 7.5.3 Reciprocal Resource Fairness...................................175 7.5.4 Experimental Evaluation........................................179 Contents ix 7.6 Related Work on Resource Management....................................182 7.6.1 Resource Utilization Optimization..................................182 7.6.2 Power and Energy Cost Saving Optimization..........................182 7.6.3 Monetary Cost Optimization.........................................182 7.6.4 Fairness Optimization..............................................183 7.7 Open Problems...........................................................183 7.7.1 SLA Guarantee for Applications.....................................183 7.7.2 Various Computation Models and Systems.............................183 7.7.3 Exploiting Emerging Hardware.......................................184 7.8 Summary.................................................................184 References...............................................................184 CHAPTER 8 Local Resource Consumption Shaping: A Case for MapReduce.....................189 8.1 Introduction............................................................189 8.2 Motivation..............................................................191 8.2.1 Pitfalls of Fair Resource Sharing.................................192 8.3 Local Resource Shaper...................................................194 8.3.1 Design Philosophy..................................................194 8.3.2 Splitter...........................................................195 8.3.3 The Interleave MapReduce Scheduler.................................195 8.4 Evaluation..............................................................198 8.4.1 Experiments With Hadoop Lx.........................................198 8.4.2 Experiments With Hadoop 2.x........................................204 8.5 Related Work............................................................210 8.6 Conclusions.............................................................211 Appendix CPU Utilization With Different Slot Configurations and LRS......212 References...............................................................213 CHAPTER 9 System Optimization for Big Data Processing...................................215 9.1 Introduction.............................................................215 9.2 Basic Framework of the Hadoop Ecosystem.................................217 9.3 Parallel Computation Framework: MapReduce...............................218 9.3.1 Improvements of MapReduce Framework................................218 9.3.2 Optimization for Task Scheduling and Load Balancing of MapReduce..219 9.4 Job Scheduling of Hadoop................................................220 9.4.1 Built-In Scheduling Algorithms of Hadoop...........................220 9.4.2 Improvement of the Hadoop Job Scheduling Algorithm.................221 9.4.3 Improvement of the Hadoop Job Management Framework.................223 9.5 Performance Optimization of HDFS........................................224 9.5.1 Small File Performance Optimization................................224 9.5.2 HDFS Security Optimization.........................................226 x Contents 9.6 Performance Optimization of HBase.....................................228 9.6.1 HBase Framework, Storage, and Application Optimization...........228 9.6.2 Load Balancing of HBase..........................................229 9.6.3 Optimization of HBase Configuration..............................230 9.7 Performance Enhancement of Hadoop System...............................230 9.7.1 Efficiency Optimization of Hadoop.................................231 9.7.2 Availability Optimization of Hadoop...............................232 9.8 Conclusions and Future Directions.................................. 233 References..............................................................233 CHAPTER 10 Packing Algorithms for Big Data Replay on Multicore............................................................239 10.1 Introduction............................................................239 10.2 Performance B ottlenecks...............................................241 10.2.1 Hadoop/MapReduce Performance Bottlenecks.........................241 10.2.2 Performance Bottlenecks Under Parallel Loads.....................243 10.2.3 Parameter Spaces for Storage and Shared Memory..................244 10.2.4 Main Storage Performance.........................................245 10.2.5 Shared Memory Performance........................................248 10.3 The Big Data Replay Method......................................... 250 10.3.1 The Replay Method................................................250 10.3.2 Jobs as Sketches on a Timeline...................................251 10.3.3 Performance Bottlenecks Under Replay.............................252 10.4 Packing Algorithms.....................................................253 10.4.1 Shared Memory Performance Tricks.................................253 10.4.2 Big Data Replay at Scale.........................................255 10.4.3 Practical Packing Models.........................................256 10.5 Performance Analysis...................................................256 10.5.1 Hotspot Distributions............................................256 10.5.2 Modeling Methodology.............................................258 10.5.3 Processing Overhead Versus Bottlenecks...........................259 10.5.4 Control Grain for Drop Versus Drag Models.......................261 10.6 Summary and Future Directions........................................ 262 References..............................................................264 PART III BIG DATA SECURITY AND PRIVACY____________________________________________________ CHAPTER 11 Spatial Privacy Challenges in Social Networks...............................269 11.1 Introduction............................................................269 11.2 Background.............................................................269 11.3 Spatial Aspects of Social Networks.....................................271 Contents xi 11.4 Cloud-Based Big Data Infrastructure.......................................273 11.5 Spatial Privacy Case Studies..............................................275 11.6 Conclusions...............................................................281 Acknowledgments............................................................282 References.................................................................282 CHAPTER 12 Security and Privacy in Big Data................................................285 12.1 Introduction...............................................................285 12.2 Secure Queries Over Encrypted Big Data.....................................287 12.2.1 System Model........................................................287 12.2.2 Threat Model and Attack Model.......................................288 12.2.3 Secure Query Scheme in Clouds.......................................289 12.2.4 Security Definition of Index-Based Secure Query Techniques..........291 12.2.5 Implementations of Index-Based Secure Query Techniques..............291 12.3 Other Big Data Security....................................................295 12.3.1 Digital Watermarking................................................295 12.3.2 Self-Adaptive Risk Access Control...................................296 12.4 Privacy on Correlated Big Data.............................................296 12.4.1 Correlated Data in Big Data.........................................296 12.4.2 Anonymity...........................................................298 12.4.3 Differential Privacy................................................300 12.5 Future Directions..........................................................304 12.6 Conclusions................................................................305 References.................................................................305 CHAPTER 13 Location Inferring in Internet of Things and Big Data...........................309 13.1 Introduction...............................................................309 13.2 Device-Based Sensing Using Big Data........................................310 13.2.1 Introduction........................................................310 13.2.2 Approach Overview...................................................310 13.2.3 Trajectories Matching...............................................311 13.2.4 Establishing the Mapping Between Floor Plan and RSS Readings........314 13.2.5 User Localization...................................................318 13.2.6 Graph Matching Based Tracking.......................................318 13.2.7 Evaluation..........................................................318 13.3 Device-Free Sensing Using Big Data.........................................319 13.3.1 Customer Behavior Identification....................................319 13.3.2 Human Object Estimation.............................................328 13.4 Conclusion.................................................................334 Acknowledgements...........................................................334 References.................................................................334 xii Contents PART IV BIG DATA APPLICATIONS______________________________________________________________ CHAPTER 14 A Framework for Mining Thai Public Opinions.............................339 14.1 Introduction............................................................339 14.2 XDOM....................................................................340 14.2.1 Data Sources.....................................................340 14.2.2 DOM System Architecture..........................................341 14.2.3 MapReduce Framework..............................................342 14.2.4 Sentiment Analysis...............................................343 14.2.5 Clustering-Based Summarization Framework.........................344 14.2.6 Influencer Analysis..............................................349 14.2.7 AskDOM: Mobile Application.......................................350 14.3 Implementation..........................................................350 14.3.1 Server...........................................................350 14.3.2 Core Service.....................................................351 14.3.3 I/O.......................................................... 351 14.4 Validation......................................................... .352 14.4.1 Validation Parameter.............................................352 14.4.2 Validation method................................................352 14.4.3 Validation results...............................................352 14.5 Case Studies............................................................353 14.5.1 Political Opinion: #prayforthailand..............................353 14.5.2 Bangkok Traffic Congestion Ranking...............................353 14.6 Summary and Conclusions.................................................354 Acknowledgments.........................................................354 References............................................................ 355 CHAPTER 15 A Case Study in Big Data Analytics: Exploring Twitter Sentiment Analysis and the Weather......................................357 15.1 Background..............................................................357 15.2 Big Data System Components..............................................358 15.2.1 System Back-End Architecture.....................................358 15.2.2 System Front-End Architecture................................ 359 15.2.3 Software Stack...................................................360 15.3 Machine-Learning Methodology............................................360 15.3.1 Tweets Sentiment Analysis........................................361 15.3.2 Weather and Emotion Correlation Analysis.........................371 15.4 System Implementation...................................................373 15.4.1 Home Page........................................................373 15.4.2 Sentiment Pages..................................................374 15.4.3 Weather Pages....................................................374 Contents xiii 15.5 Key Findings.............................................................378 15.5.1 Time Series.......................................................378 15.5.2 Analysis with Hourly Weather Data.................................378 15.5.3 Analysis with Daily Weather Data..................................380 15.5.4 DBSCAN Cluster Algorithm..........................................382 15.5.5 Straightforward Weather Impact on Emotion.........................383 15.6 Summary and Conclusions..................................................384 Acknowledgments..........................................................387 References...............................................................387 CHAPTER 16 Dynamic Uncertainty-Based Analytics for Caching Performance Improvements in Mobile Broadband Wireless Networks.......................389 16.1 Introduction.............................................................389 16.1.1 Big Data Concerns.................................................391 16.1.2 Key Focus Areas...................................................391 16.2 Background...............................................................392 16.2.1 Cellular Network and VoD..........................................392 16.2.2 Markov Processes..................................................393 16.3 Related Work.............................................................395 16.4 VoD Architecture.........................................................396 16.5 Overview.................................................................398 16.6 Data Generation..........................................................399 16.7 Edge and Core Components.................................................400 16.8 INCA Caching Algorithm...................................................401 16.9 QoE Estimation...........................................................403 16.10 Theoretical Framework....................................................403 16.11 Experiments and Results..................................................404 16.11.1 Cache Hits With Nu? Nc, NM and k.................................405 16.11.2 QoE Impact With Prefetch Bandwidth...............................407 16.11.3 User Satisfaction With Prefetch Bandwidth........................409 16.12 Synthetic Dataset........................................................409 16.12.1 INCA Hit Gain....................................................410 16.12.2 QoE Performance..................................................410 16.12.3 Satisfied Users..................................................412 16.13 Conclusions and Future Directions........................................413 References...............................................................414 CHAPTER 17 Big Data Analytics on a Smart Grid: Mining PMU Data for Event and Anomaly Detection..............................................417 17.1 Introduction.............................................................417 17.2 Smart Grid With PMUs and PDCs............................................418 xiv Contents 17.3 Improving Traditional Workflow..........................................418 17.4 Characterizing Normal Operation.........................................419 17.5 Identifying Unusual Phenomena...........................................420 17.6 Identifying Known Events................................................423 17.7 Related Efforts.........................................................426 17.8 Conclusion and Future Directions........................................427 Acknowledgments.........................................................428 References..............................................................428 CHAPTER 18 eScience and Big Data Workflows in Clouds: A Taxonomy and Survey...................................................431 18.1 Introduction............................................................431 18.2 Background............................................................ 432 18.2.1 History........................................................ 432 18.2.2 Grid-Based eScience..............................................434 18.2.3 Cloud Computing..................................................435 18.3 Taxonomy and Review of eScience Services in the Cloud...................436 18.3.1 Infrastructure...................................................437 18.3.2 Ownership........................................................437 18.3.3 Application......................................................438 18.3.4 Processing Tools............................................... 439 18.3.5 Storage..........................................................439 18.3.6 Security.........................................................440 18.3.7 Service Models...................................................441 18.3.8 Collaboration....................................................441 18.4 Resource Provisioning for eScience Workflows in Clouds..................442 18.4.1 Motivation.......................................................442 18.4.2 Our Solution.....................................................445 18.5 Open Problems...........................................................451 18.6 Summary.................................................................452 References..............................................................452 Index 457
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spelling	Big data principles and paradigms edited 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) Amsterdam ; Boston ; Heidelberg ; London ; New York ; Oxford ; Paris ; San Diego ; San Francisco ; Singapore ; Sydney ; Tokyo Morgan Kaufmann [2016] xxv, 468 Seiten Illustrationen txt rdacontent n rdamedia nc rdacarrier Big Data (DE-588)4802620-7 gnd rswk-swf Big Data (DE-588)4802620-7 s DE-604 Buyya, Rajkumar 1970- Sonstige (DE-588)122453905 oth Calheiros, Rodrigo N. Sonstige oth Dastjerdi, Amir Vahid Sonstige oth Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029058596&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis
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title_auth	Big data principles and paradigms
title_exact_search	Big data principles and paradigms
title_full	Big data principles and paradigms edited 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)
title_fullStr	Big data principles and paradigms edited 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)
title_full_unstemmed	Big data principles and paradigms edited 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)
title_short	Big data
title_sort	big data principles and paradigms
title_sub	principles and paradigms
topic	Big Data (DE-588)4802620-7 gnd
topic_facet	Big Data
url	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029058596&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
work_keys_str_mv	AT buyyarajkumar bigdataprinciplesandparadigms AT calheirosrodrigon bigdataprinciplesandparadigms AT dastjerdiamirvahid bigdataprinciplesandparadigms

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MARC

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