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501
Big data : concepts, technology and architecture
Published 2021Table of Contents: “…143</p> <p>5.2.3HDFS Architecture. 143</p> <p>5.2.4HDFS Read/Write Operation. 146</p> <p>5.2.5Rack Awareness. 148</p> <p>5.2.6Features of HDFS. 149</p> <p>5.2.6.1Cost-effective. 149</p> <p>5.2.6.2Distributed storage. 149</p> <p>5.2.6.3Data Replication. 149</p> <p>5.3 Hadoop Computation. 149</p> <p>5.3.1MapReduce. 149</p> <p>5.3.1.1Mapper. 151</p> <p>5.3.1.2Combiner. 151</p> <p>5.3.1.3 Reducer. 152</p> <p>5.3.1.4 JobTracker and TaskTracker. 153</p> <p>5.3.2 MapReduce Input Formats. 154</p> <p>5.3.3 MapReduce Example. 156</p> <p>5.3.4 MapReduce Processing. 157</p> <p>5.3.5 MapReduce Algorithm.. 160</p> <p>5.3.6 Limitations of MapReduce. 161</p> <p>5.4Hadoop 2.0. 161</p> <p>5.4.1Hadoop 1.0 limitations. 162</p> <p>5.4.2 Features of Hadoop 2.0. 163</p> <p>5.4.3 Yet Another Resource Negotiator (YARN). 164</p> <p>5.4.3 Core components of YARN.. 165</p> <p>5.4.3.1 ResourceManager. 165</p> <p>5.4.3.2 NodeManager. 166</p> <p>5.4.4 YARN Scheduler. 169</p> <p>5.4.4.1 <i>FIFO scheduler</i>. 169</p> <p>5.4.4.2 <i>Capacity Scheduler</i>. 170</p> <p>5.4.4.3 <i>Fair Scheduler</i>. 170</p> <p>5.4.5 Failures in YARN.. 171</p> <p>5.4.5.1ResourceManager failure. 171</p> <p>5.4.5.2 ApplicationMaster failure. 172</p> <p>5.4.5.3 NodeManagerFailure. 172</p> <p>5.4.5.4 Container Failure. 172</p> <p>5.3 HBASE. 173</p> <p>5.4 Apache Cassandra. 176</p> <p>5.5 SQOOP. 177</p> <p>5.6 Flume. 179</p> <p>5.6.1 Flume Architecture. 179</p> <p>5.6.1.1 Event. 180</p> <p>5.6.1.2 Agent. 180</p> <p>5.7 Apache Avro. 181</p> <p>5.8 Apache Pig. 182</p> <p>5.9 Apache Mahout. 183</p> <p>5.10 Apache Oozie. 183</p> <p>5.10.1 Oozie Workflow.. 184</p> <p>5.10.2 Oozie Coordinators. 186</p> <p>5.10.3 Oozie Bundles. 187</p> <p>5.11 Apache Hive. 187</p> <p>5.11 Apache Hive. 187</p> <p>Hive Architecture. 189</p> <p>Hadoop Distributions. 190</p> <p>Chapter 5refresher. 191</p> <p>Conceptual short questions with answers. 194</p> <p>Frequently asked Interview Questions. 199</p> <p>Chapter Objective. 200</p> <p>6.1 Terminologies of Big Data Analytics. 201</p> <p><i>Data Warehouse</i>. 201</p> <p><i>Business Intelligence</i>. 201</p> <p><i>Analytics</i>. 202</p> <p>6.2 Big Data Analytics. 202</p> <p>6.2.1 Descriptive Analytics. 204</p> <p>6.2.2 Diagnostic Analytics. 205</p> <p>6.2.3 Predictive Analytics. 205</p> <p>6.2.4 Prescriptive Analytics. 205</p> <p>6.3 Data Analytics Lifecycle. 207</p> <p>6.3.1 Business case evaluation and Identify the source data. 208</p> <p>6.3.2 Data preparation. 209</p> <p>6.3.3 Data Extraction and Transformation. 210</p> <p>6.3.4 Data Analysis and visualization. 211</p> <p>6.3.5 Analytics application. 212</p> <p>6.4 Big Data Analytics Techniques. 212</p> <p>6.4.1 Quantitative Analysis. 212</p> <p>6.4.3 Statistical analysis. 214</p> <p>6.4.3.1 A/B testing. 214</p> <p>6.4.3.2 Correlation. 215</p> <p>6.4.3.3 Regression. 218</p> <p>6.5 Semantic Analysis. 220</p> <p>6.5.1 Natural Language Processing. 220</p> <p>6.5.2 Text Analytics. 221</p> <p>6.7 Big Data Business Intelligence. 222</p> <p>6.7.1 Online Transaction Processing (OLTP). 223</p> <p>6.7.2 Online Analytical Processing (OLAP). 223</p> <p>6.7.3 Real-Time Analytics Platform (RTAP). 224</p> <p>6.6Big Data Real Time Analytics Processing. 225</p> <p>6.7 Enterprise Data Warehouse. 227</p> <p>Chapter 6 Refresher. 228</p> <p>Concept…”
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502
Visual Inspection Technology in the Hard Disc Drive Industry.
Published 2015Table of Contents: “…Introduction / Suchart Yammen / Paisarn Muneesawang -- 1.2. Algorithm for corrosion detection / Suchart Yammen / Paisarn Muneesawang -- 1.2.1. …”
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503
Public safety networks from LTE to 5G
Published 2020Table of Contents: “…9.2.5 Flexibility 164 -- 9.3 Starting Public Safety Implementation Versus Waiting for 5G 165 -- 9.4 5GVersus 4G Public Safety Services 166 -- 9.4.1 Video Surveillance 167 -- 9.4.2 Computer-Driven Augmented Reality (AR) Helmet 167 -- 9.5 How 5GWill Shape Emergency Services 167 -- 9.6 4G LTE Defined Public Safety Content in 5G 168 -- 9.7 The Linkage Between 4G-5G Evolution and the Spectrum for Public Safety 168 -- 9.8 Conclusion 168 -- References 168 -- 10 Fifth Generation (5G) Cellular Technology 171 -- 10.1 Introduction 171 -- 10.2 Background Information on Cellular Network Generations 172 -- 10.2.1 Evolution of Mobile Technologies 172 -- 10.2.1.1 First Generation (1G) 172 -- 10.2.1.2 Second Generation (2G) Mobile Network 172 -- 10.2.1.3 Third Generation (3G) Mobile Network 172 -- 10.2.1.4 Fourth Generation (4G) Mobile Network 173 -- 10.2.1.5 Fifth Generation (5G) 173 -- 10.3 Fifth Generation (5G) and the Network of Tomorrow 174 -- 10.3.1 5G Network Architecture 176 -- 10.3.2 Wireless Communication Technologies for 5G 177 -- 10.3.2.1 Massive MIMO 177 -- 10.3.2.2 Spatial Modulation 179 -- 10.3.2.3 Machine to Machine Communication (M2M) 179 -- 10.3.2.4 Visible Light Communication (VLC) 180 -- 10.3.2.5 Green Communications 180 -- 10.3.3 5G System Environment 180 -- 10.3.4 Devices Used in 5G Technology 181 -- 10.3.5 Market Standardization and Adoption of 5G Technology 181 -- 10.3.6 Security Standardization of Cloud Applications 183 -- 10.3.7 The Global ICT Standardization Forum for India (GISFI) 184 -- 10.3.8 Energy Efficiency Enhancements 184 -- 10.3.9 Virtualization in the 5G Cellular Network 185 -- 10.3.10 Key Issues in the Development Process 185 -- 10.3.10.1 Challenges of Heterogeneous Networks 186 -- 10.3.10.2 Challenges Caused by Massive MIMO Technology 186 -- 10.3.10.3 Big Data Problem 186 -- 10.3.10.4 Shared Spectrum 186 -- 10.4 Conclusion 187 -- References 187 -- 11 Issues and Challenges of 4G and 5G for PS 189 -- 11.1 Introduction 189 -- 11.2 4G and 5GWireless Connections 190.…”
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504
Fundamentals of Fluid Power Control.
Published 2009Table of Contents: “…Control-Volume Flow Continuity -- PRV Flow -- Force Balance at the Spindle -- 5.13.3 Frequency Response from a Linearized Transfer Function Analysis -- 5.14 Servovalve Dynamics -- First-Stage, Armature, and Flapper-Nozzle -- Flapper-Nozzle and Resistance Bridge Flow Characteristic -- Force Balance at the Spool -- 5.15 An Open-Loop Servovalve-Motor Drive with Line Dynamics Modeled by Lumped Approximations -- Servovalve, Dynamics Included, Underlapped Spool -- Lines, Laminar Mean Flow, Two Lump Approximations per Line, Negligible Motor Internal Volume -- Motor Flow and Torque Equations -- 5.16 Transmission Line Dynamics -- 5.16.1 Introduction -- Servovalve-Cylinder with Short Lines and Significant Actuator Volumes -- Servovalve-Motor with Long Lines and Negligible Actuator Volumes -- 5.16.2 Lossless Line Model for Z and Y -- 5.16.3 Average and Distributed Line Friction Models for Z and Y -- 5.16.4 Frequency-Domain Analysis -- 5.16.5 Servovalve-Reflected Linearized Coefficients -- 5.16.6 Modeling Systems with Nonlossless Transmission Lines, the Modal Analysis Method -- 5.16.7 Modal Analysis Applied to a Servovalve-Motor Open-Loop Drive -- 5.17 The State-Space Method for Linear Systems Modeling -- 5.17.1 Modeling Principles -- 5.17.2 Some Further Aspects of the Time-Domain Solution -- 5.17.3 The Transfer Function Concept in State Space -- 5.18 Data-Based Dynamic Modeling -- 5.18.1 Introduction -- 5.18.2 Time-Series Modeling -- 5.18.3 The Group Method of Data Handling (GMDH) Algorithm -- 5.18.4 Artificial Neural Networks -- 5.18.5 A Comparison of Time-Series, GMDH, and ANN Modeling of a Second-Order Dynamic System -- 5.18.6 Time-Series Modeling of a Position Control System -- 5.18.7 Time-Series Modeling for Fault Diagnosis -- 5.18.8 Time-Series Modeling of a Proportional PRV -- 5.18.9 GMDH Modeling of a Nitrogen-Filled Accumulator.…”
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505
Behind the screen : content moderation in the shadows of social media
Published 2019Full text (MFA users only)
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Pink 2.0 : encoding queer cinema on the internet
Published 2016Full text (MFA users only)
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Engineering autonomous vehicles and robots : the DragonFly modular-based approach
Published 2020Full text (MFA users only)
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A practical guide to fetal echocardiography : normal and abnormal hearts
Published 2022Full text (MFA users only)
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