[其他] Pocket Data Mining [推广有奖]

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[size=16.000000pt]Mohamed Medhat Gaber [size=16.000000pt]· [size=16.000000pt]Frederic StahlJoão Bártolo Gomes
[size=28.000000pt]Pocket Data Mining[size=18.000000pt]Big Data on Small Devices
[size=18.000000pt]

[size=10.000000pt]1 Introduction[size=10.000000pt]................................................... 1
- [size=10.000000pt]1.1 IntroductiontoMobileDataMining........................... 1
- [size=10.000000pt]1.2 PocketDataMining:AnOverview ............................ 4
- [size=10.000000pt]1.3 MonographStructure ....................................... 5
[size=10.000000pt]2 Background [size=10.000000pt]................................................... 7
- [size=10.000000pt]2.1 DataMiningonMobileDevices .............................. 72.1.1 MobileInterface..................................... 72.1.2 On-boardExecution.................................. 8
- [size=10.000000pt]2.2 DataMiningofStreamingData............................... 9
- [size=10.000000pt]2.3 ParallelandDistributedDataMining .......................... 10
  - [size=10.000000pt]2.3.1 ParallelDataMining ................................. 11
  - [size=10.000000pt]2.3.2 DistributedDataMining .............................. 14
  - [size=10.000000pt]2.3.3 The Mobile Cloud: Pocket Data Mining in the ContextofParallelandDistributedDataMining ................. 17
- [size=10.000000pt]2.4 MoblieAgentTechnologies.................................. 20

[size=10.000000pt]3 PocketDataMiningFramework[size=10.000000pt]................................. 23

[size=10.000000pt]3.1 Introduction............................................... 23
[size=10.000000pt]3.2 PDMArchitecture.......................................... 23
[size=10.000000pt]3.2.1 MobileAgentMiners................................. 243.2.2 MobileAgentResourceDiscovers...................... 243.2.3 MobileAgentDecisionMakers ........................ 243.2.4 PDMWorkflow...................................... 25
[size=10.000000pt]3.3 PDMImplementation ....................................... 27
[size=10.000000pt]3.4 Case Studies of Distributed Classification for Pocket Data
[size=10.000000pt]Mining ................................................... 283.4.1 ExperimentalSetup .................................. 293.4.2 CaseStudyofPDMUsingHoeffdingTrees .............. 31

[size=9.000000pt]VIII
[size=10.000000pt]4

[size=10.000000pt]3.4.33.4.4

[size=9.000000pt]Contents
[size=10.000000pt]CaseStudyofPDMUsingNaiveBayes ................. 33Case Study of PDM Using a Mixture of Hoeffding TreesandNaiveBayes..................................... 37

[size=10.000000pt]3.5 ConcludingRemarks........................................ 40
[size=10.000000pt]ImplementationofPocketDataMining [size=10.000000pt]........................... 41

[size=10.000000pt]4.1 Choice of Software Packages and Components in PDM. . . . . . . . . . . 414.1.1 TheMobileOperatingSystem ......................... 414.1.2 TheMobile-AgentPlatform ........................... 424.1.3 DataMiningLibraries ................................ 43
[size=10.000000pt]4.2 Background of Software Components and ProgrammingLanguageUsedinPDM ..................................... 43
- [size=10.000000pt]4.2.1 TheJavaProgrammingLanguage ...................... 43
- [size=10.000000pt]4.2.2 TheAndroidOperatingSystem ........................ 45
- [size=10.000000pt]4.2.3 Foundation for Intelligent Physical Agents (FIPA) . . . . . . . . . 46
- [size=10.000000pt]4.2.4 Java Agent Development Environment (JADE) . . . . . . . . . . . 47
- [size=10.000000pt]4.2.5 The MOA Data Stream Mining Software and Library . . . . . . 49
- [size=10.000000pt]4.2.6 TheWEKADataMiningSoftwareandLibrary........... 49
[size=10.000000pt]4.3 Java Migration of Required Software and Libraries to Android . . . . . 49
[size=10.000000pt]4.4 ThePDMImplementationintheMobileEnvironment............ 514.4.1 SomeImplementationDetails.......................... 514.4.2 WirelessNetworksforPDM........................... 524.4.3 EstablishingthePDMNetwork ........................ 53
[size=10.000000pt]4.5 Using PDM for Classification - A Walk through Example . . . . . . . . . 544.5.1 StartingPDM ....................................... 544.5.2 StartingPDMAgents................................. 554.5.3 RetrieveResultsfromPDMAgents..................... 58
[size=10.000000pt]4.6 LimitationsoftheCurrentPDMImplementation ................ 59
[size=10.000000pt]4.7 WhatIsNext? ............................................. 59

[size=10.000000pt]5 Context-AwarePDM([size=10.000000pt]Coll-Stream[size=10.000000pt])[size=10.000000pt]............................... 61

[size=10.000000pt]5.1 Motivation ................................................ 61
[size=10.000000pt]5.2 ProblemDefinition ......................................... 62
[size=10.000000pt]5.3 CollaborativeDataStreamMining ............................ 63
[size=10.000000pt]5.4 Augmenting PDM with Dynamic Classifier Selection . . . . . . . . . . . . 63
[size=10.000000pt]5.4.1 CreatingRegions .................................... 65
[size=10.000000pt]5.4.2 Variations .......................................... 67
[size=10.000000pt]5.5 Discussion ................................................ 68

[size=10.000000pt]6 ExperimentalValidationofContext-AwarePDM [size=10.000000pt].................. 696.1 Datasets .................................................. 696.1.1 STAGGER.......................................... 696.1.2 SEA ............................................... 706.1.3 Web ............................................... 706.1.4 Reuters............................................. 70

[size=9.000000pt]Contents

[size=9.000000pt]IX

[size=10.000000pt]6.2 ExperimentalSetup......................................... 71
[size=10.000000pt]6.3 AccuracyEvaluationof[size=10.000000pt]Coll-Stream[size=10.000000pt]........................... 72
[size=10.000000pt]6.4 ImpactofRegionGranularityontheAccuracy .................. 75
[size=10.000000pt]6.5 ImpactofNoiseontheAccuracy ............................. 77
[size=10.000000pt]6.6 EffectofConceptSimilarityintheEnsemble ................... 77
[size=10.000000pt]6.7 ImpactofFeatureSelectionontheAccuracy.................... 78
[size=10.000000pt]6.8 ImpactofFeatureSelectiononMemoryConsumption............ 79
[size=10.000000pt]6.9 Discussion ................................................ 79

[size=10.000000pt]7 PotentialApplicationsofPocketDataMining [size=10.000000pt]..................... 817.1 PDM as A Decision Support System for Stock Market Brokers . . . . 817.1.1 MobiMine.......................................... 817.1.2 How PDM Could Address Shortcominings of MobiMine . . . 82

[size=10.000000pt]7.2 ApplicationofPocketDataMiningintheHealthSector .......... 83
[size=10.000000pt]7.3 PDM as a Data Fusion System for Decision Support
inPublicSafety ............................................ 87
- [size=10.000000pt]7.3.1 PDM as Data Fusion System for Decision Support . . . . . . . . 88
- [size=10.000000pt]7.3.2 DisasterManagementandRelief ....................... 89
- [size=10.000000pt]7.3.3 ApplicationsinRiotManagement ...................... 91
- [size=10.000000pt]7.3.4 ApplicationsinPolicing .............................. 92
- [size=10.000000pt]7.3.5 ApplicationsinDefence .............................. 92
[size=10.000000pt]7.4 Discussion ................................................ 93

[size=10.000000pt]8 Conclusions,DiscussionandFutureWork [size=10.000000pt]........................ 95

[size=10.000000pt]8.1 SummaryofContributions................................... 95
[size=10.000000pt]8.2 OngoingandFutureWork ................................... 95
[size=10.000000pt]8.2.1 RatingSystemforAMs............................... 968.2.2 IntelligentScheduleforMADMs....................... 968.2.3 PDMBeyondDataStreamClassification ................ 968.2.4 Resource-Awareness ................................. 978.2.5 VisualizationofPDMResults.......................... 978.2.6 NewHardware ...................................... 97
[size=10.000000pt]8.3 FinalWords ............................................... 98

References......................................................... 99Index .............................................................107[size=18.000000pt]