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Preface by Editors; Contents; List of Contributors; Exploring the Hamming Distance in Distributed Infrastructures for Similarity Search; 1 Introduction; 2 Background; 2.1 Vector Space Model; 2.2 Random Hyperplane Hashing and Hamming Similarity; 3 Literature Review; 4 Similarity Search Based on Hamming Distance; 4.1 Hamming DHT; 4.2 HCube; 5 Evaluations; 5.1 Hamming Similarity; 5.2 Hamming DHT; 5.3 HCube; 6 Conclusions and Further Research Issues; References; Data Modeling for Socially Based Routing in Opportunistic Networks; 1Introduction; 2Opportunistic Networks; 2.1Definition; 2.2Challenges
2.3Use Cases3Data Routing and Dissemination; 3.1Basic Algorithms; 3.2Socially Based Algorithms; 3.3History-and Prediction-Based Algorithms; 4Potential Solutions; 4.1SPRINT; 4.2SENSE; 5Future Trends; 6Conclusions; Decision Tree Induction Methods and Their Application to Big Data; 1 Introduction; 2 Preliminary Concepts and Background; 3 Subtasks and Design Criteria for Decision Tree Induction; 4 Attribute Selection Criteria; 4.1 Information Gain Criterion and Gain Ratio; 4.2 Gini Function; 5 Discretization of Attribute Values; 5.1 Binary Discretization; 5.2 Multi-interval Discretization
5.3 Discretization of Categorical or Symbolical Attributes6 Pruning; 6.1 Overview about Pruning Methods; 6.2 An Example of a Pruning Method
Cost-Complexity Pruning; 7 Fitting Expert Knowledge into the Decision Tree Model, Improvement of Classification Performance, and Feature Subset Selection; 8 How to Interpret a Learnt Decision Tree?; 8.1 Quantitative Measures for the Quality of the Decision Tree Model; 8.2 Comparison of Two Decision Trees; 9 Conclusions; References; Sensory Data Gathering for Road TrafficMonitoring: Energy Efficiency, Reliability,and Fault Tolerance; 1 Introduction
2 Literature Survey3 Convergecast Tree Management Scheme; 3.1 System Model and Assumptions; 3.2 Initialization; 3.3 Tree Maintenance; 3.4 Convergecast Controller; 4 Simulation Result; 5 Conclusion and Future Directions of Research; References; Data Aggregation and Forwarding Route Control for Efficient Data Gathering in Dense Mobile Wireless Sensor Networks; 1 Introduction; 2 Assumptions; 2.1 System Environment; 2.2 Geo-Routing; 3 Related Work; 3.1 Location-Based DataManagement in Dense MANETs; 3.2 Data Gathering Utilizing Correlation of Data in Wireless Sensor Networks
4 DGUMA: Our Previous Method4.1 Mobile Agent; 4.2 Deployment of Mobile Agents; 4.3 Movement of Mobile Agent; 4.4 Transmission of Sensor Data; 5 DGUMA/DA: The Extended Method; 5.1 Outline; 5.2 Timer Setting; 5.3 Transmission of Sensor Data; 5.4 Forwarding Route Control; 5.5 Restoring Sensor Readings at the Sink; 6 Discussion; 6.1 Overhead Generated by the Forwarding Route Control; 6.2 Traffic for Data Gathering Using Lengthwise Tree in the Lengthwise Distribution; 6.3 Traffic for Data Gathering Using Crosswise Tree in the Crosswise Distribution
2.3Use Cases3Data Routing and Dissemination; 3.1Basic Algorithms; 3.2Socially Based Algorithms; 3.3History-and Prediction-Based Algorithms; 4Potential Solutions; 4.1SPRINT; 4.2SENSE; 5Future Trends; 6Conclusions; Decision Tree Induction Methods and Their Application to Big Data; 1 Introduction; 2 Preliminary Concepts and Background; 3 Subtasks and Design Criteria for Decision Tree Induction; 4 Attribute Selection Criteria; 4.1 Information Gain Criterion and Gain Ratio; 4.2 Gini Function; 5 Discretization of Attribute Values; 5.1 Binary Discretization; 5.2 Multi-interval Discretization
5.3 Discretization of Categorical or Symbolical Attributes6 Pruning; 6.1 Overview about Pruning Methods; 6.2 An Example of a Pruning Method
Cost-Complexity Pruning; 7 Fitting Expert Knowledge into the Decision Tree Model, Improvement of Classification Performance, and Feature Subset Selection; 8 How to Interpret a Learnt Decision Tree?; 8.1 Quantitative Measures for the Quality of the Decision Tree Model; 8.2 Comparison of Two Decision Trees; 9 Conclusions; References; Sensory Data Gathering for Road TrafficMonitoring: Energy Efficiency, Reliability,and Fault Tolerance; 1 Introduction
2 Literature Survey3 Convergecast Tree Management Scheme; 3.1 System Model and Assumptions; 3.2 Initialization; 3.3 Tree Maintenance; 3.4 Convergecast Controller; 4 Simulation Result; 5 Conclusion and Future Directions of Research; References; Data Aggregation and Forwarding Route Control for Efficient Data Gathering in Dense Mobile Wireless Sensor Networks; 1 Introduction; 2 Assumptions; 2.1 System Environment; 2.2 Geo-Routing; 3 Related Work; 3.1 Location-Based DataManagement in Dense MANETs; 3.2 Data Gathering Utilizing Correlation of Data in Wireless Sensor Networks
4 DGUMA: Our Previous Method4.1 Mobile Agent; 4.2 Deployment of Mobile Agents; 4.3 Movement of Mobile Agent; 4.4 Transmission of Sensor Data; 5 DGUMA/DA: The Extended Method; 5.1 Outline; 5.2 Timer Setting; 5.3 Transmission of Sensor Data; 5.4 Forwarding Route Control; 5.5 Restoring Sensor Readings at the Sink; 6 Discussion; 6.1 Overhead Generated by the Forwarding Route Control; 6.2 Traffic for Data Gathering Using Lengthwise Tree in the Lengthwise Distribution; 6.3 Traffic for Data Gathering Using Crosswise Tree in the Crosswise Distribution