Anmerkung: Intro -- 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 -- 1 Introduction -- 2 Opportunistic Networks -- 2.1 Definition -- 2.2 Challenges -- 2.3 Use Cases -- 3 Data Routing and Dissemination -- 3.1 Basic Algorithms -- 3.2 Socially Based Algorithms -- 3.3 History-and Prediction-Based Algorithms -- 4 Potential Solutions -- 4.1 SPRINT -- 4.2 SENSE -- 5 Future Trends -- 6 Conclusions -- 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 Attributes -- 6 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 Traffic Monitoring: Energy Efficiency, Reliability, and Fault Tolerance -- 1 Introduction -- 2 Literature Survey -- 3 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 Method -- 4.1 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 -- 6.4 Traffic Generated by Data Gathering Using Lengthwise Tree in the Crosswise Distribution -- 6.5 The Relation between the Performance Gain and the Overhead Generated by the Forwarding Route Control -- 7 Simulation Experiments -- 7.1 Simulation Model -- 7.2 Validation of the Discussion in Section 6 -- 7.3 Performance Evaluation of DGUMA/DA -- 8 Conclusion -- References -- P2P Data Replication: Techniques and Applications -- 1 Introduction -- 2 P2PSystems -- 3 Data Replication and Update Management in P2P Systems -- 3.1 Data Replication Update Management. , 3.2 Data Replication Techniques in P2P -- 4 Replication Requirements and Solutions for Different Applications -- 4.1 Consistency and Limits to Replication -- 4.2 Context and Uses of Data Replication -- 5 A Fuzzy-Based System for Evaluating Data Replication Factor -- 5.1 Fuzzy Logic -- 5.2 Proposed System -- 5.3 Simulation Results -- 6 Discussion and Analysis -- 6.1 Setting Up a Replication Plan -- 7 Conclusions -- References -- Leveraging High-Performance Computing Infrastructures to Web Data Analytic Applications by Means of Message-Passing Interface -- 1 Introduction -- 2 Data-Centric Parallelisation with MPI -- 3 OMPIJava - Java Bindings for Open MPI -- 4 OMPIJava Performance Evaluation -- 5 Parallelisation of Random Indexing with MPI -- 6 Performance Analysis Tools -- 7 Conclusion and Future Directions -- References -- ReHRS: A Hybrid Redundant System for Improving MapReduce Reliability and Availability -- 1 Introduction -- 2 Background and Related Work -- 2.1 Background -- 2.2 Related Work -- 3 The Proposed System -- 3.1 Metadata -- 3.2 State/Metadata Synchronization -- 3.3 Periodical P-Metadata Backup/Update -- 3.4 Failure Detection -- 3.5 Dynamic Warmup Mechanism -- 3.6 Takeover Process -- 4 Performance Evaluation -- 4.1 Takeover Delays Evaluation -- 4.2 Warmup Performance Evaluation -- 5 Conclusion and Future Work -- References -- Analysis and Visualization of Large-Scale Time Series Network Data -- 1 Introduction -- 2 Big Data Applications -- 2.1 Environmental Sustainability -- 2.2 Data Mining for Trend Identification -- 2.3 Sensor Networks and Visualization -- 2.4 Network Application Design -- 2.5 Big Data Analysis for Environmental Sustainability -- 3 Visualization and Pattern Identification in Big Data -- 3.1 Large-Scale Data for Visualization -- 3.2 Time Series Data Analysis -- 3.3 Methodology -- 4 Preparing Big D Data for Analysis. , 5 RapidMiner vs. Weka vs. Orange -- 6 Mining the Data -- 7 Visual Inspectio on of Raw Data -- 8 Visualization an nd Presentation in Context -- 9 Conclusions -- References -- Parallel Coordinates Version of Time-Tunnel (PCTT) and Its Combinatorial Use for Macro to Micro Level Visual Analytics of Multidimensional Data -- 1 Introduction -- 2 Related Work -- 3 Essential Mechanisms of IntelligentBox -- 3.1 Model-Display Object (MD) Structure -- 3.2 Message-Sending Protocol for Slot-Connections -- 4 Time-Tunnel and Its Parallel Coordinates Version -- 4.1 System Configuration -- 4.2 Parallel Coordinates Version of Time-Tunnel (PCTT) -- 4.3 2Dto2D Visualization Functionality -- 5 Network Data Visualization Using PCTT with 2Dto2D Visualization -- 5.1 IP Packet Data -- 5.2 PCTT with 2Dto2D Visualization for IP Packet Data -- 5.3 Intrusion Detection -- 6 Combinatorial Use of PCTT for Macro Level to Micro Level Visual Analytics -- 7 Other Visualization Examples -- 8 Conclusion and Remarks -- References -- Towards a Big Data Analytics Framework for IoT and Smart City Applications -- 1 Introduction -- 2 Background: Big Data and the Internet of Things -- 2.1 The Various Faces of Big Data -- 2.2 Internet of Things -- 3 State of the Art -- 3.1 Big Data -- 3.2 Batch Processing -- 3.3 Real-Time Analytics -- 4 The Big Smart City Integration Challenges -- 4.1 Integration of Batch and Stream Processing -- 4.2 Integration of Heterogeneous Data Sources -- 4.3 Natural Text and Social Media Analysis -- 5 Case Study: Smart Grid Analytics -- 5.1 Power Quality Analytics -- 5.2 Real-Time Grid Monitoring -- 5.3 Forecasting Energy Demand -- 5.4 Lessons Learned -- 6 Unified Big Data Processing -- 6.1 Model Learning in the Batch Layer -- 6.2 Viewing Data Patterns in the Serving Layer -- 6.3 In-Stream Analytics in the Streaming Layer -- 6.4 Accessing the Data in the Application Layer. , 7 Further Research Directions -- 8 Conclusions -- References -- How the Big Data Is Leading the Evolution of ICT Technologies and Processes -- 1 Introduction -- 2 What Is the Big Data -- 2.1 Telecommunication Services -- 2.2 The Social Wave -- 2.3 Science and Research -- 2.4 Government and Public Sector -- 2.5 Internet of Things -- 2.6 Clouds -- 3 The Big Data Opportunity -- 3.1 Bringing Value -- 3.2 The Big Data Value across Sectors -- 4 The Impact of Big Data on Technologies -- 4.1 Big Data and Technologies -- 4.2 Storage Costs and Efficiency -- 5 Big Data Management and Analytics -- 5.1 Database Architectures for Big Data Processing -- 5.2 The MapReduce Paradigm -- 5.3 Hybrid Solutions -- 5.4 The Hadoop Processing Framework -- 5.5 Big Data Analytics -- 5.6 Results Visualization -- 6 Big Data Models -- 7 Conclusion -- References -- Big Data, Unstructured Data, and the Cloud: Perspectives on Internal Controls -- 1 Introduction -- 2 Preliminary Concepts and Definitions -- 3 State of the Art -- 3.1 The Advantages of Cloud Services -- 3.2 The Disadvantages of Cloud Services -- 3.3 Data Storage and the Cloud -- 3.4 The Use and Frequency of Unstructured Data -- 4 Problems, Issues, and Challenges -- 4.1 Self-performed Audit Procedures -- 4.2 Third-Party (Service) Audit Procedures -- 4.3 Other Procedures -- 4.4 Timescales and Logistical Concerns -- 5 Proposed Approach and Solutions -- 5.1 Background to the Survey -- 5.2 The Survey -- 5.3 Survey Results -- 5.4 Interpretation and Analysis of the Results -- 6 Summary Evaluations and Lessons Learned -- 7 Further Research Issues -- 8 Conclusion -- References -- Future Human-Centric Smart Environments -- 1 Introduction -- 2 Main Challenges for Achieving Livable Smart Cities -- 2.1 IoT Technologies -- 2.2 Trusted IoT -- 2.3 Smart City Management and Services -- 2.4 User Involvement -- 3 User Centric IoT Systems. , 3.1 Human Centered Perspective.