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Title Modeling and processing for next-generation big-data technologies : with applications and case studies / Fatos Xhafa, Leonard Barolli, Admir Barolli, Petraq Papajorgji, editors.
Imprint Cham : Springer, [2014]
2015

LOCATION CALL # STATUS MESSAGE
 OHIOLINK SPRINGER EBOOKS    ONLINE  
View online
Series Modeling and Optimization in Science and Technologies, 2196-7326 ; volume 4
Modeling and optimization in science and technologies ; volume 4.
Subject Big data.
Database management.
Alt Name Xhafa, Fatos,
Barolli, Leonard,
Barolli, Admir,
Papajorgji, Petraq J.,
LOCATION CALL # STATUS MESSAGE
 OHIOLINK SPRINGER EBOOKS    ONLINE  
View online
Series Modeling and Optimization in Science and Technologies, 2196-7326 ; volume 4
Modeling and optimization in science and technologies ; volume 4.
Subject Big data.
Database management.
Alt Name Xhafa, Fatos,
Barolli, Leonard,
Barolli, Admir,
Papajorgji, Petraq J.,
Description 1 online resource (xx, 516 pages) : illustrations (some color).
polychrome rdacc
Note Includes indexes.
Contents 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.
Summary This book covers the latest advances in Big Data technologies and provides the readers with a comprehensive review of the state-of-the-art in Big Data processing, analysis, analytics, and other related topics. It presents new models, algorithms, software solutions and methodologies, covering the full data cycle, from data gathering to their visualization and interaction, and includes a set of case studies and best practices. New research issues, challenges and opportunities shaping the future agenda in the field of Big Data are also identified and presented throughout the book, which is inten.
Bibliography Note Includes bibliographical references and indexes.
Note Online resource; title from PDF title page (SpringerLink, viewed December 11, 2014).
ISBN 9783319091778 (electronic bk.)
3319091778 (electronic bk.)
331909176X
9783319091761
9783319091761
ISBN/ISSN 10.1007/978-3-319-09177-8
OCLC # 894554174
Additional Format Print version: Xhafa, Fatos. Modeling and Processing for Next-Generation Big-Data Technologies : With Applications and Case Studies. Cham : Springer International Publishing, 2014 9783319091761


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