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Advances in Big Data

Proceedings of the 2nd INNS Conference on Big Data, October 23-25, 2016, Thessaloniki, Greece

  • Conference proceedings
  • © 2017

Overview

Editors:
  1. Plamen Angelov

    1. School of Computing and Communications, Lancaster University , Lancaster, United Kingdom

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  2. Yannis Manolopoulos

    1. Data Engineering Lab, Dept. of Informatics, Aristotle University of Thessaloniki , Thessaloniki, Greece

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  3. Lazaros Iliadis

    1. Lab of Forest Informatics (FiLAB), Democritus University of Thrace , Orestiada, Greece

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  4. Asim Roy

    1. WPC Information Systems Faculty, Arizona State University , Tempe, USA

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  5. Marley Vellasco

    1. Electrical Engineering Dept, (ICA), Pontifical Catholic Univ of Rio de Janei , Rio de Janeiro, Brazil

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  • Reports on the latest neural network technologies for big data analytics
  • Presents innovative algorithmic approaches to analyzing big data
  • Describes big data analytics applications to solve real-world problems
  • Includes supplementary material: sn.pub/extras

Part of the book series: Advances in Intelligent Systems and Computing (AISC, volume 529)

Included in the following conference series:

Conference proceedings info: INNS 2016.

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Table of contents (34 papers)

  1. Front Matter

    Pages i-xvii
    Download chapter PDF

  2. Predicting Human Behavior Based on Web Search Activity: Greek Referendum of 2015

    • Spyros E. Polykalas, George N. Prezerakos
    Pages 1-7

  3. Spatial Bag of Features Learning for Large Scale Face Image Retrieval

    • Nikolaos Passalis, Anastasios Tefas
    Pages 8-17

  4. Compact Video Description and Representation for Automated Summarization of Human Activities

    • Ioannis Mademlis, Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas
    Pages 18-28

  5. Incremental Estimation of Visual Vocabulary Size for Image Retrieval

    • Ilias Gialampoukidis, Stefanos Vrochidis, Ioannis Kompatsiaris
    Pages 29-38

  6. Attribute Learning for Network Intrusion Detection

    • Jorge Luis Rivero Pérez, Bernardete Ribeiro
    Pages 39-49

  7. Sampling Methods in Genetic Programming Learners from Large Datasets: A Comparative Study

    • Hmida Hmida, Sana Ben Hamida, Amel Borgi, Marta Rukoz
    Pages 50-60

  8. A Fast Deep Convolutional Neural Network for Face Detection in Big Visual Data

    • Danai Triantafyllidou, Anastasios Tefas
    Pages 61-70

  9. Novel Automatic Filter-Class Feature Selection for Machine Learning Regression

    • Morten Gill Wollsen, John Hallam, Bo Nørregaard Jørgensen
    Pages 71-80

  10. Learning Using Multiple-Type Privileged Information and SVM+ThinkTank

    • Ming Jiang, Li Zhang
    Pages 81-88

  11. Learning Symbols by Neural Network

    • Yoshitsugu Kakemoto, Shinichi Nakasuka
    Pages 89-99

  12. A CPM-Based Change Detection Test for Big Data

    • Giada Tacconelli, Manuel Roveri
    Pages 100-110

  13. Hadoop MapReduce Performance on SSDs: The Case of Complex Network Analysis Tasks

    • Marios Bakratsas, Pavlos Basaras, Dimitrios Katsaros, Leandros Tassiulas
    Pages 111-119

  14. Designing HMMs in the Age of Big Data

    • Cesare Alippi, Stavros Ntalampiras, Manuel Roveri
    Pages 120-130

  15. Analyzing Big Security Logs in Cluster with Apache Spark

    • Talha Oktay, Ahmet Sayar
    Pages 131-138

  16. Delay Prediction System for Large-Scale Railway Networks Based on Big Data Analytics

    • Luca Oneto, Emanuele Fumeo, Giorgio Clerico, Renzo Canepa, Federico Papa, Carlo Dambra et al.
    Pages 139-150

  17. An Empirical Comparison of Methods for Multi-label Data Stream Classification

    • Konstantina Karponi, Grigorios Tsoumakas
    Pages 151-159

  18. Extended Formulations for Online Action Selection on Big Action Sets

    • Shaona Ghosh, Adam Prügel-Bennett
    Pages 160-168

  19. A-BIRCH: Automatic Threshold Estimation for the BIRCH Clustering Algorithm

    • Boris Lorbeer, Ana Kosareva, Bersant Deva, Dženan Softić, Peter Ruppel, Axel Küpper
    Pages 169-178

  20. Playlist Generation via Vector Representation of Songs

    • Burak Köse, Süleyman Eken, Ahmet Sayar
    Pages 179-185
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Other volumes

  1. Advances in Big Data

Keywords

About this book

The book offers a timely snapshot of neural network technologies as a significant component of big data analytics platforms. It promotes new advances and research directions in efficient and innovative algorithmic approaches to analyzing big data (e.g. deep networks, nature-inspired and brain-inspired algorithms); implementations on different computing platforms (e.g. neuromorphic, graphics processing units (GPUs), clouds, clusters); and big data analytics applications to solve real-world problems (e.g. weather prediction, transportation, energy management). The book, which reports on the second edition of the INNS Conference on Big Data, held on October 23–25, 2016, in Thessaloniki, Greece, depicts an interesting collaborative adventure of neural networks with big data and other learning technologies.

Editors and Affiliations

  • School of Computing and Communications, Lancaster University , Lancaster, United Kingdom

    Plamen Angelov

  • Data Engineering Lab, Dept. of Informatics, Aristotle University of Thessaloniki , Thessaloniki, Greece

    Yannis Manolopoulos

  • Lab of Forest Informatics (FiLAB), Democritus University of Thrace , Orestiada, Greece

    Lazaros Iliadis

  • WPC Information Systems Faculty, Arizona State University , Tempe, USA

    Asim Roy

  • Electrical Engineering Dept, (ICA), Pontifical Catholic Univ of Rio de Janei , Rio de Janeiro, Brazil

    Marley Vellasco

Bibliographic Information

  • Book Title: Advances in Big Data

  • Book Subtitle: Proceedings of the 2nd INNS Conference on Big Data, October 23-25, 2016, Thessaloniki, Greece

  • Editors: Plamen Angelov, Yannis Manolopoulos, Lazaros Iliadis, Asim Roy, Marley Vellasco

  • Series Title: Advances in Intelligent Systems and Computing

  • DOI: https://doi.org/10.1007/978-3-319-47898-2

  • Publisher: Springer Cham

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: Springer International Publishing AG 2017

  • Softcover ISBN: 978-3-319-47897-5Published: 09 October 2016

  • eBook ISBN: 978-3-319-47898-2Published: 20 October 2016

  • Series ISSN: 2194-5357

  • Series E-ISSN: 2194-5365

  • Edition Number: 1

  • Number of Pages: XVII, 348

  • Number of Illustrations: 101 b/w illustrations

  • Topics: Computational Intelligence, Data Mining and Knowledge Discovery, Artificial Intelligence

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