Advances in Big Data: Proceedings of the 2nd INNS Conference on Big Data

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.

Table of Contents

Chapter 1. Predicting Human Behavior Based on Web Search Activity: Greek Referendum of 2015
Chapter 2. Spatial Bag of Features Learning for Large Scale Face Image Retrieval
Chapter 3. Compact Video Description and Representation for Automated Summarization of Human Activities
Chapter 4. Incremental Estimation of Visual Vocabulary Size for Image Retrieval
Chapter 5. Attribute Learning for Network Intrusion Detection
Chapter 6. Sampling Methods in Genetic Programming Learners from Large Datasets: A Comparative Study
Chapter 7. A Fast Deep Convolutional Neural Network for Face Detection in Big Visual Data
Chapter 8. Novel Automatic Filter-Class Feature Selection for Machine Learning Regression
Chapter 9. Learning Using Multiple-Type Privileged Information and SVM+ThinkTank
Chapter 10. Learning Symbols by Neural Network
Chapter 11. A CPM-Based Change Detection Test for Big Data
Chapter 12. Hadoop MapReduce Performance on SSDs: The Case of Complex Network Analysis Tasks
Chapter 13. Designing HMMs in the Age of Big Data
Chapter 14. Analyzing Big Security Logs in Cluster with Apache Spark
Chapter 15. Delay Prediction System for Large-Scale Railway Networks Based on Big Data Analytics
Chapter 16. An Empirical Comparison of Methods for Multi-label Data Stream Classification
Chapter 17. Extended Formulations for Online Action Selection on Big Action Sets
Chapter 18. A-BIRCH: Automatic Threshold Estimation for the BIRCH Clustering Algorithm
Chapter 19. Playlist Generation via Vector Representation of Songs
Chapter 20. A Distributed Framework for Early Trending Topics Detection on Big Social Networks Data Threads
Chapter 21. Multi-task Deep Neural Networks for Automated Extraction of Primary Site and Laterality Information from Cancer Pathology Reports
Chapter 22. A Graph-Based Big Data Model for Wireless Multimedia Sensor Networks
Chapter 23. CCM: Controlling the Change Magnitude in High Dimensional Data
Chapter 24. Spark Parameter Tuning via Trial-and-Error
Chapter 25. Parallel Computing TEDA for High Frequency Streaming Data Clustering
Chapter 26. A Big Data Intelligent Search Assistant Based on the Random Neural Network
Chapter 27. RandomFIS: A Fuzzy Classification System for Big Datasets
Chapter 28. Big Data for a Linked Open Economy
Chapter 29. Smart Data Integration by Goal Driven Ontology Learning
Chapter 30. An Infrastructure and Approach for Inferring Knowledge Over Big Data in the Vehicle Insurance Industry
Chapter 31. Defining and Identifying Stophashtags in Instagram
Chapter 32. Big Data and the Virtuous Circle of Railway Digitization
Chapter 33. Unified Retrieval Model of Big Data
Chapter 34. Adaptive Elitist Differential Evolution Extreme Learning Machines on Big Data: Intelligent Recognition of Invasive Species