This comprehensive edited 2-volume handbook provides a unique platform for researchers, engineers, developers, educators and advanced students in the field of Big Data analytics. The first volume presents methodologies that support Big Data analytics, while the second volume offers a wide range of Big Data analytics applications
Analysis
big data analytics
distributed ledger technology
algorithmic contracts
automated analytics pipelines
contract-driven financial reporting
parallel hierarchical clustering
evolving spiking neural networks
stock market movement prediction
banking analytics
GPU
wavelet neural network
parallel self-organizing maps
bank customer complaints
visual sentiment analysis
Apache Spark
parallelized radial basis function neural network
big data regression
recommender systems
e-commerce
gender-based classification
smart city
IoT data streams
traffic prediction
behaviour analytics
privacy-preserving techniques
cloud storage
secure big data storage
ciphertext-policy attribute-based signcryption
Internet of Things
software defined networking
secure routing
zero attraction data selective adaptive filtering algorithm
