Big Data analytics is the complex process of examining big data to uncover information such as correlations, hidden patterns, trends and user and customer preferences, to allow organizations and businesses to make more informed decisions. These methods and technologies have become ubiquitous in all fields of science, engineering, business and management due to the rise of data-driven models as well as data engineering developments using parallel and distributed computational analytics frameworks, data and algorithm parallelization, and GPGPU programming. However, there remain potential issues that need to be addressed to enable big data processing and analytics in real time.

• Chapter 1: Big data analytics for security intelligence
• Chapter 2: Zero attraction data selective adaptive filtering algorithm for big data applications
• Chapter 3: Secure routing in software defined networking and Internet of Things for big data
• Chapter 4: Efficient ciphertext-policy attribute-based signcryption for secure big data storage in cloud
• Chapter 5: Privacy-preserving techniques in big data
• Chapter 6: Big data and behaviour analytics
• Chapter 7: Analyzing events for traffic prediction on IoT data streams in a smart city scenario
• Chapter 8: Gender-based classification on e-commerce big data
• Chapter 9: On recommender systems with big data
• Chapter 10: Analytics in e-commerce at scale
• Chapter 11: Big data regression via parallelized radial basis function neural network in Apache Spark
• Chapter 12: Visual sentiment analysis of bank customer complaints using parallel self-organizing maps
• Chapter 13: Wavelet neural network for big data analytics in banking via GPU
• Chapter 14: Stock market movement prediction using evolving spiking neural networks
• Chapter 15: Parallel hierarchical clustering of big text corpora
• Chapter 16: Contract-driven financial reporting: building automated analytics pipelines with algorithmic contracts, Big Data and Distributed Ledger technology
• Overall conclusions