Transforming Management with AI, Big-Data, and IoT

Introduction.- Part I.- Data analytics for distributed computing and IoT.- Innovations in Software Requirements Engineering for IoT and Big Data.- Innovations in Software Design and Architectures of IoT and Big Data Platforms.- Data storage channels and distributed storage networks.- Big data practices and applications.- Big data retrieval, processing, analysis, and analytics.- IoT and pharmacocybernetics.- IoT for smart manufacturing (industry 4.0) and smart spaces.- IoT big data and predictive analysis.- Managing big data models with interactive Machine learning techniques.- Managing big data models with interactive Deep learning techniques.- Machine learning, data mining, web mining, and graph mining for social network analysis.- Part II.- Mobile networking and computing for cloud and fog/edge computing.- Security and Privacy Approaches to IoT and Big Data Applications and Platforms.- Virtualization of storage, networking, and computing.- SDN-enabled cloud data centers.- Elasticity and scalability of cloud resources.- Cloud management, orchestration, and automation.- Server less computing and FaaS.- Cloud-based storage platforms.- Decentralized storage in cloud and fog/edge computing systems.- Software-defined storage.- Part III.- Decentralized applications (Exchanges, Mining Pools, Trading Platforms).- Blockchain technology for developing countries.- Scalability, sharding, and scalable services for blockchain systems.- Security and privacy in the cloud and fog/edge infrastructure, services and storage.- Big Data and blockchain technology.- Cloud based authentication and privacy mechanisms Part IV.- Agile Software Engineering Approaches to IoT and Big Data Applications.- Software Engineering Framework for IoT.- Software Engineering Framework for Big Data.- Big Data Algorithms and Applications.- IoT design and architectures.- Security and Privacy Approaches to IoT and Big Data Applications and Platforms.- Conclusion.