Method and apparatus for combining techniques of calculus, statistics and data normalization in machine learning for analyzing large volumes of data

摘要

The advancements of the Internet of Things and the big data analytics systems demand new model for analyzing large volumes of data from a plurality of software systems, machines and embedded sensors used for a plurality of application areas such as natural ecosystems, bioinformatics, smart homes, smart cities, automobiles and airplanes. These complex systems need efficient methods for near real time collection, processing, analysis and sharing of data from and among the plurality of sensors, machines and humans. This invention identities and proposes implementation of a new model (CALSTATDN) for machine learning over large volumes of data combining methods of calculus (CAL), statistics (STAT) and database normalization (DN) in order to reduce error in learning process and to increase performance by several orders of magnitude. This invention further describes machine learning techniques for storing and processing of high speed real-time streaming data with variations in time, space and other dimensions.