Combining techniques of Calculus, Statistics and Data Normalization in CALSTATDN model

The advancements of the Internet of Things and the big data analytics systems need new model for analyzing large volumes of data from a plurality of software systems, machines and embedded sensors used for application areas such as natural ecosystems, bioinformatics, smart homes, smart cities, automobiles, airplanes etc. 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 CALSTATDN model identifies and proposes implementation of a new model (CALSTATDN) for machine learning combining methods of calculus (CAL), statistics (STAT) and database normalization (DN) in order to reduce error and processing time of extremely large volumes of data by several orders of magnitude.