Abstract: The DATA LEARNING AND ANALYTICS APPARATUSES, METHODS AND SYSTEMS (“DLA”) provides a data learning platform that analyzes and incorporates data knowledge from new data event updates (e.g., in a real-time stream, in a batch, etc.). In one implementation, the DLA provides a real-time data mining mechanism that accommodates an ever increasing data load instantaneously with reduced complexity, e.g., without re-building the data analytical model in its entirety whenever new data is received. In this way, the DLA provides an incremental learning mechanism for data analytics and thus the latency for real-time data assessment is reduced and the data processing efficiency is improved.

Abstract: The Parallelizable Distributed Data Preservation Apparatuses, Methods and Systems (“PDDP”) transforms an ad impression event, a bidding invite, original data set, original data distribution estimation, symetry ML BET table inputs via PDDP components into real-time mobile bid, mobile ad placement, pseudo random datastet, build classifier structure, build regression structure outputs. In one example embodiment, the PDDP includes an apparatus. The PDDP's apparatus' instructions include obtaining original data set and determine appropriate symmetry ML basic element table, generating original data distribution estimation structure and generate new dataset random generation structure, generating new random dataset transformation structure and transforming original data with the symmetry ML basic element table into pseudo random dataset. The PDDP also provides pseudo random dataset to machine learning component and to generate build classifier and build regression structures from the machine learning component.

Abstract: The DATA LEARNING AND ANALYTICS APPARATUSES, METHODS AND SYSTEMS (“DLA”) provides a data learning platform that analyzes and incorporates data knowledge from new data event updates (e.g., in a real-time stream, in a batch, etc.). In one implementation, the DLA provides a real-time data mining mechanism that accommodates an ever increasing data load instantaneously with reduced complexity, e.g., without re-building the data analytical model in its entirety whenever new data is received. In this way, the DLA provides an incremental learning mechanism for data analytics and thus the latency for real-time data assessment is reduced and the data processing efficiency is improved.