Nonparametric Distributed Learning Architecture for Big Data: Algorithm and Applications

Abstract

Dramatic increases in the size and complexity of modern datasets have made traditional "centralized" statistical inference prohibitive. In addition to computational challenges associated with big data learning, the presence of numerous data types (e.g. discrete, continuous, categorical, etc.) makes automation and scalability difficult. A question of immediate concern is how to design a data-intensive statistical inference architecture without changing the basic statistical modeling principles developed for "small" data over the last century. To address this problem, we present MetaLP, a flexible, distributed statistical modeling framework.

The purpose of this paper is to answer the question: What is the relevance of small-data-ideas in this big-data world? The bigger question is: Should we make difficult things easy or easy things look difficult? The first option will probably make some impact in the long-run, but the second one will surely earn prestigious journal publications in short-run, IEEE Transactions on Big Data (forthcoming). The first report came out in 2015