Providing cooperative data analytics for real applications using machine learning

Abstract

This paper presents a data analytics system which determines optimal analytics algorithms by selectively testing a wide range of different algorithms and optimizing parameters using Transformer-Estimator Graphs. Our system is applicable to situations in which multiple clients need to perform calculations on the same data sets. Our system allows clients to cooperate in performing analytics calculations by sharing results and avoiding redundant calculations. Computations may be distributed across multiple nodes, including both client and server nodes. We provide multiple options for dealing with changes to data sets depending upon the data consistency requirements of applications. Another key contribution of our work is the Transformer-Estimator Graph, a system for specifying a wide variety of options to use for machine learning modeling and prediction. We show how Transformer-Estimator Graphs can be used for analyzing time series data. A key feature that we provide for making our system easy to use is solution templates which are customized to problems in specific domains.