Polystore and Tensor Data Model for Logical Data Independence and Impedance Mismatch in Big Data Analytics

Abstract

This paper presents a Tensor based Data Model (TDM) for polystore systems meant to address two major closely related issues in big data analytics architectures, namely logical data independence and data impedance mismatch. The TDM is an expressive model that subsumes traditional data models, it allows to link different data models of various data stores, and which also facilitates data transformations by using operators with clearly defined semantics. Our contribution is twofold. Firstly, it is the addition of the notion of a schema for the tensor mathematical object using typed associative arrays. Secondly, it is the definition of a set of operators to manipulate data through the TDM. In order to validate our approach we first show how our TDM model is inserted into a given polystore architecture. We then describe some use cases of real analyses using our TDM and its operators in the context of the French Presidential Election in 2017.