A Tensor Based Data Model for Polystore: An Application to Social Networks Data

TL;DR

This paper introduces a tensor-based multi-paradigm data model for social network data in polystore systems, enhancing data integration, query performance, and semantic expressiveness, demonstrated through a Twitter case study.

Contribution

It presents a novel tensor-based model that enables logical independence and efficient integration of diverse data models in polystore systems for social data.

Findings

Improved query performance in social data analysis.

Enhanced semantic expressiveness of social network data.

Successful application to Twitter message virality analysis.

Abstract

In this article, we show how the mathematical object tensor can be used to build a multi-paradigm model for the storage of social data in data warehouses. From an architectural point of view, our approach allows to link different storage systems (polystore) and limits the impact of ETL tools performing model transformations required to feed different analysis algorithms. Therefore, systems can take advantage of multiple data models both in terms of query execution performance and the semantic expressiveness of data representation. The proposed model allows to reach the logical independence between data and programs implementing analysis algorithms. With a concrete case study on message virality on Twitter during the French presidential election of 2017, we highlight some of the contributions of our model.