A Mathematical Database to Process Time Series

Cyrille Ponchateau, Ladjel Bellatreche, Carlos Ordonez and Mickael Baron
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Cyrille Ponchateau: LIAS/ENSMA, Futuroscope Chasseneuil, France
Ladjel Bellatreche: LIAS/ENSMA, Futuroscope Chasseneuil, France
Carlos Ordonez: University of Houston, Houston, USA
Mickael Baron: LIAS/ENSMA, Futuroscope Chasseneuil, France

International Journal of Data Warehousing and Mining (IJDWM), 2018, vol. 14, issue 3, 1-21

Abstract: In scientific research, the results of an experiment commonly take the form of a time series, in which such time series consists of measurements collected from a sensor over time. After time series are stored, mathematical models are derived using numerical methods. Even though there exist plenty of tools to store and analyze time series data, there is scarce research aimed at storing and querying derived models, which are the most important mechanism for a scientist to understand data. In this article, the authors propose to help scientists with a flexible database structure to persist and manage mathematical models with a mathematical models store, with extended features, to handle time series. In this article, the authors introduce the concept of a mathematical models store enriched with numerical processing methods to allow queries based on raw time series data. Then they introduce a prototype, that is an implementation of such a data store with PostgreSQL.

Date: 2018
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