neOCampus – IRIT – CNRS , Toulouse University

Keywords

Big Data, datalake, big data analytics, IoT, data management, data analysis, open-source, open science, web semantic

IoT data is increasingly integrated into the core of today's society. Whether you want to analyze a market or a product or study a specific research area, it is increasingly necessary to integrate IoT data but also combine it with massive data produced internally or externally with Open Data. To have a complete vision, it is necessary to integrate both voluminous fast data and numerous small data. Thus, in order to respond to the Vs of Big Data, we have designed an architecture that allows us to manage the Volumetry, Velocity, Variety and Veracity of data to generate Value. This architecture aims at allowing the simple crossing of data whatever the volume, the type or the rate while emphasizing the security of the data, the valorization of these data through the advanced use of the metadata and the use of these metadata through high added value services.

Scientific goals

- Manage any type of data in large volumes with efficiency

- Create value through adequate data modeling

- Enable cross-analysis of heterogeneous data simply in the Big Data context

Contacts

Vincent-Nam.Dang_at_irit.fr / dang.vincentnam_at_gmail.com,  Francois.Thiebolt_at_irit.fr, Marie-Pierre.Gleizes_at_irit.fr

Project repository

https://gitlab.irit.fr/datalake/docker_datalake/

https://github.com/vincentnam/docker_datalake

Scientific Paper

DANG, ZHAO, MEGDICHE, RAVAT (2021), A Zone-Based Data Lake Architecture for IoT, Small and Big Data. IDEAS 2021, to appear. (DOI: 10.1145/3472163.3472185 / ISBN : 978-1-4503-8991-4/21/07)