@ARTICLE{10.21494/ISTE.OP.2021.0606, TITLE={Evolution and formalization of the Lambda Architecture for high performance analytics - Application to Twitter data}, AUTHOR={Annabelle Gillet, Éric Leclercq, Nadine Cullot, }, JOURNAL={Open Journal in Information Systems Engineering}, VOLUME={2}, NUMBER={Issue 1}, YEAR={2021}, URL={http://www.openscience.fr/Evolution-and-formalization-of-the-Lambda-Architecture-for-high-performance}, DOI={10.21494/ISTE.OP.2021.0606}, ISSN={2634-1468}, ABSTRACT={Extracting value from social network data is a task whose complexity is driven by speed, volume and variability of data. Users develop multiple uses of these systems, that enhance the semantic variability. Analytics results must be produce as soon as possible (optimally in real-time) to be more relevant. Thus, business knowledge is essential and can usually be acquired by doing exploratory analysis. Accordingly, systems that harvest, store and analyze data from social networks have to support important streams of data, real-time analysis and exploratory analysis. Architecture styles and pattern allow to take these specificities into consideration, by proposing techniques to handle those data, and thus to facilitate their processing. These architectures have to be formalized, to study if essential properties are fulfilled, to know their behaviour, and to anticipate the effects that components can have on other components when they are gathered into a same architecture, and this even before developing and putting in production the architecture. In this article, we propose an architecture pattern, the Lambda+ Architecture, inspired from the Lambda Architecture and adapted to the processing of Big Data. We propose a formalization for architectures based on category theory, and an implementation of our pattern to analyze Twitter data.}}