%0 Journal Article
%9 ACL : Articles dans des revues avec comité de lecture répertoriées par l'AERES
%A Chibah, A.
%A Amer-Yahi, S.
%A Berti-Equille, Laure
%T QeNoBi : a system for QuErying and miNing BehavIoral patterns [demonstration paper]
%B International Conference on Data Engineering (ICDE)
%C Chania
%D 2021
%L fdi:010082404
%G ENG
%I IEEE
%@ 978-1-7281-9185-0
%K MONDE
%M ISI:000687830800293
%P 2673-2676
%R 10.1109/ICDE51399.2021.00301
%U https://www.documentation.ird.fr/hor/fdi:010082404
%> https://www.documentation.ird.fr/intranet/publi/2021-07/010082404.pdf
%W Horizon (IRD)
%X We demonstrate QeNoBi, a system for mining and querying customer behavioral patterns. QeNoBi combines an interactive visual interface, on-demand mining, and efficient topk processing, to provide the exploration of customer behavior over time. QeNoBi relies on two distinct data models: a customercentric graph that represents customers with similar purchasing behaviors and is annotated with a change algebra to reflect their behavior evolution, and product-centric time series that reflect the evolution of customer purchases over time. Users can query both representations along three dimensions : shape (the sketched trend of the behavior), scope (the set of customers/products of interest), and time granularity. QeNoBi provides a holistic behavior exploration capability by allowing users to seamlessly switch between customer-centric and product-centric views in a coordinated manner, thereby catering to various needs. A demonstration of QeNoBi is available at https://bit.ly/2HlcO3S
%B ICDE.International Conference on Data Engineering
%8 2021/04/19-22
%$ 122 ; 020 ; 096