QeNoBi: A System for QuErying and miNing BehavIoral Patterns Authors' Copy

Abdelouahab Chibah (1) , Sihem Amer-Yahia (1) , Laure Berti-Equille (2)

1 LIG - Laboratoire d'Informatique de Grenoble
2 UMR 228 Espace-Dev, Espace pour le développement

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.

Domaines: Informatique [cs]

Abdelouahab Chibah, Sihem Amer-Yahia, Laure Berti-Equille. QeNoBi: A System for QuErying and miNing BehavIoral Patterns Authors' Copy. 2021 IEEE 37th International Conference on Data Engineering (ICDE), Apr 2021, Chania, France. pp.2673-2676, ⟨10.1109/ICDE51399.2021.00301⟩. ⟨hal-03379587⟩