QeNoBi: a system for QuErying and miNing BehavIoral patterns [demonstration paper]

A. Chibah, S. Amer-Yahi and Laure Berti-Équille ()
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Laure Berti-Équille: UMR 228 Espace-Dev, Espace pour le développement - IRD - Institut de Recherche pour le Développement - UPVD - Université de Perpignan Via Domitia - AU - Avignon Université - UR - Université de La Réunion - UM - Université de Montpellier - UG - Université de Guyane - UA - Université des Antilles

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Abstract: 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

Keywords: MONDE (search for similar items in EconPapers)
Date: 2021
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Published in International Conference on Data Engineering (ICDE), IEEE, pp.2673-2676, 2021, 978-1-7281-9185-0

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03278948

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