Massive Data Exploration using Estimated Cardinalities

Pierre Nerzic (1, 2) , Grégory Smits (1, 2) , Olivier Pivert (1, 2) , Marie-Jeanne Lesot (3)

1 IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires
2 SHAMAN - A Symbolic and Human-centric view of dAta MANagement
3 LFI - Learning, Fuzzy and Intelligent systems

Linguistic summaries are used in this work to provide personalized exploration functionalities on massive relational data. To ensure a fluid exploration of the data, cardinalities of the data properties described in the summaries are estimated from statistics about the data distribution. The proposed workflow also involves a vocabulary inference mechanism from these statistics and a sampling-based approach to consolidate the estimated cardinalities. The paper shows that soft computing techniques are particularly relevant to build concrete and functional business intelligence solutions.

Mots clés: Linguistic summarization; vocabulary inference; cardinality estimation; big data; proof-of-concept

Domaines: Recherche d'information [cs.IR]; Base de données [cs.DB]

Pierre Nerzic, Grégory Smits, Olivier Pivert, Marie-Jeanne Lesot. Massive Data Exploration using Estimated Cardinalities. WCCI 2022 - IEEE World Congress on Computational Intelligence, Jul 2022, Padoue, Italy. ⟨hal-03696293⟩