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Lucile Sautot

dblp:151/1439 · DBLP profile ↗
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9ranked-venue papers
2as first author
3since 2021 · last 2021
0000-0002-4204-7427ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 5 · 2 first-author · 2 since 2021Artificial intelligence and machine learning · 4 · 1 since 2021Applied, interdisciplinary, general and emerging computing · 2 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Data integration and cleaning · 44% Data models and query languages · 44% Database system architecture and tuning · 13%

Topics — the 1 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Data integration and cleaning
data warehouse
0.512021
A Semi-Automatic Design Methodology for (Big) Data Warehouse Transforming Facts into Dimensions · IEEE Trans. Knowl. Data Eng. 2021

Methods — techniques the papers use, named apart from their topics

schema transformation · 0.5multidimensional modeling · 0.5
YearPublicationVenuePosition
2021 WEIR-P: An Information Extraction Pipeline for the Wastewater Domain
Nanee Chahinian, Thierry Bonnabaud La Bruyère, Francesca Frontini, Carole Delenne, Marin Julien, Rachel Panckhurst, Mathieu Roche, Lucile Sautot, Laurent Deruelle, Maguelonne Teisseire
RCIS8
2021 A profile-aware methodological framework for collaborative multidimensional modeling
Amir Sakka, Sandro Bimonte, Stefano Rizzi, Lucile Sautot, François Pinet, Michela Bertolotto, Aurélien Besnard, Nora Rouillier
Data Knowl. Eng.4
2021 A Semi-Automatic Design Methodology for (Big) Data Warehouse Transforming Facts into Dimensions
abstract
A decision support system is used by decision makers for a long time. But, in some cases, the originally designed multidimensional schema does not cover the entire needs of decision makers, which can change over time. One such unfulfilled need, is using facts to describe dimension members. In this article, we propose a methodology to transform the constellation schema of a data warehouse by integrating factual data into a dimension. The proposed methodology and algorithms enrich a constellation multidimensional schema with new analytical possibilities for decision makers. This enrichment has repercussions for the entire multidimensional schema that are managed by multidimensional modeling, hierarchy calculation and the hierarchy version. In this article, we present a theoretical view of the proposed methodology supported by a case study, an implemented prototype and a complete evaluation based on a standard benchmark.
Lucile Sautot, Sandro Bimonte, Ludovic Journaux
IEEE Trans. Knowl. Data Eng.1
2019 Volunteered Multidimensional Design to the Test: The Farmland Biodiversity VGI4Bio Project's Experiment
Sandro Bimonte, Stefano Rizzi, Lucile Sautot, Benoît Fontaine
DOLAP3
2018 A Volunteer Design Methodology of Data Warehouses
Amir Sakka, Sandro Bimonte, Lucile Sautot, Guy Camilleri, Pascale Zaraté, Aurélien Besnard
ER3
2017 Large-scale nonlinear dimensionality reduction for network intrusion detection
Yasir Hamid, Ludovic Journaux, John A. Lee 0001, Lucile Sautot, Nabi Bushra, M. Sugumaran
ESANN4
2015 NLDR methods for high dimensional NIRS dataset : application to vineyard soils characterization
Clément Delion, Ludovic Journaux, Aurore Payen, Lucile Sautot, Emmanuel Chevigny, Pierre Curmi
ESANN4
2015 Efficient unsupervised clustering for spatial birds population analysis along the river Loire
Aurore Payen, Ludovic Journaux, Clément Delion, Lucile Sautot, Bruno Faivre
ESANN4
2014 A Methodology and Tool for Rapid Prototyping of Data Warehouses Using Data Mining: Application to Birds Biodiversity
Lucile Sautot, Sandro Bimonte, Ludovic Journaux, Bruno Faivre
MEDI1