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16 results on '"Barret, Nelly"'

1. User-Friendly Exploration of Highly Heterogeneous Data Lakes

Author

Barret, Nelly, Ebel, Simon, Galizzi, Théo, Manolescu, Ioana, Mohanty, Madhulika, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sellami, Mohamed, editor, Vidal, Maria-Esther, editor, van Dongen, Boudewijn, editor, Gaaloul, Walid, editor, and Panetto, Hervé, editor

Published
2024
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4. PathWays: Entity-Focused Exploration of Heterogeneous Data Graphs

Author

Barret, Nelly, Gauquier, Antoine, Law, Jia-Jean, Manolescu, Ioana, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Pesquita, Catia, editor, Skaf-Molli, Hala, editor, Efthymiou, Vasilis, editor, Kirrane, Sabrina, editor, Ngonga, Axel, editor, Collarana, Diego, editor, Cerqueira, Renato, editor, Alam, Mehwish, editor, Trojahn, Cassia, editor, and Hertling, Sven, editor

Published
2023
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5. Exploring Heterogeneous Data Graphs Through Their Entity Paths

Author

Barret, Nelly, Gauquier, Antoine, Law, Jia Jean, Manolescu, Ioana, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Abelló, Alberto, editor, Vassiliadis, Panos, editor, Romero, Oscar, editor, and Wrembel, Robert, editor

Published
2023
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7. An Environmental Study of French Neighbourhoods

Author

Barret, Nelly, Duchateau, Fabien, Favetta, Franck, Gentil, Aurélien, Bonneval, Loïc, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Hammoudi, Slimane, editor, Quix, Christoph, editor, and Bernardino, Jorge, editor

Published
2021
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8. Computing Generic Abstractions from Application Datasets

Author

Barret, Nelly, Manolescu, Ioana, Upadhyay, Prajna, Rich Data Analytics at Cloud Scale (CEDAR), Laboratoire d'informatique de l'École polytechnique [Palaiseau] (LIX), École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), and ANR-20-CHIA-0015,SourcesSay,Analyse et Interconnexion Intelligente des Contenus Héterogènes dans des Arènes Numériques(2020)

Subjects
Data sumarization, Semi-structured data, [INFO]Computer Science [cs], Entity-Relationship schema
Abstract

International audience; Digital data plays a central role in sciences, journalism, environment, digital humanities, etc. Open Data sharing initiatives lead to many large, interesting datasets being shared online. Some of these are RDF graphs, but other formats like CSV, relational, property graphs, JSON or XML documents are also frequent.Practitioners need to understand a dataset to decide whether it is suited to their needs. Datasets may come with a schema and/or may be summarized, however the first is not always provided and the latter is often too technical for non-IT users. To overcome these limitations, we present an end-to-end dataset abstraction approach, which (i) applies on any (semi)structured data model; (ii) computes a description meant for human users, in the form of an Entity- Relationship diagram; (ii) integrates Information Extraction and data profiling to classify dataset entities among a large set of intelligible categories. We implemented our approach in a system called Abstra, and detail its performance on various datasets.

Published
2024

9. Abstra: Toward Generic Abstractions for Data of Any Model

Author

Barret, Nelly, Manolescu, Ioana, Upadhyay, Prajna, Institut Polytechnique de Paris (IP Paris), Rich Data Analytics at Cloud Scale (CEDAR), Laboratoire d'informatique de l'École polytechnique [Palaiseau] (LIX), École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)

Subjects
[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], [INFO]Computer Science [cs]
Abstract

International audience; Digital data sharing leads to unprecedented opportunities to develop data-driven systems for supporting economic activities, the social and political life, and science. Many open-access datasets are RDF (Linked Data) graphs, but others are JSON or XML documents, CSV files, Neo4J property graphs, etc. Potential users need to understand a dataset in order to decide if it is useful for their goal. While some published datasets come with a schema and/or documentation, this is not always the case. We demonstrate Abstra, a dataset abstraction system, which applies on a large variety of data models. Abstra computes a description meant for humans, and integrates Information Extraction to classify dataset content among a set of categories of interest to the user. Our abstractions are conceptually close to Entity-Relationship diagrams, but our entities can have deeply nested structure.

Published
2022

11. Toward Generic Abstractions for Data of Any Model

Author

Barret, Nelly, Manolescu, Ioana, Upadhyay, Prajna, Barret, Nelly, Analyse et Interconnexion Intelligente des Contenus Héterogènes dans des Arènes Numériques - - SourcesSay2020 - ANR-20-CHIA-0015 - PNIA - VALID, Département d'informatique de l'École polytechnique (X-DEP-INFO), École polytechnique (X), Rich Data Analytics at Cloud Scale (CEDAR), Laboratoire d'informatique de l'École polytechnique [Palaiseau] (LIX), Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), ANR-20-CHIA-0015,SourcesSay,Analyse et Interconnexion Intelligente des Contenus Héterogènes dans des Arènes Numériques(2020), and École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France

Subjects
ComputingMethodologies_DOCUMENTANDTEXTPROCESSING, [INFO]Computer Science [cs], [INFO] Computer Science [cs]
Abstract

International audience; Digital data sharing leads to unprecedented opportunities to develop data-driven systems for supporting economic activities, the social and political life, and science. Many open-access datasets are RDF graphs, but others are CSV files, Neo4J property graphs, JSON or XML documents, etc. Potential users need to understand a dataset in order to decide if it is useful for their goal. While some datasets come with a schema and/or documentation, this is not always the case. Data summarization or schema inference tools have been proposed, specializing in XML, or JSON, or the RDF data models. In this work, we present a dataset abstraction approach, which () applies on relational, CSV, XML, JSON, RDF or Property Graph data; () computes an abstraction meant for humans (as opposed to a schema meant for a parser); () integrates Information Extraction data profiling, to also classify dataset content among a set of categories of interest to the user. Our abstractions are conceptually close to an Entity-Relationship diagram, if one allows nested and possibly heterogeneous structure within entities.

Published
2021

12. Facilitating Heterogeneous Dataset Understanding

Author

Barret, Nelly, Département d'informatique de l'École polytechnique (X-DEP-INFO), École polytechnique (X), Rich Data Analytics at Cloud Scale (CEDAR), Laboratoire d'informatique de l'École polytechnique [Palaiseau] (LIX), École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), ANR-20-CHIA-0015,SourcesSay,Analyse et Interconnexion Intelligente des Contenus Héterogènes dans des Arènes Numériques(2020), and Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Inria Saclay - Ile de France

Subjects
[INFO]Computer Science [cs]
Abstract

International audience; The era of Big Data and data sharing has lead to very large volumes of data becoming available to users across the world. This data is heterogeneous in its modelling, format and quality. Taking full advantage of such data raises many challenges, in particular related to the integration and the understanding of such data. My PhD thesis, started in January 2021, seeks to develop novel methods to help users without advanced IT skills discover a new dataset, by () building an abstract understanding of the data, as consisting of records and collections, () interpreting or classifying the data based on users' interests, and leveraging Information Extraction and Natural Language tools.

Published
2021

15. À la recherche du quartier idéal

Author

Barret, Nelly, Duchateau, Fabien, Favetta, Franck, Miquel, Maryvonne, Gentil, Aurélien, Bonneval, Loïc, Base de Données (BD), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), Centre Max Weber (CMW), Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Lyon (ENS Lyon)-Université Lumière - Lyon 2 (UL2)-Université Jean Monnet [Saint-Étienne] (UJM), Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), École normale supérieure de Lyon (ENS de Lyon)-Université Lumière - Lyon 2 (UL2)-Université Jean Monnet - Saint-Étienne (UJM)-Centre National de la Recherche Scientifique (CNRS), and Franck, Favetta

Subjects
[INFO]Computer Science [cs], [INFO] Computer Science [cs], ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2019

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