Braud A., Gutierrez A., Huchard M., Keip P., Le Ber F., Martin P., Nica C., Silvie Pierre. (2022). Dealing with large volumes of complex relational data using RCA. In :
Missaoui R. (ed.), Kwuida L. (ed.), Abdessalem T. (ed.). Complex data analytics with formal concept analysis. Cham : Springer, [30 p.].
Titre du document
Dealing with large volumes of complex relational data using RCA
Année de publication
2022
Type de document
Partie d'ouvrage
Auteurs
Braud A., Gutierrez A., Huchard M., Keip P., Le Ber F., Martin P., Nica C., Silvie Pierre
In
Missaoui R. (ed.), Kwuida L. (ed.), Abdessalem T. (ed.), Complex data analytics with formal concept analysis
Source
Cham : Springer, 2022,
[30 p.]
Most of available data are inherently relational, with e.g. temporal, spatial, causal or social relations. Besides, many datasets involve complex and voluminous data. Therefore, the exploration of relational data is a major challenge for Formal Concept Analysis (FCA). Relational Concept Analysis (RCA) is specifically designed to investigate the relational structure of a dataset in the FCA paradigm. In this chapter, we examine how RCA can take over the issues raised by complex data. Using two datasets, one about the quality monitoring of waterbodies in France, the other about the use of pesticidal and antimicrobial plants in Africa, we study the limitations of different FCA algorithms, and their current implementations to explore these datasets with RCA. We also show how pattern extraction combined with the presentation of data in hierarchical structures is appropriate for the analysis of temporal datasets by the domain expert. Finally, we discuss about the possible directions to investigate.
Plan de classement
Mathématiques appliquées [020MATH01]
;
Maladies des plantes [076MALPLA]
Description Géographique
AFRIQUE ; FRANCE
Localisation
Fonds IRD [F B010085466]
Identifiant IRD
fdi:010085466
