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Erschienen in:
Formal Concept Analysis and Extensions for Complex Data Analytics Kapitel lesen Erstes Kapitel lesen

2022 | OriginalPaper | Buchkapitel

5. Dealing with Large Volumes of Complex Relational Data Using RCA

verfasst von : Agnès Braud, Xavier Dolques, Alain Gutierrez, Marianne Huchard, Priscilla Keip, Florence Le Ber, Pierre Martin, Cristina Nica, Pierre Silvie

Erschienen in: Complex Data Analytics with Formal Concept Analysis

Verlag: Springer International Publishing

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Abstract

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.

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Vorheriges Kapitel Analysis of Complex and Heterogeneous Data Using FCA and Monadic Predicates
Nächstes Kapitel Computing Dependencies Using FCA
Fußnoten
5
Based on Java Collection Framework and Lambda Expressions.
 
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Metadaten
Titel
Dealing with Large Volumes of Complex Relational Data Using RCA
verfasst von
Agnès Braud
Xavier Dolques
Alain Gutierrez
Marianne Huchard
Priscilla Keip
Florence Le Ber
Pierre Martin
Cristina Nica
Pierre Silvie
Copyright-Jahr
2022
Buch
Complex Data Analytics with Formal Concept Analysis

Print ISBN: 978-3-030-93277-0

Electronic ISBN: 978-3-030-93278-7

Copyright-Jahr: 2022

DOI
https://doi.org/10.1007/978-3-030-93278-7_5