@incollection{fdi:010085466,
  title = {{D}ealing with large volumes of complex relational data using {RCA}},
  author = {{B}raud, {A}. and {G}utierrez, {A}. and {H}uchard, {M}. and {K}eip, {P}. and {L}e {B}er, {F}. and {M}artin, {P}. and {N}ica, {C}. and {S}ilvie, {P}ierre},
  editor = {},
  language = {{ENG}},
  abstract = {{M}ost of available data are inherently relational, with e.g. temporal, spatial, causal or social relations. {B}esides, many datasets involve complex and voluminous data. {T}herefore, the exploration of relational data is a major challenge for {F}ormal {C}oncept {A}nalysis ({FCA}). {R}elational {C}oncept {A}nalysis ({RCA}) is specifically designed to investigate the relational structure of a dataset in the {FCA} paradigm. {I}n this chapter, we examine how {RCA} can take over the issues raised by complex data. {U}sing two datasets, one about the quality monitoring of waterbodies in {F}rance, the other about the use of pesticidal and antimicrobial plants in {A}frica, we study the limitations of different {FCA} algorithms, and their current implementations to explore these datasets with {RCA}. {W}e 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. {F}inally, we discuss about the possible directions to investigate.},
  keywords = {{AFRIQUE} ; {FRANCE}},
  booktitle = {{C}omplex data analytics with formal concept analysis},
  numero = {},
  pages = {[30 ]},
  address = {{C}ham},
  publisher = {{S}pringer},
  series = {},
  year = {2022},
  DOI = {10.1007/978-3-030-93278-7_5},
  URL = {https://www.documentation.ird.fr/hor/fdi:010085466},
}