@incollection{fdi:010067401,
  title = {{D}evelopment of a knowledge system for {B}ig {D}ata : case study to plant phenotyping data},
  author = {{L}e {N}goc, {L}. and {T}ireau, {A}. and {V}enkatesan, {A}. and {N}eveu, {P}. and {L}armande, {P}ierre},
  editor = {},
  language = {{ENG}},
  abstract = {{I}n the recent years, the data deluge in many areas of scientific research brings challenges in the treatment and improvement of agricultural data. {R}esearch in bioinformatics field does not outside this trend. {T}his paper presents some approaches aiming to solve the {B}ig {D}ata problem by combining the increase in semantic search capacity on existing data in the plant research laboratories. {T}his helps us to strengthen user experiments on the data obtained in this research by infering new knowledge. {T}o achieve this, there exist several approaches having different characteristics and using different platforms. {N}evertheless, we can summarize it in two main directions: the query re-writing and data transformation to {RDF} graphs. {I}n reality, we can solve the problem from origin of increasing capacity on semantic data with triplets. {T}hus, data transformation to {RDF} graphs direction was chosen to work on the practical part. {H}owever, the synchronization data in the same format is required before processing the triplets because our current data are heterogeneous. {T}he data obtained for triplets are larger that regular triplestores could manage. {S}o we evaluate some of them thus we can compare the benefits and drawbacks of each and choose the best system for our problem.},
  keywords = {},
  booktitle = {{WIMS}'16 : international conference on web intelligence, mining and semantics},
  numero = {},
  pages = {art. no 27 [9 ]},
  address = {{N}ew {Y}ork},
  publisher = {{ACM}},
  series = {{I}nternational {C}onference {P}roceedings {S}eries},
  year = {2016},
  DOI = {10.1145/2912845.2912869},
  ISBN = {978-1-4503-4056-4},
  URL = {https://www.documentation.ird.fr/hor/fdi:010067401},
}