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Institut National de la Recherche Agronomique
Institut de Recherche en Informatique de Toulouse
Université de Genève
Ministère de la Culture et de la Communication
Djilali Liabes Sidi Bel Abbes
Universidade Federal do Espirito Santo
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Papers by Julien Lecubin
production is both expensive and few automated. Long time series and / or large spatial surveys are difficult to conduct,
since it is necessary to use several observers. The robustness and reproducibility of the observation are also harder to
get and is obviously impossible in archaeological sciences, even if modeling methods are improved.
In a context of multi-source data production, the equivalence of observation systems and the inter-calibration of the
observers become crucial. Multi-disciplinary integrative approaches become necessary to study systems where the output
© 2017 ISTE OpenScience – Published by ISTE Ltd. London, UK – openscience.fr Page | 2
of data, in each discipline, is discontinuous, imprecise and poorly distributed. Yet, all variables (characterization of
economic activities and human installation, productions studies, characteristics of the discovered or reconstituted objects,
biotic or abiotic data, maps of anthropogenic and natural pressures, rendered services and feelings, societal perception...)
of these systems interact over time and at each spatial scale.
After a few years of existence, ArkeoGIS aggregates 67 databases representing over 50 000 objects (sites, analyzes...).
With this standardization of archaeological and paleoenvironmental information, it seemed important to test new data
mining methods, to see whether "related" and complex data can be linked to these archaeological data sets. The link
between aggregated-bases extracts within ArkeoGIS allowed us to set up a cross-requesting and test possibilities in a
prototype developed by the consortium IndexMed. This prototype, open source, allows the establishment of links between
objects from different databases.
The consortium IndexMed aims to identify and to raise the scientific challenges related to data quality and heterogeneity.
The use of graphs allows us to consider data despite their disparity and without prioritization, and improve decision
support using emerging data mining methods (collaborative clustering, machine learning, graphs approaches,
representation knowledge). Adapting these methods in archeology allows us to go beyond the "simple"
production is both expensive and few automated. Long time series and / or large spatial surveys are difficult to conduct,
since it is necessary to use several observers. The robustness and reproducibility of the observation are also harder to
get and is obviously impossible in archaeological sciences, even if modeling methods are improved.
In a context of multi-source data production, the equivalence of observation systems and the inter-calibration of the
observers become crucial. Multi-disciplinary integrative approaches become necessary to study systems where the output
© 2017 ISTE OpenScience – Published by ISTE Ltd. London, UK – openscience.fr Page | 2
of data, in each discipline, is discontinuous, imprecise and poorly distributed. Yet, all variables (characterization of
economic activities and human installation, productions studies, characteristics of the discovered or reconstituted objects,
biotic or abiotic data, maps of anthropogenic and natural pressures, rendered services and feelings, societal perception...)
of these systems interact over time and at each spatial scale.
After a few years of existence, ArkeoGIS aggregates 67 databases representing over 50 000 objects (sites, analyzes...).
With this standardization of archaeological and paleoenvironmental information, it seemed important to test new data
mining methods, to see whether "related" and complex data can be linked to these archaeological data sets. The link
between aggregated-bases extracts within ArkeoGIS allowed us to set up a cross-requesting and test possibilities in a
prototype developed by the consortium IndexMed. This prototype, open source, allows the establishment of links between
objects from different databases.
The consortium IndexMed aims to identify and to raise the scientific challenges related to data quality and heterogeneity.
The use of graphs allows us to consider data despite their disparity and without prioritization, and improve decision
support using emerging data mining methods (collaborative clustering, machine learning, graphs approaches,
representation knowledge). Adapting these methods in archeology allows us to go beyond the "simple"