Big Data and Archaeology

Proceedings of the XVIII UISPP World Congress (4-9 June 2018, Paris, France) Volume 15, Session III-1

François Djindjian, Paola Moscati, François Djindjian, Paola Moscati

• Éditeur: Archaeopress Archaeology
• Année: 2021
• ISBN de l'eBook: 9781789697223
• Sujet: Sciences sociales
• Sujet: Archéologie

Big Data and Archaeology presents the papers from two sessions of the 18th UISPP World Congress (Paris, June 2018): Session III-1 (CA): 'Big data, databases and archaeology', and Session III-1 (T): 'New advances in theoretical archaeology'. The advent of Big Data is a recent and debated issue in Digital Archaeology. Historiographic context and current developments are illustrated in this volume, as well as comprehensive examples of a multidisciplinary and integrative approach to the recording, management and exploitation of excavation data and documents produced over a long period of archaeological research. In addition, specific attention is paid to neoprocessual archaeology, as a new platform aimed at renewing the theoretical framework of archaeology after thirty years of post-modernism, and to the refinement of the concept of archaeological cultures, combining processual, contextual and empirical approaches.

Table des matières

• Cover
• Contents
• Title Page
• Copyright Page
• UISPP PROCEEDINGS SERIES VOLUME 15
• Foreword to the XVIII UISPP Congress Proceedings
• List of Figures
• Introduction au volume
• François Djindjian
• Mégadonnées et archéologie : une introduction
• How Big Is Big Data?
• Paola Moscati
• Figure 1. J.-C. Gardin’s and P. Braffort’s paper presented at the UNESCO International Conference on Information Processing (Paris 1959). <https://unesdoc.unesco.org/ark:/48223/pf0000157450?posInSet=3&queryId=5331e227-746e-401d-8db9-d395fb6d21e6>.
• Figure 2. The Virtual Museum of Archaeological Computing, section Protagonists: interactive itinerary dedicated to Robert G. Chenhall. <http://archaeologicalcomputing.isma.cnr.it/itineraries/>
• Figure 3. The Virtual Museum of Archaeological Computing, section Events: interactive itinerary dedicated to the European postgraduate course on Data Processing and Mathematics Applied to Archaeology (Valbonne-Montpellier 1983). <http://archaeologicalcomp
• Figure 4. The Virtual Museum of Archaeological Computing, section Projects: interactive itinerary dedicated to the project ‘Automatisation of Etruscan corpora’. <http://archaeologicalcomputing.isma.cnr.it/itineraries/>.
• Figure 5. The Virtual Museum of Archaeological Computing, section Institutions: interactive itinerary dedicated to the Centre de recherches sur les TraitementsAutomatisés en Archéologie Classique (TAAC). <http://archaeologicalcomputing.isma.cnr.it/itinera
• Figure 6. Spatial distributions of the article published in «Archeologia e Calcolatori»n. 2014-2017 on Peripleo search engine. <https://peripleo.pelagios.org/>.
• Les statistiques et l’analyse spatiale des sites archéologiques sont à notre portée
• Olivier Buchsenschutz
• Innovative multidisciplinary method using Machine Learning to define human behaviors and environments during the Caune de l’Arago (Tautavel, France) Middle Pleistocene occupations
• Sophie Grégoire1, Nicolas Boulbes1, Bernard Quinio2, Matthieu Boussard3, Caroline Chopinaud3, Anne-Marie Moigne4, Agnès Testu1,Vincenzo Celiberti1, Cédric Fontaneil1, Christian Perrenoud4, Anne-Sophie Lartigot Campin1, Thibaud Saos1, Tony Chevalier1, Véro
• Figure 1. The Caune de l’Arago cave (Tautavel, Oriental Pyrenees, France).
• Figure 2. A conceptual model of research.
• Figure 3. Example of a part of the cognitive map of the occupation duration and site function scenario (red: general archaeological considerations, yellow: faunal raw data and indicators, blue: lithics raw data and indicators, grey: constructs and concept
• Figure 4. 2D visualization of high dimensional data with TSNE.
• Figure 5. Biome’s predictions in the stratigraphic levels of Caune de l’Arago cave.
• Figure 6. Variable importance plot for the level O.
• Figure 7. Visualization (force plot) of the prediction’s explanation of the majority class from the level J.
• Figure 8. Result of prediction classifying by short (C, green), not short (NC) output class. The training set has learnt on three expert labels: short, not short, and unknown (?) assigned and predicted on the whole layers (red: concordant predictions). Th
• Figure 10. Principle of the triple loop learning in AI.
• Cagny-l’Épinette (Somme valley, France), Thirty Years of Mixed Data: Potential and Limits
• Floriane Peudon1, Éric Masson2, Patrick Auguste3, Agnès Lamotte1, Anne-Marie Moigne4, Alain Tuffreau1
• Figure 1. 3D-Model of the location of the site of Cagny-l’Épinette, Somme Valley, Northern France (Map: Modified after d-map.com; 3D-Model: Modified after Marcy in Tuffreau et al. (2001)).
• Figure 2. Field recording protocol applied at the site of Cagny-l’Épinette from 1980 to 2010. A – Photo coverage by film photography in the 1980s, then by digital photography, B – Manual coordinate measurements in the 1980s, then replaced with a Total Sta
• Figure 3. Methodological protocol submitted to the archives of the site of Cagny-l’Épinette (BDD: A. Lamotte, A.-M. Moigne, P. Auguste; plans, montage & CAD: F. Peudon).
• Figure 4. Rule set applied on the digitized georeferenced maps with the OBIA-software eCognition: A – Raster digitized georeferenced plan, B – Initial Segmentation, C – Classification and Objects Merging, D – Removal of the landmarks, Background Merging a
• Figure 5. Overall digitalization of the archaeological vestiges of Level I1 overlapped by a corresponding scatter plot, both with their respective attribute tables linked through the ID Number. Grey: Digitized remains (Lithic and faunal remains), Red Dots
• Figure 6. Integrity assessment of the spatial information of the Level I1 with three identified main degrees of integrity: areas with complete spatial Data (field drawings and coordinates (x, y, z)), areas with either field drawings or coordinates (x, y,
• Réjane Roure1,2, Hakima Manseri1, Sébastien Munos2, Michel Py3
• Towards an Archaeological Information System: the evolution of Syslat, an archaeological data management software
• Figure 1.1. The Syslat software.
• Figure 2.1. Cover of Lattara 4.
• Figure 2.2. Cover of Lattara 10.
• Figure 3.1. A mind map of the Syslat architecture.
• François Djindjian
• L’archéologie néoprocessuelle
• Figure 1.
• Figure 2. Evolution des « cultures » du paléolithique supérieur européen depuis les débuts du stade isotopique 3 jusqu’à la fin du stade isotopique 2, illustrée par le modèle de Holling.
• Figure 3. Evolution des systèmes d’exploitation des territoires des groupes humains depuis les débuts du stade isotopique 3 jusqu’à la fin du stade isotopique 2, illustrée par le modèle de Holling.
• Figure 4. Evolution des procédés de fabrication de l’industrie lithique depuis les débuts du stade isotopique 8 jusqu’à la fin du stade isotopique 2, illustrée par le modèle de Holling.
• Figure 5. Processus concernés par la simulation globale d’une société complexe.
• Pascaline Gaussein
• Transcending ‘Technocomplexes’ When French Empiricism calls for Hypothetico-deductive Method
• Figure 1. Simplified framework for the interpretation of variability, homogeneity and change of material culture styles. After Wobst 1977; Wiessner 1986, 1990; Boer (de) 1990; Macdonald 1990; Gosselain 2000, 2011. CAD P. Gaussein.
• Figure 2. Contextualized framework for the interpretation of variability, homogeneity and change of material culture styles. After Wiessner 1990; Whallon 2006; Kelly 2013. CAD P. Gaussein.
• Figure 3. Schematic mapping of social unit territories depending on the application of human density: a. ‘archaeological application’ according to known archaeological settlements; b. and c. ‘global application’, archaeological map none taken into account
• List of Authors
• Back cover