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Abstract
This paper creates a conceptual frame and explanatory point of reference for the collection of papers presented at the exploratory workshop “Data Science for Digital Art History: Tackling Big Data Challenges, Algorithms, and Systems” organized at the KDD 2018 Conference in Data Mining and Knowledge Discovery held in London in August 2018. The goal of the workshop was to probe the field and to build a constructive interdisciplinary dialogue between two research areas: Data Science and Art History. The workshop’s chairs and the authors of this paper share the conviction that Data Science can enrich art studies while analysis of visual data can have a positive impact on Data Science. Thus, the research initiative tried to critically reflect on the interdisciplinary collaboration between diverse research communities and its epistemological and ontological effects.
DOI: https://doi.org/10.11588/dah.2019.4.72068
Authors
Ewa Machotka
is Associate Professor at the Department of Asian, Middle Eastern and Turkish Studies of the Stockholm University. She also serves as a member of the Young Academy of Sweden. She is an art historian specializing in Japan and East Asia. Formerly she served as Lecturer in the Art and Visual Culture of Japan at Leiden University and Curator of Japanese Art at the Museum of Far Eastern Antiquities in Stockholm, Sweden and the National Museum in Kraków, Poland.
Panagiotis Papapetrou
is Professor of Computer Science at the Department of Computer and Systems Sciences at Stockholm University and Adjunct Professor at the Computer Science Department at Aalto University. His area of expertise is algorithmic data mining with particular focus on mining and indexing sequential data, complex metric and nonmetric spaces, biological sequences, time series, and sequences of temporal intervals.