Big Data Analytics for Cultural Heritage, Volume II

[special issue]

Full reference

Manolis Wallace, Vassilis Poulopoulos, Angeliki Antoniou and Martín López-Nores (editors), Big Data Analytics for Cultural Heritage, Volume II, Big Data and Cognitive Computing, in preparation

Abstract

Although big data was initially coined as a term to represent our inability to manage and process the volumes of data that we record, recent advances in both the technological and algorithmic frontier have led to the development of the field of big data analytics. Big data analytics, i.e., methods and applications designed specifically to operate with vast data sets, have become widely accepted as general-purpose tools that can be applied to any domain.

As such, we have seen the same, or very similar, big data analytics tools applied to fields such as social media, economics, biomedicine, smart cities, and so on. The caveat here is that the meaning of the data is not being considered in the process, such as in the case of deep learning, even if some data structures, such as word embeddings, do reflect structures of meaning.

Cultural heritage, on the other hand, is a domain that produces vast amounts of data but also where the meaning of the data is crucially important in its handling; particularly to the extent that it refers to people’s opinions, perceptions, and interpretations of their past and their present, or to people’s feelings, preferences, and attitudes.

In this Special Issue, we focus on big data analytics methods and tools that have been specifically developed for the domain of cultural heritage, as well as on experiences from the adaptation and/or application of general-purpose solutions to the domain of cultural heritage. The aim is to gather solutions, but also lessons learnt, methodologies, and good practices, that researchers and practitioners can use as a basis for their own work in the domain.