
Data Mining Workspace Sensors
Author(s) -
Ajda Pretnar,
Dan Podjed
Publication year - 2019
Publication title -
prispevki za novejšo zgodovino
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.213
H-Index - 4
eISSN - 2463-7807
pISSN - 0353-0329
DOI - 10.51663/pnz.59.1.09
Subject(s) - merge (version control) , ethnography , data science , lagging , workspace , computer science , qualitative property , sociology , anthropology , artificial intelligence , information retrieval , machine learning , mathematics , statistics , robot
While social sciences and humanities are increasingly including computational methods in their research, anthropology seems to be lagging behind. But it does not have to be so. Anthropology is able to merge quantitative and qualitative methods successfully, especially when traversing between the two. In the following contribution, we propose a new methodological approach and describe how to engage quantitative methods and data analysis to support ethnographic research. We showcase this methodology with the analysis of sensor data from a University of Ljubljana’s faculty building, where we observed human practices and behaviours of employees during working hours and analysed how they interact with the building and their environment. We applied the proposed circular mixed methods approach that combines data analysis (quantitative approach) with ethnography (qualitative approach) on an example of a “smart building” and empirically identified the main benefits of the new anthropological methodology.