A data mining approach for indoor air assessment, an alternative tool for cultural heritage conservation

Florence Heri-Tech - The Future of Heritage Science and Technologies

Pernia, D Leyva^1,4 ; Demeyer, S.^1 ; Schalm, O.^2 ; Anaf, W.^3

Department of Mathematics-omputer Science, University of Antwerp, Middelheimlaan 1, Antwerp

2020, Belgium^1

Conservation Studies, University of Antwerp, Blindestraat 9, Antwerp

2000, Belgium^2

War Heritage Institute, Renaissancelaan 30, Brussels

B-1000, Belgium^3

Department of Physics, CEADEN, Calle 30, 502, Havana

11300, Cuba^4

关键词: Cultural heritage studies;    Environmental parameter;    High-speed processing;    Historical artefacts;    Indoor air assessment;    K-means clustering method;    Tool for cultural heritages;    Traditional assessment;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/364/1/012045/pdf
DOI  :  10.1088/1757-899X/364/1/012045

来源: IOP

【 摘 要 】

The exposure of cultural heritage to the environment has a significant impact on its degradation process and degradation rate. Consequently, managing the indoor air quality is vital to minimize further damage to historical artefacts and works of art. Despite its potential impact, the traditional assessment of the indoor air quality still represents a challenge for most collection guardians. This approach typically relays on the comparison of measured environmental parameters and corresponding acceptable values. However, determining the acceptable values and relative importance of the different environmental parameters turns out to be quite complex since it depends on the material types present in the collection and their preservation state. Furthermore, the significant amount of data generated during the measurements hampers the application of traditional methods of analysis. Considering all these, we propose the use of data mining as an alternative method for the indoor air quality assessment in cultural heritage studies. Data mining can provide knowledge from vast volumes of heterogeneous data, through high-speed processing, detection, and analysis. Here we present its application to identify dynamics and patterns affecting the indoor air quality in a realistic case. Using data from a measuring campaign held at a late Gothic church in Belgium, we show that inappropriate periods can be identified without using standards. In addition, different types of periods can be identified by studying the relation between multiple parameters. For that we use the k-means clustering method, interpreting the results with both visual and statistical tools.