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|Title:||Applying Statistics and Computer Science to the Study of Big Coin Finds: An Engineering Approach||Authors:||Gianazza, Luca||Keywords:||Coin hoards; Statistics; Computer Science; point estimators; interval estimators; linked data; open data; semantic web; speech recognition software||Issue Date:||2019||Publisher:||EUT Edizioni Università di Trieste||Source:||Luca Giannazza, "Applying Statistics and Computer Science to the Study of Big Coin Finds: An Engineering Approach", in: Bruno Callegher (Edited by), “Too Big to Study? Troppo grandi da studiare?”, Trieste, EUT Edizioni Università di Trieste, 2019, pp. 131-159||Abstract:||
Any large amount of data raises processing and interpretation issues. Coin finds, particularly hoards made of several thousand pieces, are no exception. In front of a great number of specimens, a comprehensive study, conducted with methods usually applied to small finds, becomes a difficult target to achieve. Statistics, as well as Computer Science, can provide important analysis tools and solutions allowing the researchers to extract relevant information from finds data. This contribution will examine how Statistics and Computer Science can support the work of numismatists. It will present at an introductory level what is still available today and what could become affordable hopefully not too far in the future, going through the major pros and cons. It will be shown how large and articulated amounts of data – from denominations of coins to the mints of origin, from image descriptions to weights and diameters – can be managed and organized in a smart way along with coin images into a structured information system.
The analysis will be carried out under an engineering perspective, always focusing on aspects such as application limits, implementation costs and the effort required in terms of human resources.
|URI:||http://hdl.handle.net/10077/24669||ISBN:||978-88-5511-016-7||eISBN:||978-88-5511-017-4||Rights:||Attribution-NonCommercial-NoDerivatives 4.0 Internazionale|
|Appears in Collections:||11 Too Big to Study? Troppo grandi da studiare?|
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