Mining big data in tourism

Carmela Iorio (), Giuseppe Pandolfo (), Antonio D’Ambrosio () and Roberta Siciliano ()
Additional contact information
Carmela Iorio: University of Naples Federico II
Giuseppe Pandolfo: University of Naples Federico II
Antonio D’Ambrosio: University of Naples Federico II
Roberta Siciliano: University of Naples Federico II

Quality & Quantity: International Journal of Methodology, 2020, vol. 54, issue 5, No 17, 1655-1669

Abstract: Abstract Knowledge discovery from various sources of information based on different data types for decision and accurate prediction can be rather complex and costly without a statistical information system. In Big Data Era, Statistical Tourism Observatory needs to be revised. This paper introduces a conceptual model of Digital Tourism System (DTS) where various types of standard and non-standard data can be processed by actors and spectators in tourism sector. Particularly, big data can be very useful and the figure of Data Scientist within the tourism industry becomes prominent. DTS allows to emphasize four knowledge areas of interest for different purposes, specifically, destination management, research and innovation, market analysis, labor market, in order to improve tourism management and research. Key steps of the knowledge discovery pyramid are exploited to provide an added value in decision-making on the basis of statistical learning methods. Two examples are shown, mining online textual and photo data respectively.

Keywords: Big data; Data mining; Tourism research; Statistical tourism observatory; Statistical learning (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://link.springer.com/10.1007/s11135-019-00927-0 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:qualqt:v:54:y:2020:i:5:d:10.1007_s11135-019-00927-0

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11135

DOI: 10.1007/s11135-019-00927-0

Access Statistics for this article

Quality & Quantity: International Journal of Methodology is currently edited by Vittorio Capecchi

More articles in Quality & Quantity: International Journal of Methodology from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().