Mining big data in tourism
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Mining big data in tourism Carmela Iorio1 · Giuseppe Pandolfo1 · Antonio D’Ambrosio2 · Roberta Siciliano1
© Springer Nature B.V. 2019
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
1 Introduction Nowadays, data can be easy and cheap, information may be available in real time, the knowledge discovery from different sources of information for decision and accurate prediction can be rather complex and costly without a statistical information system. This paper provides a conceptual model of Digital Tourism System for monitoring with an overview of the different sources of big data as well as data mining processes in tourism sector. * Roberta Siciliano [email protected] Carmela Iorio [email protected] Giuseppe Pandolfo [email protected] Antonio D’Ambrosio [email protected] 1
Department of Industrial Engineering, University of Naples Federico II, Naples, Italy
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Department of Economics and Statistics, University of Naples Federico II, Naples, Italy
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Fundamental issues are related to the amount of the data and its quality as well. These both significantly affect the results of a knowledge discovery process. Specifically, the volume of data is connected with the scalability of the data mining techniques while the data are continuously updated and modified because of its changing nature. Obviously, data quality plays a prominent role. Indeed, precision, completeness and redundancy are all related to the quality of the data. All the above mentioned issues are thus to be taken into account before and during the analysis of the data, otherwise the impact on the knowledge discovery process outcome may be seriously affected in a negative way. Interpreting the unstructured data and acquiring relevant insights is a big challenge facing the tourism industry. Big data can be a very useful source of information for the tourism sector. Anyway, it needs to combine modern statistics and tech
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