Abstract
The travel and tourism domain as a global competitive service business has a special need to understand the customer and market trends. Typically, available customer-based data is stored in data warehouses and analysed by either OLAP queries or data mining techniques. However, a more powerful approach is to combine these techniques and to integrate data mining results directly into the original data warehouse structures. This comprehensive data source builds the basis for further applications of business intelligence. This paper presents a novel approach to integrate data mining results into multi-dimensional data warehouse structures and to store data mining results with the original information. A first implementation for the leading Swedish mountain destination Åre has shown the advantages of this new concept: the end-user can now easily access data mining results by simple OLAP queries and even combine them with the original information stored in the data warehouse.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Blockeel, H., Calders, T., Fromont, E., Goethals, B., & Prado, A. (2008). Mining views: Database views for data mining. Leuven: Belgium.
Blockeel, H., Calders, T., Fromont, È., Goethals, B., Prado, A., & Robardet, C. (2010). Inductive querying with virtual mining views. Berlin: Springer.
Buhalis, D. (2003). ETourism: Information technology for strategic tourism management. Harlow: Pearson.
Calders, T., & Goethals, B. P. (2006). Integrating pattern mining in relational databases. Belgium: Antwerp.
Davies, D. L., & Bouldin, D. W. (2009). A cluster separation measure. In The IEEE transactions on pattern analysis and machine intelligence (TPAMI), pp. 224–227.
Fayyad, U., Grinstein, G. G., & Wierse, A. (2002). Information visualization in data mining and knowledge discovery (1st ed.). San Francisco, CA: Morgan Kaufmann.
Fromont, É., Blockeel, H., & Struyf, J. (2007). Integrating decision tree learning into inductive databases (Lecture notes in computer science, Vol. 4747, pp. 81–96). Leuven: Springer.
Fuchs, M., Höpken, W., & Lexhagen, M. (2015). Big data analytics for knowledge generation in tourism destinations – A case from Sweden. Journal of Destination Marketing and Management. doi:10.1016/j.jdmm.2014.08.002.
Han, J., Fu, Y., Wang, W., Koperski, K., & Zaiane, O. (1996). DMQL: A data mining query language for relational databases. Burnaby: Simon Fraser University.
Han, J., Kamber, M., & Pei, J. (2006). Data mining: Concepts and techniques (2nd ed.). Waltham: Morgan Kaufmann.
Höpken, W., Fuchs, M., Höll, G., Keil, D., & Lexhagen, M. (2013). Multi-dimensional data modelling for a tourism destination data warehouse. In L. Cantoni & P. Xiang (Eds.), Information and communication technologies in tourism 2013 (pp. 157–169). New York: Springer.
Höpken, W., Fuchs, M., & Lexhagen, M. (2014). The knowledge destination – Applying methods of business intelligence to tourism destinations. In J. Wang (Ed.), Encyclopaedia of business analytics and optimization (pp. 307–321). IGI Global: Hershey.
Imielinski, T. V. (1999). MSQL: A query language for database mining. Data Mining and Knowledge Discovery, 3, 373–408.
Kimball, R., & Ross, M. (2002). The data warehouse toolkit: The complete guide to dimensional modeling. New York: Wiley.
Kramer, S., Aufschild, V., Hapfelmeier, A., Jarasch, A., Kessler, K., Reckow, S., et al. (2006). Inductive databases in the relational model: The data as the bridge. Heidelberg: Springer.
Law, R. (1998). Room occupancy rate forecasting: A neural network approach. International Journal of Contemporary Hospitality Management, 6(10), 234–239.
Lexhagen, M., Kuttainen, C., Fuchs, M., & Höpken, W. (2012). Destination talk in social media: A content analysis for innovation. In E. Christou, D. Chionis, D. Gursory, & M. Sigala (Eds.), Advances in hospitality and tourism marketing & management. Greece: Corfu. ISBN 978-960-287-139-3.
Liu, B. (2011). Web data mining: Exploring hyperlinks, contents, and usage data (2nd ed.). Berlin: Springer.
Meo, R., Psaila, G., & Ceri, S. (1998). An extension to SQL for mining association rules (pp. 195–224). Boston, MA: Kluwer.
Morales, D. R., & Wang, J. (2008). Passenger name record data mining based cancellation forecasting for revenue management. Innovative Applications of OR, 2(202), 554–562.
Pyo, S., Uysal, M., & Chang, H. (2002). Knowledge discovery in database for tourist destinations. Journal of Travel Research, 4(40), 374–384.
Schmunk, S., Höpken, W., Fuchs, M., & Lexhagen, M. (2014). Sentiment analysis – Extracting decision-relevant knowledge from UGC. In Z. Xiang & I. Tussyadiah (Eds.), Information and communication technologies in tourism (pp. 253–265). Heidelberg: Springer.
Vlahogianni, E. I., & Karlaftis, M. G. (2010). Advanced computational approaches for predicting tourist arrivals: The case of charter air-travel. In T. Evans (Ed.), Nonlinear-dynamics (pp. 309–324). Croatia: INTECH.
Wallace, M., Maglogiannis, I., Karpouzis, K., Kormentzas, G., & Kollias, S. (2004). Intelligent one-stop-shop travel recommendations using an adaptive neural network and clustering of history. Information Technology and Tourism, 3(6), 181–193.
Wong, J.-Y., Chen, H.-J., Cung, P.-H., & Kao, N.-C. (2006). Identifying valuable travellers by the application of data mining. Asia-Pacific Journal of Tourism Research, 4(11), 355–373.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Meyer, V., Höpken, W., Fuchs, M., Lexhagen, M. (2015). Integration of Data Mining Results into Multi-dimensional Data Models. In: Tussyadiah, I., Inversini, A. (eds) Information and Communication Technologies in Tourism 2015. Springer, Cham. https://doi.org/10.1007/978-3-319-14343-9_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-14343-9_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14342-2
Online ISBN: 978-3-319-14343-9
eBook Packages: Business and EconomicsBusiness and Management (R0)