The knowledge destination - a customer information-based destination management information system

Huge amounts of customer-based data, such as tourists’ website navigation, transaction and survey data are available in tourism destinations, however, remain largely unused (Pyo, 2002). This paper presents the concept of a knowledge-based destination management information system (DMIS) that supports value creation through enhanced decision making. Information is extracted from heterogeneous data sources of the Swedish tourism destination of Are and is categorized in explicit feedback (e.g. survey data) and implicit information traces (e.g. navigation and transaction data). Methods of business intelligence are applied to retrieve interesting data patterns, thus, to generate knowledge in the form of empirically validated models. The paper deduces new insights about the applicability of data mining techniques and related models at tourist destinations depending on the type of tourism data and concrete problem characteristics at hand (Pick & Schell, 2002).

[1]  Wolfram Höpken,et al.  Context-based Adaptation of Ubiquitous Web Applications in Tourism , 2008, ENTER.

[2]  M. Gordon,et al.  PUBLICATION RECORDS AND TENURE DECISIONS IN THE FIELD OF STRATEGIC MANAGEMENT , 1996 .

[3]  David B. Klenosky.  The “Pull” of Tourism Destinations: A Means-End Investigation , 2002 .

[4]  James H. Martin,et al.  Speech and language processing: an introduction to natural language processing, computational linguistics, and speech recognition, 2nd Edition , 2000, Prentice Hall series in artificial intelligence.

[5]  Ignacio Olmeda,et al.  Data Mining Techniques and Applications for Tourism Internet Marketing , 2002 .

[6]  Sungsoo Pyo,et al.  Knowledge Discovery in Database for Tourist Destinations , 2002 .

[7]  Vincent Cho,et al.  Towards Using Knowledge Discovery Techniques in Database Marketing for the Tourism Industry , 2002 .

[8]  Bing Liu,et al.  Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data , 2006, Data-Centric Systems and Applications.

[9]  Hinrich Schütze,et al.  Book Reviews: Foundations of Statistical Natural Language Processing , 1999, CL.

[10]  Carol Saunders,et al.  EDITOR’S COMMENTS Volume 29 Iss. 4 , 2005 .

[11]  Markus Zanker,et al.  Automated Semantic Annotations of Tourism Resources Based on Geospatial Data , 2009, J. Inf. Technol. Tour..

[12]  Matthias Fuchs,et al.  Data Mining im Tourismus : Theoretische Grundlagen und Anwendungen in der Praxis , 2009 .

[13]  Wolfram Höpken,et al.  Exploiting Semantic Web Technologies for Harmonizing E-Markets , 2004, J. Inf. Technol. Tour..

[14]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[15]  Stefan Berger,et al.  Location-Based Services in the Tourist Industry , 2002, J. Inf. Technol. Tour..

[16]  U. Gretzel,et al.  Role of social media in online travel information search , 2010 .

[17]  Hokey Min,et al.  A data mining approach to developing the profiles of hotel customers , 2002 .

[18]  Youcheng Wang,et al.  Conceptualizing and evaluating the functions of destination marketing systems , 2007 .

[19]  J. R. Brent Ritchie,et al.  Determinants of tourism success for DMOs & destinations: An empirical examination of stakeholders' perspectives , 2010 .

[20]  Robin J. B. Ritchie,et al.  A framework for an industry supported destination marketing information system , 2002 .

[21]  Sungsoo Pyo,et al.  Knowledge map for tourist destinations—needs and implications , 2005 .

[22]  Maria Lexhagen,et al.  Web Usage Mining in Tourism - A Query Term Analysis and Clustering Approach , 2010, ENTER.

[23]  U. Gretzel,et al.  Information and Communication Technologies in Tourism 2010 , 2010 .

[24]  Olfa Nasraoui,et al.  Web Usage Mining , 2011 .

[25]  Ulrike Gretzel,et al.  Implementing a Knowledge-Based Tourism Marketing Information System: The Illinois Tourism Network , 2003, J. Inf. Technol. Tour..