1. Constructing a Data Warehouse
Based Decision Support Platform for
China Tourism Industry
Xiangjie Qiao, Lingyun Zhang, Nao Li and Wei Zhu
Institute of Tourism
Beijing Union University, China
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2. Content
• Background
• Related research
• System introduction
• Conclusion and future work
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3. Background
• in-depth studies on changes and impacts of big data from
the view of management and intelligent decision-making
are very limited (Feng, Guo, Zeng, Chen & Chen, 2013)
• The application of big data in the tourism industry,
particularly by tourism enterprises, is even more limited
and very rare in industrial development policy making by
government
– FlightCaster analyzed the past ten years of flight data to forecast whether
a flight would be late (Mayer & Kenneth,2013)
– Teradata eCircle company used data and applications to keep London
moving during the 2012 Olympic and Paralympic Games (McDonald, 2013)
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4. Background
• China’s tourism public management and
service is facing a big challenge
– The numbers of tourists often exceeds the
capacity of popular tourist attractions
– Individual backpack travelers often face an
information flood; they needs more
personalized information and services
– Some problems like resource depletion,
pollution, and worsening ecological
environment have become the key issues for
industry’s sustainable development
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5. Background
• Data is rich for decades of industry development
(data rich but information poor)
• BI has been effectively used in enterprises for
managers’ decision making, which can also applied
in government policy making to assist tourism public
management and service
• Make full use of the data exists in tourism industry
–
–
–
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Tourism safety emergency command
Tourism demand forecasting
Tourism resources carrying capacity monitoring
Public information services
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6. Content
• Background
• Related research
• System introduction
• Conclusion and future work
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7. Related research
• Data Warehouse for tourism industry
– Although huge amounts of data are available at tourism
destinations, these valuable knowledge sources typically remain
unused
(Danubianu, Socaciu & Barila, 2009; Hendawi & El-Shishiny, 2008)
– There exists high demand in electronic publishing of market research
results in the tourism industry (Wӧber, 1998)
– Some tourism data warehouse prototypes were built with various
indicators. (Hendawi & El-Shishiny,2008; Hӧpken, Fuchs, Hӧll, Keil & Lexhagen, 2013)
– It is still not popular to develop decision support systems for government
or industry managers
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8. Related research
• Data mining application in tourism industry
– automatic predicting trends or behaviors , association analysis ,
clustering, concept description and deviation detection
• Tourism demand forecast
– traditional methods: delphi, time series, econometric
methods
– modern methods: artificial neural network, rough set, fuzzy
time series, grey theory and support vector machine;
Neural network often outperform others
– compared to the traditional econometric or statistical
modeling techniques, data mining is still at its infancy(Law et
al., 2007)
• Other methods are more widely used for tourism marketing:
market segment , cross selling, and CRM
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9. Related research
• Data mining application in tourism industry
– Web mining is to find interesting patterns from
hyperlinks, web content and web logs (Wӧber, 2007;
Pitman, Zanker, Fuchs & Lexhagen, 2010)
• building a personalized web site
• clustering customers
• improving customer loyalty
• destination or products recommendations
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10. Content
• Background
• Related research
• System introduction
• Conclusion and future work
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11. Technical architecture
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12. Indicators and data source
Primary
Indicators
Secondary Indicators
Data Source
market
inbound tourism market
outbound tourism market
domestic tourism market
international tourism market
national tourism administration
national tourism administration
national tourism administration
UNWTO, tourism official web site of
the country
industry
employment
tourism investment
enterprise direct reporting systems
national
tourism
administration,
enterprise direct reporting systems
accommodation for visitors in hotels and enterprise direct reporting systems
similar establishments
travel agencies
tourist attractions
enterprise direct reporting systems
listed companies
economy
enterprise direct reporting systems
listed companies' Quarterly report
GDP, exchange rate,
disposable
income,
economic indicators
per capita national bureau of statistics, the world
international bank, IMF, the people's bank of china,
OECD, UNCTAD
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13. Implementation process
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14. Implementation process
The logic structure of arrivals for accommodation
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15. Implementation process
Dimension level for regions/countries
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16. Application cases
Multi-dimensional report viewed by Five Brics
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17. Application cases
Example for the correlation between two charts
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18. Application cases
Drill path analysis in management cockpit
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19. Content
• Background
• Related research
• System introduction
• Conclusion and future work
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20. Conclusion and future work
• The greatest difficulty doesn’t lie at the
technical level, but rather at the needs
analysis level to organize the data well
• Data preparation and validation are tedious
tasks during the platform’s implementation
• The system is still in development,
evaluations should be done to test its
effectiveness and get some revisions
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21. Conclusion and future work
• To make full use of the data
– Developing applications such as tourism early
warning and monitoring system, tourism
demand forecasting
– Modeling some indexes of the industry such as
industry climate index, tourism
competitiveness index, tourism purchasing
index
• To collect more external data into the data
warehouse
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22. Xiangjie Qiao
Institute of Tourism
Beijing Union University, China
lytxiangjie@buu.edu.cn
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