Linked Data for Cross-Domain Decision-making in Tourism
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Presentation done by Marta Sabou, Adrian M.P. Brașoveanu, & Irem Önder, during "Intelligence & analytics" workshop, of the ENTER2015 eTourism conference.
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Linked Data for Cross-Domain Decision-making in Tourism
- 1. ENTER 2015 Research Track Slide Number 1 Linked Data for Cross-Domain Decision-making in Tourism Marta Sabou, Adrian M.P. Brașoveanu, & Irem Önder MODUL University Vienna, Austria {marta.sabou, adrian.brasoveanu,irem.onder} @modul.ac.at http://www.modul.ac.at
- 2. ENTER 2015 Research Track Slide Number 2 Agenda • Motivation • Purpose of the study • Creating the ETIHQ linked data dataset and dashboard • Summary • Lessons learned
- 3. ENTER 2015 Research Track Slide Number 3 Motivation • Tourism decision making (i.e. benchmarking, forecasting) does not only depend on pure tourism statistics such as arrivals, bednights and capacity) but must also consider data from other domains. • For example: – Economic indicators (i.e. Inflation rate, unemployment rate, currency exchange rate) are interlinked with tourism consumption. – Environmental indicators (i.e. CO2 emissions) could be affected in heavily touristic areas.
- 4. ENTER 2015 Research Track Slide Number 4 Motivation cont. • Most tourism decision support systems usually only cater for investigating tourism indicators in isolation from economic or sustainability indicators. • Semantic Web and Linked Data (LD) technologies have been developed to support the intelligent integration of data on the Web (Berners-Lee et al., 2001).
- 5. ENTER 2015 Research Track Slide Number 5 What is Linked Data? • Linked Data technologies provide a mechanism to publish “intelligent” data on the Web and creating links between elements of different datasets, thus facilitating their integration.
- 6. ENTER 2015 Research Track Slide Number 6 Purpose of the study • To create the technical solution for providing TourMIS (tourism related database) as LD • To implement a cross-domain decision support dashboard • To reflect on the main lessons learned while using LD technology for tourism.
- 7. ENTER 2015 Research Track Slide Number 7 Related Work • Sources of Tourism Indicators: UN’s World Tourism Organization (UNWTO), Eurostat, TourMIS, The World Bank. • Tourism Decision Support Systems: TourMIS, PATA, Bastis
- 8. ENTER 2015 Research Track Slide Number 8 Sources of Tourism indicators UNWTO Eurostat WorldBank ETIHQ Country Y Y Y Y City N N N Y Year Y Y Y Y Month N Y N Y
- 9. ENTER 2015 Research Track Slide Number 9 Tourism decision support systems Coverage Indicator’s domain Technology Bastis Baltic Sea Region Mixed Wiki/community contributions Pata Asia Tourism Not known (commercial) ETIHQ Europe Tourism, economics, environment LD (QB)
- 10. ENTER 2015 Research Track Slide Number 10 Creating the ETIHQ Linked Data Dataset • Data Cleaning • Semantic Modeling • RDB2RDF conversion • Interlinking • Linked Data Interface Publishing
- 11. ENTER 2015 Research Track Slide Number 11 TourMIS - a key source of European Tourism Statistics Developed at … supported and used by several national and European tourism organisations Data since 1985 10+ indicators About 154 destinations Annual and monthly measurements Heterogeneous ownership Daily Updates
- 12. ENTER 2015 Research Track Slide Number 14 Indicators in ETIHQ • Economic Indicators – GDP growth (annual %); – Inflation, consumer prices (annual %); – Consumer price index (2005 = 100); – Official exchange rate (LCU per US$, period avg); – Unemployment rate, total (%of total labor force). • Sustainability Indicators – CO2 emissions (kt); – Forest area (% of land area); – Roads, paved (% of total roads); – Agricultural land (% of land area).
- 13. ENTER 2015 Research Track Slide Number 15 Example: German tourists in Venice, Budapest and Dubrovnik
- 14. ENTER 2015 Research Track Slide Number 16 Adding a new indicator to the list
- 15. ENTER 2015 Research Track Slide Number 17 Adding GDP to the chart
- 16. ENTER 2015 Research Track Slide Number 18 Summary • In this paper we described advances to the state of the art in terms of (1) publishing TourMIS data as linked data; (2) interlinking TourMIS data with data form other data sources covering the economic and statistical domains; (3) creating a visual dashboard that explores this integrated data to support cross-domain decision making processes.
- 17. ENTER 2015 Research Track Slide Number 19 Lessons Learned • LD technologies, greatly facilitate data integration at the syntactic and semantic level to establish links between various datasets. • Data compatibility (had to make changes to the TourMIS dataset before publishing it). • Licensing problems due to the heterogeneous origin of the TourMIS data set.
- 18. ENTER 2015 Research Track Slide Number 20 Thank you! irem.onder@modul.ac.at marta.sabou@modul.ac.at adrian.brasoveanu @modul.ac.at
Editor's Notes
- Understanding correlations between tourism and economic indicators, for example, can prepare tourism managers to plan their activities according to future financial predictions. Or, contrasting tourism and environmental indicators, as another example, can shed light on environmental impacts (e.g., increased CO2 emissions in heavily touristic areas) potentially caused by (mass)tourism in host countries thus helping to avoid them in the future. Therefore, such cross-domain queries are an important decision support instrument for tourism decision makers
- Most tourism decision support systems usually only cater for investigating tourism indicators in isolation from economic or sustainability indicators. The main reason for this is that technologically, merging data from various sources of indicators is challenging and relies on much manual effort MS: SW and LD technologies have been developed exactly for addressing such data integration challenges.
- However, to date no significant amount of tourism statistics have been published as linked data.
- MS: this is a new (more structured version) of the previous slide. See if you like it more
- Some examples of tourism decision support systems include DieToRecs, TourBO and MobyRec. However, the studies related to travel recommendation systems are directed towards the consumer (i.e., the individual tourists) rather than the tourism managers therefore focusing on different content (i.e., destinations, touristic offers, events). There are also yield management systems that are used mainly in hospitality and aviation industry.
- This slide depicts the main sources for tourism indicators and their key differences in terms of what data is provided and how. [Remove if this is too much detail] To important conclusions: Data access usually through techniques that hamper easy automatic integration TourMIS provides the most fine-grained data focusing on destinations and reporting monthly figures as opposed to most other sources that provide only contry level and yearly statistics So, let‘s talk about integration first. Sources of Tourism Indicators: UN’s World Tourism Organization (UNWTO), Eurostat, TOurMIS, The World Bank. Although there are various data sources that offer a multitude of indicators in the area of tourism and beyond, the publication of these datasets as LD has been primarily undertaken by third-parties rather than data owners, resulting in low-quality datasets that simply expose database tables into an RDF format without enriching them with domain-specific metadata or linking them to other datasets. Upon inspection, many of these datasets were slow or returned erroneous (or no) data
- Some examples of tourism decision support systems include DieToRecs, TourBO and MobyRec. However, the studies related to travel recommendation systems are directed towards the consumer (i.e., the individual tourists) rather than the tourism managers therefore focusing on different content (i.e., destinations, touristic offers, events). There are also yield management systems that are used mainly in hospitality and aviation industry.
- Data Cleaning – This step prepares the raw data for being published as Linked Data and it is especially needed for legacy databases. Semantic Modelling – Selecting vocabularies, creating ontologies and a structure for the dataset. RDB2RDF conversion – Databases are transformed into triples using the process defined in the Semantic Modelling step. Interlinking – Linked Data offers simple mechanisms, such as links, to extend a dataset with connections towards other linked datasets. Linked Data Interface Publishing – exposes the data of the Web both in a way that it can be browsed by people and queried programmatically.
- MS: remove D26.1 from the title
- MS: remove D26.1 from the title
- These are coming from TourMIS, WorldBank and EuroStat
- Let’s say that we want to see the development of German tourists in cities, we chose Venice, Budapest and Dubrovnik. We choose the time frame of dec 2008-dec 2014 (6 years). This data comes from TourMIS and it is bednights data. You can also ask to retrieve arrivals, capacity data as well. On the right hand side, the map shows the top city destinations German tourists went during this time frame. On the list under the chart, you can see the busiest month was in 2014-11 month in Brussels in terms of German tourists.
- We can add any indicator that we have on our system. For instance, I want to see Bednights in Vienna by German tourists. After I click save, it appears on the left hand side menu as an option. This is useful for destination management organization, which are usually reporting about their own destination and their competing cities, which can all be easily added to the list on the left. Each user can have a customized indicator list based on their specific needs.
- You can see that the number of bednights in Vienna from Japan shows a similar pattern with the GDP growth of Japan. AS the economy gets better, the number of tourists from Japan to Vienna also increase and vice a versa.
- Although the tourism domain heavily relies on complex decision-making, it is currently difficult to build decision support systems for this domain that would be capable of seamlessly integrating and visualising data from multiple data sources of tourism (and other) indicators. LD technologies, on the other hand, when adopted at large scale, greatly facilitate data integration at the syntactic and semantic level alike by providing a uniform data encoding format, as well as the possibility to clearly specify the meaning of the data and to establish links between various datasets.
- LD technologies, when adopted at large scale, greatly facilitate data integration at the syntactic and semantic level alike by providing a uniform data encoding format, as well as the possibility to clearly specify the meaning of the data and to establish links between various datasets. We had to make various changes to the TourMIS dataset before publishing it. We also encountered licensing problems due to the heterogeneous origin of the TourMIS data set. Although TourMIS is open to anyone who registers to the system, the data comes from different sources and is financed by multiple organizations. Therefore, opening the entire TourMIS data set as Open Data for third parties is an issue which has not yet received a satisfactory answer yet.