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andafter travel.Mostof thisdata isexternal: forexample, in the formofTwitterorother
social networking feeds.Thefirst challenge is thedifficultyof identifying the right data
anddetermininghow touse it best since the proliferation of a large amount of unstruc-
tured and structured tourism-related open data (see Table 1). The second challenge is
to find the rightmix of theory and technology capable of interpreting the data to find
meaningful tourisminsights, and the third is toovercometheobstacleofdataaccessand
connectivity,which requires the rightplatforms toaggregateandmanagebigdata.
Table1. Categorizationofopen tourismdataextracted from[1].
Opendata type Description Wheredatahasbeenused
Geographicdata GPS-locations Mobileapplications,websites
Eventdata Descriptionofevents,bandsplaying, timeta-
bles, even type Mobileapplications,websites
Visitor statistics Numberofovernights Mobileapplications,websites
Supply statistics Number of businesses, types of businesses,
number and information on attractions and
museums Mobileapplications,websites
Surveydata Data fromsurveystudies Mobile applications,websites, aca-
demicandbusiness research
Supply information Information on travel destinations, attrac-
tions, restaurants andhappenings Mobile applications,websites, aca-
demicandbusiness research
Transit data Timetables Mobileapplications,websites
Governmentaldata Taxdistributionandcollection Mobile applications,websites, aca-
demicandbusiness research
Allof above Smart Tourism City, augmented
reality applications, services that
combinedata fromseveral sources
2.2. Analysis
Toturnavastamountof informationintoasemanticknowledgeinterconnectedorganiza-
tionstructure,whichcanbeanalyzed indepth to identifypatternsandanomalies indata-
drivenprocesses andnetworks, canbe a considerable challenge. In otherwords, it is to
knowhowtoextractdataandinformationfromdifferentsources, linkingit, integrate into
a domain ontology and to include it in a graph-orientedKnowledgeBase. To this end,
someapproachesarebeingpursued that canbeused in thecontextof thiswork. Indeed,
a setof technologiescouldbeused toconstruct this typeofgraphs, ranging fromgraph-
orienteddatastorageandmanagementbuiltonrelationalandnon-relationaldatabases to
semantic approachesmade on SemanticWebTechnologies. They are becoming nowa-
days substantial assets for representing storing, sharing, and accessing information and
domain-knowledge. Examples of graph analyzing techniques such as graph neural net-
works [6]anddeep learninggraphs [7]can thusbeusefullyapplied toclassifyand learn
fromgraph inputs.
M.GomesandP.Novais /AData-DrivenTool toPromoteTourism
forPeoplewithDisabilities48
Intelligent Environments 2019
Workshop Proceedings of the 15th International Conference on Intelligent Environments
- Title
- Intelligent Environments 2019
- Subtitle
- Workshop Proceedings of the 15th International Conference on Intelligent Environments
- Authors
- Andrés Muñoz
- Sofia Ouhbi
- Wolfgang Minker
- Loubna Echabbi
- Miguel Navarro-Cía
- Publisher
- IOS Press BV
- Date
- 2019
- Language
- German
- License
- CC BY-NC 4.0
- ISBN
- 978-1-61499-983-6
- Size
- 16.0 x 24.0 cm
- Pages
- 416
- Category
- Tagungsbände