Towards a semantic indoor trajectory model: application to museum visits
| Autor: | Claudia Marinica, Karine Zeitouni, Dimitris Kotzinos, Dan Vodislav, Alexandros Kontarinis |
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| Přispěvatelé: | Données et algorithmes pour une ville intelligente et durable - DAVID (DAVID), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Equipes Traitement de l'Information et Systèmes (ETIS - UMR 8051), Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA)-Centre National de la Recherche Scientifique (CNRS)-CY Cergy Paris Université (CY), Horizon 2020 Framework Programme, H2020: 777695, This work was supported by the TRAJECTOIRES project funded by the Paris Seine Graduate School Humanities, Creation, Heritage, Investissement d’Avenir ANR-17-EURE-0021 – Foundation for Cultural Heritage Science., Karine Zeitouni’s work in this paper was supported by the MASTER project that has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie-Slodowska Curie grant agreement N. 777695. |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Computer science
media_common.quotation_subject Geography Planning and Development Big data Museum visitor study 02 engineering and technology Space (commercial competition) Article 020204 information systems 0202 electrical engineering electronic engineering information engineering [INFO]Computer Science [cs] Sequential Pattern Mining Representation (mathematics) media_common Information retrieval business.industry Semantic trajectory Indoor trajectory Analytics Model application Trajectory pattern mining Trajectory Conceptual model 020201 artificial intelligence & image processing Trajectory modeling Louvre museum business Information Systems |
| Zdroj: | Geoinformatica Geoinformatica, 2021, 25 (2), pp.311-352. ⟨10.1007/s10707-020-00430-x⟩ |
| ISSN: | 1384-6175 |
| DOI: | 10.1007/s10707-020-00430-x⟩ |
| Popis: | International audience; In this paper we present a new conceptual model of trajectories, which accounts for semantic and indoor space information and supports the design and implementation of context-aware mobility data mining and statistical analytics methods. Motivated by a compelling museum case study, and by what we perceive as a lack in indoor trajectory research, we combine aspects of state-of-the-art semantic outdoor trajectory models, with a semantically-enabled hierarchical symbolic representation of the indoor space, which abides by OGC’s IndoorGML standard. We drive the discussion on modeling issues that have been overlooked so far and illustrate them with a real-world case study concerning the Louvre Museum, in an effort to provide a pragmatic view of what the proposed model represents and how. We also present experimental results based on Louvre’s visiting data showcasing how state-of-the-art mining algorithms can be applied on trajectory data represented according to the proposed model, and outline their advantages and limitations. Finally, we provide a formal outline of a new sequential pattern mining algorithm and how it can be used for extracting interesting trajectory patterns. |
| Databáze: | OpenAIRE |
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