Stop-and-move sequence expressions over semantic trajectories

References

• Alvares, L.O., et al. 2007a. Dynamic modeling of trajectory patterns using data mining and reverse engineering. In: 26th international conference on conceptual modeling, poster session. Auckland, New Zealand: ACM, vol. 83, 149–154.
   Google Scholar
• Alvares, L.O., et al. 2007b. A model for enriching trajectories with semantic geographical information. In: 15th annual ACM international symposium on advances in geographic information systems. Auckland, New Zealand: ACM, 22.
   Google Scholar
• Baader, F., et al. eds., 2003. The description logic handbook: theory, implementation, and applications. USA: Cambridge University Press.
   Google Scholar
• Baglioni, M., et al. 2008. An ontology-based approach for the semantic modelling and reasoning on trajectories. In: International conference on conceptual modeling. LNCS. Berlin, Heidelberg: Springer, vol. 5232, 344–353.
   Google Scholar
• Bast, H., Buchhold, B., and Haussmann, E., 2016. Semantic search on text and knowledge bases. Foundations and Trends® in Information Retrieval, 10 (1), 119–271. doi:10.1561/1500000032
   Google Scholar
• Bergamaschi, S., et al. 2016. Combining user and database perspective for solving keyword queries over relational databases. Information Systems, 55, 1–19. doi:10.1016/j.is.2015.07.005.
   Web of Science ®Google Scholar
• Bogorny, V., et al. 2014. Constant – a conceptual data model for semantic trajectories of moving objects. Transactions in GIS, 18 (1), 66–88. doi:10.1111/tgis.12011
   Web of Science ®Google Scholar
• Brilhante, I., et al., 2014. TripBuilder: a tool for recommending sightseeing tours. In: 36th European Conf. on Information Retrieval (ECIR’14). Springer, Cham, 771–774.
   Google Scholar
• Fileto, R., et al. 2013. Baquara: A holistic ontological framework for movement analysis using linked data. In: International conference on conceptual modeling. LNCS. Springer, Berlin, Heidelberg, vol. 8217, 342–355.
   Google Scholar
• Fileto, R., et al. 2015. The baquara2 knowledge-based framework for semantic enrichment and analysis of movement data. Data & Knowledge Engineering, 98, 104–122. doi:10.1016/j.datak.2015.07.010.
   Web of Science ®Google Scholar
• Furtado, A.S., et al. 2016. Multidimensional similarity measuring for semantic trajectories. Transactions in GIS, 20 (2), 280–298. doi:10.1111/tgis.12156
   Web of Science ®Google Scholar
• García, G.M., et al. 2017. RDF keyword-based query technology meets a real-world dataset. In: 20th International Conference on Extending Database Technology, Venice, Italy. OpenProceedings.
   Google Scholar
• Ghanbarpour, A. and Naderi, H., 2019. A model-based keyword search approach for detecting top-k effective answers. The Computer Journal, 62 (3), 377–393. doi:10.1093/comjnl/bxy056
   Web of Science ®Google Scholar
• Han, S., et al. 2017. Keyword search on RDF graphs - A query graph assembly approach. In: ACM Conference on Information and Knowledge, CIKM 2017, Singapore, Singapore. ACM Press, vol. Part F1318, 227–236.
   Google Scholar
• Hristidis, V. and Papakonstantinou, Y., 2002. Discover: keyword search in relational databases. In: 28th International Conference on Very Large Databases (VLDB’02). Hong Kong, China: VLDB Endowment, 670–681. Available from: https://linkinghub.elsevier.com/retrieve/pii/B9781558608696500652
   Google Scholar
• Hu, Y., et al. 2013. A geo-ontology design pattern for semantic trajectories. In: International Conference on Spatial Information Theory. LNCS. Cham: Springer, vol. 8116. 438–456.
   Google Scholar
• Izquierdo, Y.T., et al. 2018. QUIOW: A keyword-based query processing tool for RDF datasets and relational databases. In: 30th International Conference on Database and Expert Systems Applications (DEXA’18), Regensburg, Germany. Springer, vol. 11030, 259–269.
   Google Scholar
• Le, W., et al. 2014. Scalable keyword search on large RDF data. IEEE Transactions on Knowledge and Data Engineering, 26 (11), 2774–2788. doi:10.1109/TKDE.2014.2302294
   Web of Science ®Google Scholar
• Lin, X.Q., Ma, Z.M., and Yan, L., 2018. RDF keyword search using a type-based summary. Journal of Information Science and Engineering, 34 (2), 489–504.
   Web of Science ®Google Scholar
• Oliveira, P.D., Silva, A.D., and Moura, E.D., 2015. Ranking Candidate Networks of relations to improve keyword search over relational databases. In: 31st IEEE International Conference on Data Engineering (ICDE’15), Seoul, S. Korea. IEEE, 399–410.
   Google Scholar
• Parent, C., et al. 2013. Semantic trajectories modeling and analysis. ACM Computing Surveys (CSUR), 45 (4), 42. doi:10.1145/2501654.2501656
   Web of Science ®Google Scholar
• Petry, L.M., et al. 2019. Towards semantic-aware multiple-aspect trajectory similarity measuring. Transactions in GIS, 23 (5), 960–975. doi:10.1111/tgis.12542
   Web of Science ®Google Scholar
• Renso, C., et al. 2013a. How you move reveals who you are: understanding human behavior by analyzing trajectory data. Knowledge and Information Systems, 37 (2), 331–362. doi:10.1007/s10115-012-0511-z
   Web of Science ®Google Scholar
• Renso, C., Spaccapietra, S., and Zimányi, E., 2013b. Mobility data. Cambridge: Cambridge University Press.
   Google Scholar
• Rihany, M., Kedad, Z., and Lopes, S., 2018. Keyword search over RDF graphs using wordnet. In: 1st International Conference on Big Data and Cyber-Security Intelligence (BDCSIntell’18), Hadath, Lebanon. CEUR-WS, vol. 2343, 75–82.
   Google Scholar
• Santipantakis, G.M., et al. 2017. Specification of semantic trajectories supporting data transformations for analytics: the datacron ontology. In: 13th International Conference on Semantic Systems. Amsterdam, Netherlands: ACM, 17–24.
   Google Scholar
• Spaccapietra, S., et al. 2008. A conceptual view on trajectories. Data & Knowledge Engineering, 65 (1), 126–146. doi:10.1016/j.datak.2007.10.008
   Web of Science ®Google Scholar
• Tran, T., et al., 2009. Top-k exploration of query candidates for efficient keyword search on graph-shaped (RDF) data. In: 25th International Conference on Data Engineering, ICDE 2009, Shanghai, China. IEEE, 405–416.
   Google Scholar
• Wen, Y., Jin, Y., and Yuan, X., 2018. KAT: keywords-to-SPARQL translation over RDF graphs. In: 23rd International Conference on Database Systems for Advanced Applications (DASFAA’18), Gold Coast, Australia. Springer, vol. 10827, 802–810.
   Google Scholar
• Yan, Z., et al. 2008. Trajectory ontologies and queries. Transactions in GIS, 12, 75–91. doi:10.1111/j.1467-9671.2008.01137.x.
   Google Scholar
• Zenz, G., et al. 2009. From keywords to semantic queries-Incremental query construction on the semantic web. Journal of Web Semantics, 7 (3), 166–176. doi:10.1016/j.websem.2009.07.005
   Web of Science ®Google Scholar
• Zheng, W., et al. 2016. Semantic SPARQL similarity search over RDF knowledge graphs. 42nd International Conference on Very Large Databases (VLDB’16), 9 (11), 840–851.
   Web of Science ®Google Scholar
• Zhou, Q., et al. 2007. SPARK: adapting keyword query to semantic search. In: 6th International Semantic Web Conference (ISWC’07), Busan, Korea. Springer, vol. 4825, 694–707.
   Google Scholar