Abstract
Focus on movement data has increased as a consequence of the larger availability of such data due to current GPS, GSM, RFID, and sensors techniques. In parallel, interest in movement has shifted from raw movement data analysis to more application-oriented ways of analyzing segments of movement suitable for the specific purposes of the application. This trend has promoted semantically rich trajectories, rather than raw movement, as the core object of interest in mobility studies. This survey provides the definitions of the basic concepts about mobility data, an analysis of the issues in mobility data management, and a survey of the approaches and techniques for: (i) constructing trajectories from movement tracks, (ii) enriching trajectories with semantic information to enable the desired interpretations of movements, and (iii) using data mining to analyze semantic trajectories and extract knowledge about their characteristics, in particular the behavioral patterns of the moving objects. Last but not least, the article surveys the new privacy issues that arise due to the semantic aspects of trajectories.
- Abul, O., Bonchi, F., and Nanni, M. 2008. Never walk alone: Uncertainty for anonymity in moving objects databases. In Proceedings of the 24th IEEE International Conference on Data Engineering (ICDE'08). Google ScholarDigital Library
- Alvares, L. O., Bogorny, V., Kuijpers, B., De Macedo, J. A. F., Moelans, B., and Vaisman, A. 2007. A model for enriching trajectories with semantic geographical information. In Proceedings of the 15th ACM International Symposium on Advances in Geographic Information Systems (ACM-GIS'07). 162--169, ACM Press, New York. Google ScholarDigital Library
- Alvares, L. O., Loy, A. M., Renso, C., and Bogorny, V. 2011. An algorithm to identify avoidance behavior in moving object trajectories. J. Brazil. Comput. Soc. 17, 3, 193--203.Google ScholarCross Ref
- Andersson, M., Gudmundsson, J., Laube, P., and Wolle, T. 2008. Reporting leaders and followers among trajectories of moving point objects. GeoInformatica 12, 4, 497--528. Google ScholarDigital Library
- Andrienko, G., Andrienko, N., and Wrobel, S. 2007. Visual analytics tools for analysis of movement data. ACM SIGKDD Explor. Newslett. 9, 2, 38--46. Google ScholarDigital Library
- Andrienko, N. and Andrienko, G. 2007. Designing visual analytics methods for massive collections of movement data. Cartographica 42, 2, 117--138.Google ScholarCross Ref
- Andrienko, G., Andrienko, N., Bak, P., Keim, D., Kisilevich, S., and Wrobel, S. 2011. A conceptual framework and taxonomy of techniques for analyzing movement. J. Vis. Lang. Comput. 22, 3, 213--232. Google ScholarDigital Library
- Ashbrook, D. and Starner, T. 2003. Using gps to learn significant locations and predict movement across multiple users. Personal Ubiq. Comput. 7, 275--286. Google ScholarDigital Library
- Baglioni, M., De Macedo, J. A. F., Renso, C., Trasarti, R., and Wachowicz, M. 2012. How you move reveals who you are: Understanding human behavior by analyzing trajectory data. Knowl. Inf. Syst. J. (To appear).Google Scholar
- Bamba, B., Liu, L., Pesti, P., and Wang, T. 2008. Supporting anonymous location queries in mobile environments with privacygrid. In Proceedings of the 17th International Conference on World Wide Web. Google ScholarDigital Library
- Benkert, M., Gudmundsson, J., Hübner, F., and Wolle, T. 2008. Reporting flock patterns. Comp. Geom. 41, 111--125. Google ScholarDigital Library
- Beresford, A. R. and Stajano, F. 2003. Location privacy in pervasive computing. IEEE Pervas. Comput. 2, 1, 46--55. Google ScholarDigital Library
- Bogorny, V., Kuijpers, B., and Alvares, L. O. 2009. ST-dmql: A semantic trajectory data mining query language. Int. J. Geograph. Inf. Sci. 23, 1245--1276.Google ScholarCross Ref
- Bogorny, V., Avancini, H., De Paula, B. L., Kuplish, C. R., and Alvares, L. O. 2011. Weka-stpm: A software architecture and prototype for semantic trajectory data mining. Trans. GIS 15, 2, 227--248.Google ScholarCross Ref
- Brakatsoulas, S., Pfoser, D., Salas, R., and Wenk, C. 2005. On map-matching vehicle tracking data. In Proceedings of the 31st International Conference on Very Large Data Bases (VLDB'05). 853--864. Google ScholarDigital Library
- Cagnacci, F., Boitani, L., Powell, R. A., and Boyce, M. S., Eds. 2010. Challenges and opportunities of using gps location data in animal ecology. Philosoph. Trans. Royal Soc. London Biol. Sci. 365, 1550.Google Scholar
- Cao, H., Mamoulis, N., and Cheung, D. W. 2007. Discovery of periodic patterns in spatiotemporal sequences. IEEE Trans. Knowl. Data Engin. 19, 4, 453--467. Google ScholarDigital Library
- Cao, H. and Wolson, O. 2005. Nonmaterialized motion information in transport networks. In Proceedings of the International Conference on Database Theory (ICDT'05). Lecture Notes in Computer Science, Vol. 3363, Springer, 173--188. Google ScholarDigital Library
- Cao. X., Cong, G., and Jensen, C. S. 2010. Mining significant semantic locations from gps data. Proc. VLDB Endow. 3, 1, 1009--1020. Google ScholarDigital Library
- Carey, J. R, Zou, S., Liedo, P., Robles, L., Morice, A., et al. 2010. A high-resolution system for recording the daily and lifetime behavioral and movement patterns of individual tephritid fruit flies. In Proceedings of the 7th International Conference on Methods and Techniques in Behavioral Research. http://measuringbehavior.org/files/ProceedingsPDF(website)/Carey_FullPaper6.4.pdf.Google Scholar
- Chow, C. Y., Mokbel, M., and Aref, W. 2009. Casper*: Query processing for location services without compromising privacy. ACM Trans. Datab. Syst. 34, 4, 24:1--24:48. Google ScholarDigital Library
- Damiani, M. L., Bertino, E., and Silvestri C. 2010. The probe framework for the personalized cloaking of sensitive positions. Trans. Data Privacy 3, 2, 123--148. Google ScholarDigital Library
- Damiani, M. L., Silvestri, C., and Bertino, E. 2011. Fine-grained cloaking of sensitive positions in location sharing applications. IEEE Pervas. Comput. 10, 4, 64--72. Google ScholarDigital Library
- Ding, Z. and Deng, K. 2011. Collecting and managing network-matched trajectories of moving objects in databases. In Proceedings of the International Conference on Database and Expert Systems Applications (DEXA'11). Lecture Notes in Computer Science, vol. 6860, Springer, 270--279. Google ScholarDigital Library
- Dodge, S., Weibel, R., and Lautenschütz, A. K. 2008. Taking a systematic look at movement: Developing a taxonomy of movement patterns. In Proceedings of the AGILE Workshop on GeoVisualization of Dynamics, Movement and Change.Google Scholar
- Douglas, D. and Peucker, T. 1973. Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. Canad. Cartograph. 10, 2, 112--122.Google ScholarCross Ref
- Duckham, M. and Kulik L. 2005. A formal model of obfuscation and negotiation for location privacy. In Proceedings of the 3rd International Conference on Pervasive Computing. Springer, 152--170. Google ScholarDigital Library
- Du Mouza, C. and Rigaux, P. 2005. Mobility patterns. Geoinformatica 9, 4, 297--319. Google ScholarDigital Library
- Giannotti, F., Nanni, M., Pedreschi, D., Pinelli, F., Renso, C., Rinzivillo, S., and Trasarti, R. 2011. Unveiling the complexity of human mobility by querying and mining massive trajectory data. Int. J. VLDB 20, 5, 695--719. Google ScholarDigital Library
- Giannotti, F., Nanni, M., Pinelli, F., and Pedreschi, D. 2007. Trajectory pattern mining. In Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM Press, New York, 330--339. Google ScholarDigital Library
- Giannotti, F. and Pedreschi, D., Eds. 2008. Mobility, Data Mining and Privacy. Springer. Google ScholarDigital Library
- Gkoulalas-Divanis, A. and Verykios, V. 2008. A privacy-aware trajectory tracking query engine. SIGKDD Explor. 10, 1, 40--49. Google ScholarDigital Library
- Gomez, L., Kuijpers, B., and Vaisman, A. 2008. Aggregation languages for moving object and place of interest. In Proceedings of the ACM Symposium on Applied Computing (SAC'08). 16--20. Google ScholarDigital Library
- Gruteser, M. and Grunwald, D. 2003. Anonymous usage of location-based services through spatial and temporal cloaking. In Proceedings of the 1st International Conference on Mobile Systems, Applications and Services. Google ScholarDigital Library
- Guc, B., May, M., Saygin Y., and Korner, C. 2008. Semantic annotation of gps trajectories. In Proceedings of the 11th AGILE International Conference on Geographic Information Science.Google Scholar
- Gudmundsson, J., Van Kreveld, M., and Speckmann, B. 2007. Efficient detection of patterns in 2d trajectories of moving points. Geoinformatica 11, 195--215. Google ScholarDigital Library
- Güting, R. H., De Almeida, V. T., and Ding, Z. 2006. Modeling and querying moving objects in networks. VLDB J. 15, 2, 165--190. Google ScholarDigital Library
- Güting, R. H., Böhlen, M. H., Erwig, M., Jensen, C. S., Lorentzos, N. A., Schneider, M., and Vazirgiannis, M. 2000. A foundation for representing and querying moving objects. ACM Trans. Data. Syst. 25, 1, 1--42. Google ScholarDigital Library
- Hadjieleftheriou, M., Kollios, G., Balakov, P., and Tsotras, V. 2005. Complex spatio-temporal pattern queries. In Proceedings of the 31st International Conference on Very Large Data Bases (VLDB'05). 877--888. Google ScholarDigital Library
- Hägerstrand, T. 1970. What about people in regional science. Papers Regional Sci. Assoc. 24, 1970, 6--21.Google ScholarCross Ref
- Han, B., Liu, L., and Omiecinski, E. 2012. NEAT: Road network aware trajectory clustering. In Proceedings of the 32nd International Conference on Distributed Computing Systems (ICDS'12). 142--151. Google ScholarDigital Library
- Jensen, C. S., Lu, H., and Yiu, M. 2009. Location privacy techniques in client-server architectures. In Privacy in Location-Based Applications: Research Issues and Emerging Trends. Lecture Notes in Computer Science, vol. 5599, Springer. Google ScholarDigital Library
- Jun, J., Guensler, R., and Ogle, J. 2006. Smoothing methods to minimize impact of global positioning system random error on travel distance, speed, and acceleration profile estimates. Transport. Res. Rec. J. Transport. Res. Board 1972, 1, 141--150.Google ScholarCross Ref
- Kalnis, P., Mamoulis, N., and Bakiras, S. 2005. On discovering moving clusters in spatio-temporal data. In Proceedings of the 9th International Symposium on Spatial and Temporal Databases. 364--381. Springer. Google ScholarDigital Library
- Kellaris, G., Pelekis, N., and Theodoridis, Y. 2009. Trajectory compression under network constraints. In Proceedings of the 11th International Symposium Advances in Spatial and Temporal Databases (SSTD'09). 392--398. Google ScholarDigital Library
- Koubarakis, M., Sellis, T., Frank, A. U., Grumbach, S., Guting, R. H., et al, Eds. 2003. In Spatio-Temporal Databases: The CHOROCHRONOS Approach. Lecture Notes in Computer Science, vol. 2520, Springer. Google ScholarDigital Library
- Krumm, J. and Horvitz, E. 2006. Predestination: Inferring destinations from partial trajectories. In Proceedings of the 8th International Conference on Ubiquitous Computing (UbiComp'06). 243--260. Google ScholarDigital Library
- Lashley, M. and Bevly, D. 2007. Analysis of discriminator based vector tracking algorithms. In Proceedings of the National Technical Meeting of the Institute of Navigation. 570--576.Google Scholar
- Laube, P. 2009a. Progress in movement analysis. In Behaviour Monitoring and Interpretation - BMI - Smart Environments, H. K. Aghajan, Ed., IOS Press, 43--71.Google Scholar
- Laube, P. and Imfeld, S. 2002. Analyzing relative motion within groups of trackable moving point objects. In Proceedings of the 2nd International Conference on Geographic Information Science (GIScience'02). M. J. Egenhofer and D. M. Mark, Eds., Lecture Notes in Computer Science, vol. 2478., Springer, 132--144. Google ScholarDigital Library
- Laube, P., Van Kreveld, M., and Imfeld, S. 2005a. Discovering relative motion patterns in groups of moving point objects. Int. J. Geographical Inf. Sci. 19, 6, 639--668.Google ScholarCross Ref
- Laube, P., Van Kreveld, M., and Imfeld, S. 2005b. Finding REMO - Detecting relative motion patterns in geospatial lifelines. In Developments in Spatial Data Handling, Springer, 201--215.Google Scholar
- Laube, P. 2009b. Progress in movement analysis. In Behaviour Monitoring and Interpretation - BMI - Smart Environments, H. K. Aghajan, Ed., IOS Press, 43--71.Google Scholar
- Lee, J.-G., Han, J., and Whang, K.-Y. 2007. Trajectory clustering: A partition-and-group framework. In Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD'07). ACM Press, New York, 593--604. Google ScholarDigital Library
- Lee, J. G., Han, J., Li, X., and Gonzalez, H. 2008. TraClass: Trajectory classification using hierarchical region-based and trajectory-based clustering. In Proceedings of the International Conference on Very Large Data Bases (VLDB'08). 779--790. Google ScholarDigital Library
- Li, Z., Ding, B., Han, J., Kays, R., and Nye, P. 2010a. Mining periodic behaviors for moving objects. In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'10). 1099--1108. Google ScholarDigital Library
- Li, Z., Lee, J.-G., Li, X., and Han, J. 2010b. Incremental clustering for trajectories. In Proceedings of the Database Systems for Advanced Applications (DASFAA'10). 32--46. Google ScholarDigital Library
- Liao, L., Fox, D., and Kautz, H. 2005. Location-based activity recognition using relational markov networks. In Proceedings of the 9th International Conference on Artificial Intelligence (IJCAI'05). Google ScholarDigital Library
- Lou, Y., Zhang, C., Zheng, Y., Xie, X., Wang, W., and Huang, Y. 2009. Map-matching for low-sampling-rate gps trajectories. In Proceedings of 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. 352--361. Google ScholarDigital Library
- Machanavajjhala, A., Gehrke, J., and Ventikasubramaniam, D. K. 2006. L-diversity: Privacy beyond k-anonymity. In Proceedings of the 22nd International Conference on Data Engineering. Google ScholarDigital Library
- Marketos, G., Frentzos, E., Ntoutsi, I., Pelekis, N., Raffaeta, A., and Theodoridis, Y. 2008. Building real-world trajectory warehouses. In Proceedings of the 7th International ACM SIGMOD Workshop on Data Engineering for Wireless and Mobile Access (MobiDE'08). 8--15. Google ScholarDigital Library
- Meratnia, N. and Deby, R. A. 2004. Spatiotemporal compression techniques for moving point objects. In Proceedings of the 9th International Conference on Extending Database Technology (EDBT'04). 765--782.Google Scholar
- Mohammed, N., Fung, B. C., and Debbabi, M. 2009. Walking in the crowd: Anonymizing trajectory data for pattern analysis. In Proceedings of the 18th ACM Conference on Information and Knowledge Management. Google ScholarDigital Library
- Mokhtar, H. M. O. and Su, J. 2005. A query language for moving object trajectories. In Proceedings of the 17th International Conference on Scientific and Statistical Database Management. Google ScholarDigital Library
- Monreale, A., Pinelli, F., Trasarti, R., and Giannotti, F. 2009. WhereNext: A location predictor on trajectory pattern mining. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'09). 637--646. Google ScholarDigital Library
- Monreale, A., Trasarti, R., Pedreschi, D., Renso, C., and Bogorny, V. 2011. Data privacy for the semantic trajectories of human mobility. Trans. Data Privacy 4, 2, 73--101. Google ScholarDigital Library
- Mountain, D. and Raper, J. F. 2001. Modelling human spatio-temporal behaviour: A challenge for location-based services. In Proceedings of the 6th International Conference on GeoComputation.Google Scholar
- Nanni, M., Kuijpers, B., Körner, C., May, M., and Pedreschi, D. 2008. Spatiotemporal data mining. In Mobility, Data Mining and Privacy. Springer, 267--296.Google Scholar
- Nergiz, E, Atzori, M., and Saygin Y. 2008. Towards trajectory anonymization: A generalization-based approach. In Proceedings of the ACM GIS Workshop on Security and Privacy in GIS and LBS. Google ScholarDigital Library
- Newson, P. and Krumm, J. 2009. Hidden markov map matching through noise and sparseness. In Proceedings of the 17th ACM SIGSPATIAL International Symposium on Advances in Geographic Information Systems. 336--343. Google ScholarDigital Library
- Orellana, D., Wachowicz, M., Andrienko, N., and Andrienko, G. 2009. Uncovering interaction patterns in mobile outdoor gaming. In Proceedings of the International Conference on Advanced Geographic Information Systems and Web Services (GEOWS'09). Google ScholarDigital Library
- Palma, A. T., Bogorny, V., Kuijpers, B., and Alvares, L. O. 2008. A clustering-based approach for discovering interesting places in trajectories. In Proceedings of the ACM Symposium on Applied Computing (SAC'08). ACM Press, New York, 863--868. Google ScholarDigital Library
- Panagiotakis, C., Pelekis, N., Kopanakis, I., Ramasso, E., and Theodoridis, Y. 2012. Segmentation and sampling of moving object trajectories based on representativeness. IEEE Trans. Knowl. Data Engin 24, 7, 1328--1343. Google ScholarDigital Library
- Pelekis, N., Andrienko, G., Andrienko, N., Kopanakis, I., Marketos, G., and Theodoridis, Y. 2011a. Visually exploring movement data via similarity-based analysis. J. Intell. Inf. Syst. 38, 2, 129--140. Google ScholarDigital Library
- Pelekis, N., Kopanakis, I., Kotsifakos, E. E., Frentzos, E., and Theodoridis, Y. 2011b. Clustering uncertain trajectories. Knowl. Inf. Syst. 28, 1, 117--147. Google ScholarDigital Library
- Pelekis, N., Gkoulalas-Divanis, A., Vodas, M., Kopanakis, D., and Theodoridis, Y. 2011c. A privacy- aware query engine for sensitive trajectory data. In Proceedings of the 20th ACM Conference on Information and Knowledge Management (CIKM'11). Google ScholarDigital Library
- Potamias, M., Patroumpas, K., and Sellis, T. 2006. Sampling trajectory streams with spatiotemporal criteria. In Proceedings of the 18th International Conference on Scientific and Statistical Database Management (SSDBM'06). 275--284. Google ScholarDigital Library
- Quddus, M. A., Ochieng, W. Y., and Noland, R. B. 2007. Current map-matching algorithms for transport applications: State-of-the art and future research directions. Transport. Res. Part C: Emerging Technol. 15, 5, 312--328.Google ScholarCross Ref
- Rinzivillo, S., Pedreschi, D., Nanni, M., Giannotti, F., Andrienko, N., and Adrienko, G. 2008. Visually--driven analysis of movement data by progressive clustering. Inf. Vis. 7, 3--4, 225--239. Google ScholarDigital Library
- Rocha, J. A. M., Times, V. C., Oliveira, G., Alvares, L. O., and Bogorny, V. 2010. DB-smot: A direction based spatio-temporal clustering method. In Proceedings of the IEEE Conference on Intelligent Systems. 114--119.Google Scholar
- Ruiz-Vicente, C., Freni, D., Bettini, C., and Jensen, C. S. 2011. Location-related privacy in geo-social networks. IEEE Internet Comput. 15, 3, 20--27. Google ScholarDigital Library
- Sakr, M. A. and Güting, R. H. 2011. Spatiotemporal pattern queries. Geoinformatica 15, 497--540. Google ScholarDigital Library
- Schmid, F., Richter, K.-F., and Laube, P. 2009. Semantic trajectory compression. In Proceedings of the 11th International Symposium on Advances in Spatial and Temporal Databases. 411--416. Google ScholarDigital Library
- Schussler, N. and Axhausen, K. W. 2009. Processing raw data from global positioning systems without additional information. Transport. Res. Rec. J. Transport. Res. Board 8, 28--36.Google ScholarCross Ref
- Seek. 2012. SEmantic enrichment of trajectory knowledge. Eu fp7--people, Marie Curie Actions. http://www.seekproject.eu.Google Scholar
- Siqueira, F. L. and Bogorny, V. 2011. Discovering chasing behavior in moving object trajectories. Trans. GIS, 15, 5, 667--688.Google ScholarCross Ref
- Spaccapietra, S., Parent, C., Damiani, M. L., Macedo, J. A., Porto, F., and Vangenot, C. 2008. A conceptual view on trajectories. Data Knowl. Engin. 65, 126--146. Google ScholarDigital Library
- Spaccapietra, S. and Parent, C. 2011. Adding meaning to your steps. In Proceedings of the 30th International Conference on Conceptual Modeling (ER'11). Lecture Notes in Computer Science, vol. 6998, Springer. Google ScholarDigital Library
- Spinsanti, L., Celli, F., and Renso, C. 2010. Where you stop is who you are: Understanding peoples' activities. In Proceedings of the 5th Workshop on Behaviour Monitoring and Interpretation (BMI'10).Google Scholar
- Stewart-Hornsby, K. and Cole, S. 2007. Modeling moving geospatial objects from an event-based perspective. Trans. GIS 11, 4, 555--573.Google ScholarCross Ref
- Sweeney L. 2002. Achieving k-anonymity privacy protection using generalization and suppression. Int. J. Uncert. Fuzzin. Knowl.-Based Syst. 10, 5, 571--588. Google ScholarDigital Library
- Terrovitis M. and Mamoulis N. 2008. Privacy preservation in the publication of trajectories. In Proceedings of the 9th International Conference on Mobile Data Management. Google ScholarDigital Library
- Thériault, M., Claramunt, C., and Villeneuve, P. 1999. A spatio-temporal taxonomy for the representation of spatial set behaviours. In Proceedings of the International Workshop on Spatio-Temporal Database Management (STDBM'99). Lecture Notes in Computer Science, vol. 1678, Springer, 1--18. Google ScholarDigital Library
- Tiakas, E., Papadopoulos, A. N., Nanopoulos, A., Manolopoulos, Y., Stojanovic, D., and Djordjevic-Kajan, S. 2009. Searching for similar trajectories in spatial networks. J. Syst. Softw. 82, 5, 772--788. Google ScholarDigital Library
- Uddin, M. R., Ravishankar, C., and Tsotras, V. J. 2011. Finding regions of interest from trajectory data. In Proceedings of the 12th IEEE International Conference on Mobile Data Management (MDM'11). 39--48. Google ScholarDigital Library
- Vazirgiannis, M. and Wolfson, O. 2001. A spatiotemporal model and language for moving objects on road networks. In Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases (SSTD'01). Lecture Notes in Computer Science, vol. 2121, Springer, 20--35. Google ScholarDigital Library
- Wang, T. and Liu, L. 2009. Privacy-aware mobile services over road networks. Proc. VLDB Endow. 2, 1, 1042--1053. Google ScholarDigital Library
- Wood, Z. and Galton, A. 2009a. A taxonomy of collective phenomena. Appl. Ontol. 4, 267--292. Google ScholarDigital Library
- Wood, Z. and Galton, A. 2009b. Classifying collective motion. In Behavior Monitoring and Interpretation -- BMI, B. Gottfried and H. Aghajan, Eds., IOS Press, 129--155.Google Scholar
- Wood, Z. and Galton, A. 2010. Zooming in on collective motion. In Proceeedings of the 19th European Conference on Artificial Intelligence (ECAI'10). 25--30.Google Scholar
- Xie, K., Deng, K., and Zhou, X. 2009. From trajectories to activities: A spatio-temporal join approach. In Proceedings of the International Workshop on Location Based Social Networks (LBSN'09). 25--32. Google ScholarDigital Library
- Xue, M., Kalnis, P., and Pung, H. 2009. Location diversity: Enhanced privacy protection in location based services. In Proceedings of the 4th International Symposium on Location and Context Awareness. Google ScholarDigital Library
- YAn, Z., Chakraborty, D., Parent, C., Spaccapietra, S., and Aberer, K. 2011. SeMiTri: A framework for semantic annotation of heterogeneous trajectories. In Proceedings of the 14th International Conference on Extending Database Technology (EDBT'11). 259--270. Google ScholarDigital Library
- Yan, Z., Parent, C., Spaccapietra, S., and Chakraborty, D. 2010. A hybrid model and computing platform for spatio-semantic trajectories. In Proceedings of the 7th Extended Semantic Web Conference (ESWC'10). 60--75. Google ScholarDigital Library
- Yarovoy, R., Bonchi, F., Lakshmanan, V. S., and Wang, W. H. 2009. Anonymizing moving objects: How to hide a mob in a crowd? In Proceedings of the 12th International Conference on Extending Database Technology (EDBT'09). Google ScholarDigital Library
- Yigitoglu, E., Damiani, M. L., Abul, O., and Silvestri, C. 2012. Privacy-preserving sharing of sensitive semantic locations under road network constraints. In Proceedings of the 13th IEEE International Conference of Mobile Data Management (MDM'12). Google ScholarDigital Library
- Yin, H. and Wolfson, O. 2004. A weight-based map matching method in moving objects databases. In Proceedings of the 16th Scientific and Statistical Database Management (SSDBM'04). 437--438. Google ScholarDigital Library
- Yu, M., Li, Z., Chen, Y., and Chen, W. 2006. Improving integrity and reliability of map matching techniques. J. Global Position. Syst. 5, 1--2, 40--46.Google ScholarCross Ref
- Zheng, Y., Chen, Y., Xie, X., and Ma, W.-Y. 2010. Understanding transportation modes based on gps data for web applications. ACM Trans. Web 4, 1. Google ScholarDigital Library
- Zheng, Y., Zhang, L., Ma, Z., Xie, X., and Ma, W-Y. 2011. Recommending friends and locations based on individual location history. ACM Trans. Web 5, 1, 1559--1131. Google ScholarDigital Library
- Zheng, Y. and Zhou, X., Eds. 2011. Computing with Spatial Trajectories. Springer. Google ScholarDigital Library
Index Terms
- Semantic trajectories modeling and analysis
Recommendations
Semantic enrichment for medical ontologies
The Unified Medical Language System (UMLS) contains two separate but interconnected knowledge structures, the Semantic Network (upper level) and the Metathesaurus (lower level). In this paper, we have attempted to work out better how the use of such a ...
The Baquara2 knowledge-based framework for semantic enrichment and analysis of movement data
The analysis of movements frequently requires more than just spatio-temporal data. Thus, despite recent progresses in trajectory handling, there is still a gap between movement data and formal semantics. This gap hinders movement analyses benefiting ...
Adding meaning to your steps
ER'11: Proceedings of the 30th international conference on Conceptual modelingMobility data is becoming an important player in many application domains. Many techniques have been elaborated to extract statistical knowledge from the data sets gathering raw data tracks about the moving objects of interest to an application. These ...
Comments