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Title
Abstract
Figures
Introduction
Background and Motivation
Related Work
Annotation Design Principles
Experiment Analysis
Discussion
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Natural Language Processing

SeMiTri

Profile image of Dipanjan ChakrabortyDipanjan Chakraborty

2011, Proceedings of the 14th International Conference on Extending Database Technology - EDBT/ICDT '11

https://doi.org/10.1145/1951365.1951398
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Abstract

GPS devices allow recording the movement track of the moving object they are attached to. This data typically consists of a stream of spatio-temporal (x,y,t) points. For application purposes the stream is transformed into finite subsequences called trajectories. Existing knowledge extraction algorithms defined for trajectories mainly assume a specific context (e.g. vehicle movements) or analyze specific parts of a trajectory (e.g. stops), in association with data from chosen geographic sources (e.g. points-of-interest, road networks). We investigate a more comprehensive semantic annotation framework that allows enriching trajectories with any kind of semantic data provided by multiple 3rd party sources.

Figures (10)
Figure 2: System Architecture of SeMiTri
Figure 2: System Architecture of SeMiTri
Alg. 1 shows the pseudocode of the annotation algorithm with regions, which directly annotates GPS records with re- gions. Note that, depending on requirements, the spatial join can be computed only for selected episodes. We apply R*-tree index on semantic regions Pregion [2] to improve ef- ficiency of the algorithm. The complexity of the annotation algorithm with region is O(n *log(m)), where n is the num- ber of GPS records (or stop episodes) whilst m is the size of Pregion- For well-divided landuse data, the complexity can be even less, i.e. O(n). Fig. 4 illustrates landuse classification categories and sub- categories that Swisstopo uses to annotate 1,936,439 cells (100mx 100m) covering Switzerland.
Alg. 1 shows the pseudocode of the annotation algorithm with regions, which directly annotates GPS records with re- gions. Note that, depending on requirements, the spatial join can be computed only for selected episodes. We apply R*-tree index on semantic regions Pregion [2] to improve ef- ficiency of the algorithm. The complexity of the annotation algorithm with region is O(n *log(m)), where n is the num- ber of GPS records (or stop episodes) whilst m is the size of Pregion- For well-divided landuse data, the complexity can be even less, i.e. O(n). Fig. 4 illustrates landuse classification categories and sub- categories that Swisstopo uses to annotate 1,936,439 cells (100mx 100m) covering Switzerland.
Algorithm 2: Trajectory annotation with LOIs Since each GPS point considers only the neighboring road segments as a set of candidate segments (by R*-tree), the candidate set size is significantly smaller than the total size of road networks in real-life datasets. This makes the algo- rithm, besides having better matching quality, also efficient, with linear complexity on the size of the GPS points O(n). The global map matching parameters (e.g. radius R and kernel width o) are tuned in the experiment. Alg. 2 shows the detailed procedure of the semantic line annotation algorithm: (1) select candidate road segments, (2) calculate the point-segment distance, (3) normalize the distance as localScore, (4) compute the weight and calcu- late globalS'core, (5) determine the map matching segment for each point based on globalScore, (6) further infer the transport mode based on the features of the GPS points on the segment and the road type information.
Algorithm 2: Trajectory annotation with LOIs Since each GPS point considers only the neighboring road segments as a set of candidate segments (by R*-tree), the candidate set size is significantly smaller than the total size of road networks in real-life datasets. This makes the algo- rithm, besides having better matching quality, also efficient, with linear complexity on the size of the GPS points O(n). The global map matching parameters (e.g. radius R and kernel width o) are tuned in the experiment. Alg. 2 shows the detailed procedure of the semantic line annotation algorithm: (1) select candidate road segments, (2) calculate the point-segment distance, (3) normalize the distance as localScore, (4) compute the weight and calcu- late globalS'core, (5) determine the map matching segment for each point based on globalScore, (6) further infer the transport mode based on the features of the GPS points on the segment and the road type information.
Fig. 5 expresses the resultant HMM problem. The initia input is the raw trajectory Q, i.e. the sequence of (x,y,t. points; a sequence of stops is computed and forms the rea observation (O); the exact POI data are the superficial hid. den states, whilst the POI categories are the real hidder states that we are interested in. Our goal is to identify the real hidden states and use them to annotate the stops. Figure 5: HMM formalism for inferring POI category
Fig. 5 expresses the resultant HMM problem. The initia input is the raw trajectory Q, i.e. the sequence of (x,y,t. points; a sequence of stops is computed and forms the rea observation (O); the exact POI data are the superficial hid. den states, whilst the POI categories are the real hidder states that we are interested in. Our goal is to identify the real hidden states and use them to annotate the stops. Figure 5: HMM formalism for inferring POI category
Computing 6 for areas having high POI density is not easy. Our solution is based on the intuition that influence of a POI category on a stop is proportional to the number of exact POIs of that category in the stop area. We model the influence of a POI as a two-dimensional Gaussian distribu- tion - the mean is the POI’s physical position (x, y) and the variance is [o2, 0;0, a], where o- is category specific. Fig. 7 displays an example of 12 POIs’ Gaussian distributions with the corresponding densities in Fig. 8. By Bayesian rule, we deduce the lemma to determine Pr(o|C;) in B.
Computing 6 for areas having high POI density is not easy. Our solution is based on the intuition that influence of a POI category on a stop is proportional to the number of exact POIs of that category in the stop area. We model the influence of a POI as a two-dimensional Gaussian distribu- tion - the mean is the POI’s physical position (x, y) and the variance is [o2, 0;0, a], where o- is category specific. Fig. 7 displays an example of 12 POIs’ Gaussian distributions with the corresponding densities in Fig. 8. By Bayesian rule, we deduce the lemma to determine Pr(o|C;) in B.
Figure 10: Sensitivity of map matching accuracy w.r.t. R/o Figure 11: Semantic stops/ trajectories by point annotation Figure 9: Landuse category distribution for Taxi data (trajectory, moves, stops)
Figure 10: Sensitivity of map matching accuracy w.r.t. R/o Figure 11: Semantic stops/ trajectories by point annotation Figure 9: Landuse category distribution for Taxi data (trajectory, moves, stops)
Table 2: People Trajectory Data from Mobile Phones Figure 13: Trajectory context computation (sample) Figure 12: Trajectory context computation (distribution) Figure 14: Landuse category distribution and top-5 categories of people trajectories
Table 2: People Trajectory Data from Mobile Phones Figure 13: Trajectory context computation (sample) Figure 12: Trajectory context computation (distribution) Figure 14: Landuse category distribution and top-5 categories of people trajectories
Figure 15: Move annotation - a home-office example (via Metro) Figure 16: Home-office (via Bike and via Bus)
Figure 15: Move annotation - a home-office example (via Metro) Figure 16: Home-office (via Bike and via Bus)

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References (44)

  1. REFERENCES
  2. L. O. Alvares, V. Bogorny, B. Kuijpers, J. Macedo, B. Moelans, and A. Vaisman. A Model for Enriching Trajectories with Semantic Geographical Information. In GIS, page 22, 2007.
  3. N. Beckmann, H.-P. Kriegel, R. Schneider, and B. Seeger. The R*-Tree: An Efficient and Robust Access Method for Points and Rectangles. SIGMOD Record, 19(2):322-331, 1990.
  4. D. Bernstein and A. Kornhauser. An Introduction to Map Matching for Personal Navigation Assistants. Princeton University, 1996.
  5. S. Brakatsoulas, D. Pfoser, R. Salas, and C. Wenk. On Map-Matching Vehicle Tracking Data. In VLDB, pages 853-864, 2005.
  6. T. Brinkhoff, H.-P. Kriegel, and B. Seeger. Efficient Processing of Spatial Joins using R-Trees. In SIGMOD, pages 237-246, 1993.
  7. X. Cao, G. Cong, and C. Jensen. Mining Significant Street name Start time Walk Ch. veilloud 08:50:26
  8. Rt. du Boi 08:54:46
  9. Rt. de Villar 08:57:24
  10. Tir Fédéra 08:58:41
  11. Metro M1 08:59:24
  12. Walk Rt. de la Sorg 09:03:57
  13. Ch. du Barrag 09:04:42
  14. La Diagonal 09:05:24
  15. Semantic Locations From GPS Data. In VLDB, pages 1009-1020, 2010.
  16. G. D. Forney. The Viterbi Algorithm. Proceedings of the IEEE, 61(3):268-278, 1973.
  17. E. Frentzos. Trajectory Data Management in Moving Object Databases. PhD thesis, University of Piraeus, 2008.
  18. M. C. González, C. A. Hidalgo, and A. L. Barabási. Understanding Individual Human Mobility Patterns. Nature, 453(7196):779-782, 2008.
  19. R .H .G üting and M. Schneider. Realm-Based Spatial Data Types: The ROSE Algebra. VLDB Journal, 4:243-286, 1995.
  20. R .H .G üting and M. Schneider. Moving Objects Databases. Morgan Kaufmann, 2005.
  21. J. Han, J.-G. Lee, H. Gonzalez, and X. Li. Mining Massive RFID, Trajectory, and Traffic Data Sets (Tutorial). In KDD, 2008.
  22. H. Jeung, M. L. Yiu, X. Zhou, C. S. Jensen, and H. T. Shen. Discovery of Convoys in Trajectory Databases. In VLDB, pages 1068-1080, 2008.
  23. N. Kiukkoneny, J. Blom, O. Dousse, D. Gatica-Perez, and J. Laurila. Towards Rich Mobile Phone Datasets: Lausanne Data Collection Campaign. In ICPS, 2010.
  24. J. Krumm and E. Horvitz. Predestination: Inferring Destinations from Partial Trajectories. In Ubicomp, pages 243-260, 2006.
  25. Z. Li, B. Ding, J. Han, R. Kays, and P. Nye. Mining Periodic Behaviors for Moving Objects. In KDD, pages 1099-1108, 2010.
  26. Z. Li, M. Ji, J.-G. Lee, L.-A. Tang, Y. Yu, J. Han, and R. Kays. MoveMine: Mining Moving Object Databases. In SIGMOD, pages 1203-1206, 2010.
  27. Y. Lou, C. Zhang, Y. Zheng, X. Xie, W. Wang, and Y. Huang. Map-Matching for Low-Sampling-Rate Gps Trajectories. In GIS, pages 352-361, 2009.
  28. M. F. Mokbel and J. J. Levandoski. Toward Context and Preference-Aware Location-Based Services. In MobiDE, pages 25-32, 2009.
  29. M. Nergiz, M. Atzori, Y. Saygin, and B. Güç. Towards Trajectory Anonymization: a Generalization-Based Approach. Transactions on Data Privacy, 2(1):47-75, 2008.
  30. P. Newson and J. Krumm. Hidden Markov Map Matching Through Noise and Sparseness. In GIS, pages 336-343, 2009.
  31. A. T. Palma, V. Bogorny, B. Kuijpers, and L. O. Alvares. A Clustering-based Approach for Discovering Interesting Places in Trajectories. In SAC, pages 863-868, 2008.
  32. N. Pelekis, E. Frentzos, N. Giatrakos, and Y. Theodoridis. HERMES: Aggregative LBS via a Trajectory DB Engine. In SIGMOD, pages 1255-1258, 2008.
  33. M. A. Quddus, W. Y. Ochieng, and R. B. Noland. Current Map-Matching Algorithms for Transport Applications: State-Of-The Art and Future Research Directions. Transportation Research Part C, 15(5):312-328, 2007.
  34. L. R. Rabiner. A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Readings in speech recognition, pages 267-296, 1990.
  35. S. Spaccapietra, C. Parent, M. L. Damiani, J. A. de Macedo, F. Porto, and C. Vangenot. A Conceptual View on Trajectories. Data and Knowledge Engineering, 65:126-146, 2008.
  36. C. E. White, D. Bernstein, and A. L. Kornhauser. Some map matching algorithms for personal navigation assistants. Tramsportation Research Part C, 8(1-6):91-108, 2000.
  37. K. Xie, K. Deng, and X. Zhou. From Trajectories to Activities: a Spatio-Temporal Join Approach. In LBSN, pages 25-32, 2009.
  38. Z. Yan, J. Macedo, C. Parent, and S. Spaccapietra. Trajectory Ontologies and Queries. Transactions in GIS, 12(s1):75-91, 2008.
  39. Z. Yan, C. Parent, S. Spaccapietra, and D. Chakraborty. A Hybrid Model and Computing Platform for Spatio-Semantic Trajectories. In ESWC, pages 60-75, 2010.
  40. Z. Yan, L. Spremic, D. Chakraborty, C. Parent, S. Spaccapietra, and K. Aberer. Automatic Construction and Multi-level Visualization of Semantic Trajectories. In GIS, pages 524-525, 2010.
  41. Y. Zheng, Y. Chen, Q. Li, X. Xie, and W.-Y. Ma. Understanding transportation modes based on GPS data for web applications. Transactions on the Web, 4(1):1-36, 2010.
  42. Y. Zheng and X. Xie. Learning Location Correlation from GPS Trajectories. In MDM, pages 27-32, 2010.
  43. Y. Zheng, L. Zhang, X. Xie, and W.-Y. Ma. Mining Correlation Between Locations Using Human Location History. In GIS, pages 472-475, 2009.
  44. C. Zhou, D. Frankowski, P. J. Ludford, S. Shekhar, and L. G. Terveen. Discovering Personally Meaningful Places: an Interactive Clustering Approach. ACM Transactions on Information Systems, 25(3):12, 2007.

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