The organization of the catalogue is by methods developed and available, that in turn include the single algorithms and the datasets used to test the algorithms. The overall view of the datasets available in the MASTER catalogue is available on the right-hand side.
Similarity Analysis*
In this topic we investigate similarity measures for multidimensional sequences. We have tested these measures on mobility data, but they can be applied to several domains.
*Please, note that it is mandatory to always reference the original datasets.
Andre Salvaro Furtado; Despina Kopanaki; Luis Otavio Alvares; Vania Bogorny.
Transactions in GIS, v. 20, p. 280-298, 2016.
- publication: https://onlinelibrary.wiley.com/doi/abs/10.1111/tgis.12156
- code: https://github.com/bigdata-ufsc/furtado-2016-msm
- data:
MSM is the first similarity measure that can deal with the dimensions space, time and semantics, using weights for each dimension. It ignores the order and allows partial matching of dimensions, overcoming the limitations of EDR and LCSS. Finds objects that do the same things or visit the same places, but in different order.
Andre Salvaro Furtado, Luis Otávio Alvares, Nikos Pelekis, Yannis Theodoridis, Vania Bogorny
International Journal of Geographical Information Science 32(1): 140-168 (2018)
- publication: https://www.tandfonline.com/doi/full/10.1080/13658816.2017.1372763
- code: https://github.com/bigdata-ufsc/furtado-2018-ums
- data:
UMS is a parameter free spatial similarity measure that solves the threshold problem, using ell
ipses between trajectory points to measure the spatial distance, solving the problem irregular point distribution. It does not consider time but the order of the points (direction), and outperformed LCSS and EDR. Finds objects that move close in space even if the points have different sampling rates or different spatial distribution.
Lucas May Petry, Carlos Andres Ferrero, Luis Otavio Alvares, Chiara Renso, Vania Bogorny.
Transactions in GIS. 2019; 23: 960– 975.
- publication: https://onlinelibrary.wiley.com/doi/abs/10.1111/tgis.12542
- code: https://github.com/bigdata-ufsc/petry-2019-muitas
- data:
MUITAS bridges the gap between MSM and EDR. It considers the dimensions space, time, and semantics, allowing the definition of dimensions/attributes that are independent and should be analyzed individually (e.g. weather condition) or dependent dimensions that must be considered together because they are related to each other (e.g. hotel and review). This measure is interesting for data enriched with a high number of semantic dimensions.
Andre L. Lehmann, Luis Otávio Alvares, Vania Bogorny.
International Journal of Geographical Information Science 33(9): 1847-1872 (2019).
- publication: https://www.tandfonline.com/doi/full/10.1080/13658816.2019.1605074
- code: https://github.com/bigdata-ufsc/lehmann-2019-SMSM
- data:
SMSM is the first similarity measure that considers heterogeneous elements as stops and moves, and partial sequence.It considers all dimensions of space, time, and semantics, and evaluates the move similarity only the start stop and end stop of a subsequence have a spatial match.
Andre Salvaro Furtado; Laercio Pilla Lima; Vania Bogorny.
Data & Knowledge Engineering, v. 5, p. 1, 2018.
- publication: https://www.sciencedirect.com/science/article/abs/pii/S0169023X17302021
- code: https://github.com/bigdata-ufsc/furtado-2018-ftsm-msm
- data:
FTSM is an approach to improve the performance of sequence comparison, that instead of using a dynamic programming approach, adopts a solution inspired by the branch and bound paradigm to optimize the exact computation of threshold-based and dynamic threshold-based similarity measures.
Classification/Prediction*
In this topic we investigate new methods for multidimensional sequence classification. We propose a benchmark and new classification techniques based on time series shapelets, that deal with sequence and are able to automatically combine multiple dimensions. Indeed, we also propose a classification method based on POI frequency, that is faster than the shapelets based approach, but which is not so robust as the number of dimension increase and are needed to discriminate classes. The Embeddings based approach uses all three dimensions of space, time and semantics, and is very robust and in some datasets better than the Movelets, but it does not allow to interpret the patterns, as it is a black box approach. In summary, depending on the classification objective, one approach can be better than others.
*Please, note that it is mandatory to always reference the original datasets.
Carlos Andres Ferrero; Luis Otavio Alvares; Willian Zalewski; Vania Bogorny.
In: ACMSAC, 2018. Proceedings of the 33 Symposium on Applied Computing, Pau, France, April 9-13, 2018
- publication: https://dl.acm.org/doi/10.1145/3167132.3167225
- code: https://github.com/bigdata-ufsc/ferrero-2018-movelets
- data:
Movelets finds the best multidimensional subsequences that discriminate the class. It is a generic method that works for any sequencial data classification problem, being able to deals the dimensions space, time, semantics and others. Movelets uses local features (subsequences) and global features extracted from the entire sequence.
FERRERO, Carlos Andres, PETRY, Lucas May, ALVARES, Luis Otavio, SILVA, Camila Leite, ZALEWSKI, Willian, BOGORNY, Vania.
In: Data Mining and Knowledge Discovery (2019).
- publication: https://link.springer.com/article/10.1007/s10618-020-00676-x
- code: https://github.com/bigdata-ufsc/MASTERMovelets
- data:
MasterMovelets finds the most discriminant subsequences and automatically chooses the best dimension combination for each subsequence. Subsequences can be heterogenous, so a dimension that is discriminant for one class or one subsequence, may not be for another. MasterMovelets is robust for multiple aspect mobility data, biological and health data.
Francisco Vicenzi, Lucas May Petry, Camila Leite da Silva, Luis Otavio Alvares, Vania Bogorny.
In: 35th Annual ACM Symposium on Applied Computing, 624 – 631 (2020).
- publication:
- code: https://github.com/bigdata-ufsc/vicenzi-2020-poifreq
- data:
- Foursquare
- Brightkite
- Gowalla
In: International Journal of Geographical Information Science (2020).
- publication:
- code: https://github.com/bigdata-ufsc/petry-2020-marc
- data:
- Foursquare
- Brightkite
- Gowalla
SILVA, Camila Leite, PETRY, Lucas May, BOGORNY, Vania.
In: 2019 Brazilian Conference on Intelligent Systems (BRACIS). 2019. IEEE. ed 8, p. 788-794.
- publication: https://ieeexplore.ieee.org/document/8923877
- code: the codes of state of the art methods are available under a visit to our Institution.
- data:
Compares and sumarizes existing works for trajectory classification.
Data Modeling*
Conceptual data models for mobility data representation and mining.
*Please, note that it is mandatory to always reference the original datasets.
Vania Bogorny, Chiara Renso, Artur Ribeiro de Aquino, Fernando de Lucca Siqueira, Luis Otávio Alvares: CONSTAnT – A Conceptual Data Model for Semantic Trajectories of Moving Objects. T. GIS 18(1): 66-88 (2014).
Ronaldo dos Santos Mello; Vania Bogorny ; Luis Otavio Alvares ; Luiz H. Z. Santana ; Carlos Andres Ferrero; Angelo Augusto Frozza; Geomar Andre Schreiner; Chiara Renso: MASTER: A Multiple Aspect View on Trajectories. Transactions in GIS. 2019; 23: 805– 822.
Bogorny, V.; Heuser, C.A; Alvares, L.O. A conceptual data model for trajectory data mining. Sixth International Conference on Geographic Information Science (GIScience 2010), 2010, Springer Verlag, pp1-15. (BIBTEX) (TALK)
Abnormal Behavior*
we developed several methods for different abnormal behavior detection in several application domains.
*Please, note that it is mandatory to always reference the original datasets.
Mateus Barragana; Luis Otavio Alvares ; Vania Bogorny. Unusual behavior detection and object ranking from movement trajectories in target regions. International Journal of Geographical Information Science, 31(2): 364-386 (2017).
Serhan Cosar; Giuseppe Donatiello; Vania Bogorny; Carolina Garate; Luis Otavio Alvares; François Bremond. Towards Abnormal Trajectory and Event Detection in Video Surveillance. IEEE Transactions on Circuits and Systems for Video Technology, v. PP, p. 1-1, 2017.
Francesco Lettich; , Luis Otavio Alvares; Vania Bogorny; Salvatore Orlando; Alessandra Raffaeta; Claudio Silvestri. Detecting avoidance behaviors between moving object trajectories. Data & Knowledge Engineering, v. 102, p. 22-41, 2016.
Eduardo Machado Carboni, Vania Bogorny. Inferring Drivers Behavior through Trajectory Analysis. In: IEEE International Conference on Intelligent Systems (IEEE IS), 2014, Warsaw. Advances in Intelligent Systems and Computing.p. 837-848.
Vitor Cunha Fontes, Lucas Andre de Alencar, Chiara Renso, Vania Bogorny: Discovering Trajectory Outliers between Regions of Interest. GeoInfo 2013: 49-60.
Artur Ribeiro de Aquino, Luis Otávio Alvares, Chiara Renso, Vania Bogorny: Towards Semantic Trajectory Outlier Detection. GeoInfo 2013: 115-126.
Luis Otavio Alvares, Alisson Moscat Loy, Chiara Renso, Vania Bogorny. An Algorithm to Identify Avoidance Behavior in Moving Object Trajectories. In: Journal of the Brazilian Computer Society, 2011.
Siqueira, F.L.; Bogorny, V. Discovering Chasing Behavior Patterns in Moving Object Trajectories. In: Transactions in GIS, 2011.
Context-Aware or Semantic Enrichment*
*Please, note that it is mandatory to always reference the original datasets.
Francisco Javier Moreno Arboleda.; Holver Patino; Vania Bogorny. SMOT+NCS: An algorithm for detecting non-continuous stops. Computing and Informatics, 2017.
Carlos Andres Ferrero; Luis Otavio Alvares; Vania Bogorny. Multiple Aspect Trajectory Data Analysis: Research Challenges and Opportunities. In GEOINFO 2016, pages: 56-67
Francisco Javier Moreno Arboleda, Andrés Felipe Pineda, Renato Fileto, Vania Bogorny: SMoT+: Extending the SMoT Algorithm for Discovering Stops in Nested Sites. Computing and Informatics 33(2): 327-342 (2014).
Christine Parent, Stefano Spaccapietra, Chiara Renso, Gennady L. Andrienko, Natalia V. Andrienko, Vania Bogorny, Maria Luisa Damiani, Aris Gkoulalas-Divanis, José Antônio Fernandes de Macêdo, Nikos Pelekis, Yannis Theodoridis, Zhixian Yan: Semantic trajectories modeling and analysis. ACM Comput. Surv. 45(4): 42 (2013).
Salvatore Rinzivillo, Fernando de Lucca Siqueira, Lorenzo Gabrielli, Chiara Renso, Vania Bogorny: Where Have You Been Today? Annotating Trajectories with DayTag. SSTD 2013: 467-471.
Manso, J. A; Times, V. C.; Oliveira, G.; Alvares, L.O.; Bogorny, V. DB-SMoT: a Direction-based spatio-temporal clustering method. Fifth IEEE International Conference on Intelligent Systems (IEEE IS 2010), 2010, (BIBTEX)
Bogorny, V.; Valiati, J.F.; Alvares, L.O. Semantic-based pruning of redundant and uninteresting frequent geographic patterns. In: Geoinformatica. Springer, pp.201-220, vol 14(2). (BIBTEX).
Bruno Neiva Moreno, Valeria Cesario Times, Chiara Renso, Vania Bogorny. Looking Inside the Stops of Trajectories of Moving Objects, GEOINFO 2010.
Bogorny, V.; Kuijpers, B.; Alvares, L.O. ST-DMQL: A Semantic Trajectory Data Mining Query Language. In: International Journal of Geographical Information Science. Taylor and Francis . pp.1245 – 1276, vol 23 (10) (BIBTEX).
Palma, A. T; Bogorny, V.; Kuijpers, B.; Alvares, L.O. A Clustering-based Approach for Discovering Interesting Places in Trajectories. In: 23rd Annual Symposium on Applied Computing, (ACM-SAC’08), Fortaleza, Ceara, 16-20 March (2008) Brazil. pp. 863-868.
Alvares,L. O.; Bogorny, V.; Kuijpers, B.; Macedo, J. A. F.; Moelans, B.; Vaisman, A. A Model for Enriching Trajectories with Semantic Geographical Information. In: Proc. of the ACM 15th International Symposium on Advances in Geographic Information Systems (ACM-GIS’07), Seattle, Washingthon, 7-9 November (2007), pp. 162-169.
Bogorny, V.; Valiati, J.; Camargo, S.; Engel, P.; Kuijpers, B.; Alvares, L.O. Mining Maximal Generalized Frequent Geographic Patterns with Knowledge Constraints. In: Proc. of the 6rd IEEE International Conference On Data Mining – (IEEE-ICDM’06),Hong Kong, December, pp.813-817).
Bogorny,V.; Camargo, S.; Engel, P.; Alvares, L.O. Mining Frequent Geographic Patterns with Knowledge Constraints. In: Proc. of the ACM 14th International Symposium on Advances in Geographic Information Systems (ACM-GIS’06), Arlington, USA, November 10-11. (2006). Pp.139-146.
Privacy*
*Please, note that it is mandatory to always reference the original datasets.
Tarlis Tortelli Portela; Francisco Vicenzi; Vania Bogorny: Trajectory Data Privacy: Research Challenges and Opportunities. In: GEOINFO 2019
Anna Monreale, Roberto Trasarti, Chiara Renso, Dino Pedreschi, Vania Bogorny. Preserving Privacy in Semantic-Rich Trajectories of Human Mobility, SPRINGL (3rd ACM SIGSPATIAL International Workshop on Security and Privacy in GIS and LBS), 2010.
Monreale, A.;Trasarti,R.; Pedreschi,D.; Renso, C.; Bogorny, V. C-safety: a Framework for the Anonymization of Semantic Trajectories. In: Transactions on Data Privacy.