A Parallel-based Map Matching Approach over Urban Place Records
Resumo
In the Smart Cities scenario, to avoid the conflicting geospatial records between official and non-official sources, it is necessary to detect the inconsistencies regarding the geospatial data provided by them. To this end, the map matching task, i.e., the task of identifying correspondent features between two geospatial data sources, should be applied. For spatial Big Data, the map matching task is confronted with challenges related to volume and veracity of the data. In this sense, we propose a Spark-based map matching approach, called MATCH-UPS. To evaluate, real-world data sources of New York (USA) and Curitiba (Brazil) were applied. The results showed that MATCH-UPS improved the precision by 26% and reduced the execution time by one third.
Palavras-chave:
Smart cities, geospatial records, map matching task, spatial big data, spark-based map matching
Referências
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Du, H., Alechina, N., Jackson, M., and Hart, G. (2017). A method for matching crowdsourced and authoritative geospatial data. Transactions in GIS, 21(2):406–427. DOI: https://doi.org/10.1111/tgis.12210
Fan, H., Yang, B., Zipf, A., and Rousell, A. (2016). A polygon-based approach for matching openstreetmap road networks with regional transit authority data. International Journal of Geographical Information Science, 30(4):748–764. DOI: https://doi.org/10.1080/13658816.2015.1100732
Fan, H., Zipf, A., Fu, Q., and Neis, P. (2014). Quality assessment for building footprints data on openstreetmap. International Journal of Geographical Information Science, 28(4):700–719. DOI: https://doi.org/10.1080/13658816.2013.867495
Harb, J. G. and Becker, K. (2018). Emotion analysis of reaction to terrorism on twitter. In Proceedings of the SBC Brazilian Symposium on Databases, pages 97–108. DOI: https://doi.org/
Interdonato, R. and Tagarelli, A. (2017). Personalized recommendation of points-ofinterest based on multilayer local community detection. In International Conference on Social Informatics, pages 552–571. Springer. DOI: https://doi.org/10.1007/978-3-319-67217-5_33
Pi, X., Egge, M., Whitmore, J., Silbermann, A., and Qian, Z. S. (2018). Understanding transit system performance using avl-apc data: An analytics platform with case studies for the pittsburgh region. Journal of Public Transportation, 21(2):2. DOI: https://doi.org/10.5038/2375-0901.21.2.2
Xavier, E., Ariza-Lopez, F. J., and Urena-Camara, M. A. (2016). A survey of measures and methods for matching geospatial vector datasets. ACM Computing Surveys (CSUR), 49(2):39. DOI: https://doi.org/10.1145/2963147
Yang, B., Zhang, Y., and Lu, F. (2014). Geometric-based approach for integrating vgi pois and road networks. International Journal of Geographical Information Science, 28(1):126–147. DOI: https://doi.org/10.1080/13658816.2013.830728
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Zhang, X., Ai, T., Stoter, J., and Zhao, X. (2014). Data matching of building polygons at multiple map scales improved by contextual information and relaxation. ISPRS Journal of Photogrammetry and Remote Sensing, 92:147–163. DOI: https://doi.org/10.1016/j.isprsjprs.2014.03.010
Araújo, T. B., Cappiello, C., Kozievitch, N. P., Mestre, D. G., Pires, C. E. S., and Vitali, M. (2017). Towards reliable data analyses for smart cities. In Proceedings of the 21st International Database Engineering & Applications Symposium, pages 304–308. ACM. DOI: https://doi.org/10.1145/3105831.3105834
Araújo, T. B., Pires, C. E. S., da N´obrega, T. P., and Nascimento, D. C. (2016). A fine-grained load balancing technique for improving partition-parallel-based ontology matching approaches. Knowledge-Based Systems, 111:17–26. DOI: https://doi.org/10.1016/j.knosys.2016.08.017
Arriagada, J., Gschwender, A., Munizaga, M. A., and Tr´epanier, M. (2019). Modeling bus bunching using massive location and fare collection data. Journal of Intelligent Transportation Systems, 23(4):332–344. DOI: https://doi.org/10.1080/15472450.2018.1494596
Bojic, I., Massaro, E., Belyi, A., Sobolevsky, S., and Ratti, C. (2015). Choosing the right home location definition method for the given dataset. In International Conference on Social Informatics, pages 194–208. Springer. DOI: https://doi.org/10.1007/978-3-319-27433-1_14
Bouguelia, M.-R., Karlsson, A., Pashami, S., Nowaczyk, S., and Holst, A. (2018). Mode tracking using multiple data streams. Information Fusion, 43:33–46. DOI: https://doi.org/10.1016/j.inffus.2017.11.011
Christen, P. (2012). Data matching: concepts and techniques for record linkage, entity resolution, and duplicate detection. Springer Science & Business Media. DOI: https://doi.org/10.1007/978-3-642-31164-2
Du, H., Alechina, N., Jackson, M., and Hart, G. (2017). A method for matching crowdsourced and authoritative geospatial data. Transactions in GIS, 21(2):406–427. DOI: https://doi.org/10.1111/tgis.12210
Fan, H., Yang, B., Zipf, A., and Rousell, A. (2016). A polygon-based approach for matching openstreetmap road networks with regional transit authority data. International Journal of Geographical Information Science, 30(4):748–764. DOI: https://doi.org/10.1080/13658816.2015.1100732
Fan, H., Zipf, A., Fu, Q., and Neis, P. (2014). Quality assessment for building footprints data on openstreetmap. International Journal of Geographical Information Science, 28(4):700–719. DOI: https://doi.org/10.1080/13658816.2013.867495
Harb, J. G. and Becker, K. (2018). Emotion analysis of reaction to terrorism on twitter. In Proceedings of the SBC Brazilian Symposium on Databases, pages 97–108. DOI: https://doi.org/
Interdonato, R. and Tagarelli, A. (2017). Personalized recommendation of points-ofinterest based on multilayer local community detection. In International Conference on Social Informatics, pages 552–571. Springer. DOI: https://doi.org/10.1007/978-3-319-67217-5_33
Pi, X., Egge, M., Whitmore, J., Silbermann, A., and Qian, Z. S. (2018). Understanding transit system performance using avl-apc data: An analytics platform with case studies for the pittsburgh region. Journal of Public Transportation, 21(2):2. DOI: https://doi.org/10.5038/2375-0901.21.2.2
Xavier, E., Ariza-Lopez, F. J., and Urena-Camara, M. A. (2016). A survey of measures and methods for matching geospatial vector datasets. ACM Computing Surveys (CSUR), 49(2):39. DOI: https://doi.org/10.1145/2963147
Yang, B., Zhang, Y., and Lu, F. (2014). Geometric-based approach for integrating vgi pois and road networks. International Journal of Geographical Information Science, 28(1):126–147. DOI: https://doi.org/10.1080/13658816.2013.830728
Zhang, C., Zhao, T., and Li, W. (2015). Geospatial semantic web. Springer.
Zhang, X., Ai, T., Stoter, J., and Zhao, X. (2014). Data matching of building polygons at multiple map scales improved by contextual information and relaxation. ISPRS Journal of Photogrammetry and Remote Sensing, 92:147–163. DOI: https://doi.org/10.1016/j.isprsjprs.2014.03.010
Publicado
07/10/2019
Como Citar
ARAÚJO, Tiago Brasileiro; PIRES, Carlos Eduardo Santos; MESTRE, Demetrio Gomes; DE QUEIROZ, Andreza Raquel Monteiro; SANTOS, Veruska Borges; DA NÓBREGA, Thiago Pereira.
A Parallel-based Map Matching Approach over Urban Place Records. In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 34. , 2019, Fortaleza.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 121-132.
ISSN 2763-8979.
DOI: https://doi.org/10.5753/sbbd.2019.8813.