CL-raster: Uma Nova Abordagem para Compactação e Processamento de Séries de Dados Raster
Resumo
As operações da álgebra de mapas contribuíram para que o gerenciamento e processamento eficiente de dados espaciais se tornassem essenciais. Essas operações em séries de dados raster são custosas, contudo cruciais para obter insights valiosos. As estruturas de dados compactas que foram propostas para suportar essas operações enfrentam problemas de representação de dados ou desempenho. Este trabalho apresenta a CL-raster, uma nova estrutura que aplica compressão nos dados linha a linha. A CL-raster armazena dados raster de forma comprimida pronta para o processamento de operações. Os experimentos realizados mostram que a abordagem é eficiente, superando significativamente o concorrente em tempo de processamento e consumo de memória.
Palavras-chave:
Séries de dados raster, Estruturas compactas, Álgebra de mapas
Referências
Amâncio, A. F. and de Senna Carneiro, T. G. (2018). An algebra for modeling and simulation of continuous spatial changes. Journal of Information and Data Management, 9(3):275–275.
Brisaboa, N. R., Cerdeira-Pena, A., de Bernardo, G., Navarro, G., and Óscar Pedreira (2020). Extending general compact querieable representations to GIS applications. Information Sciences, 506:196–216.
Brisaboa, N. R., Ladra, S., and Navarro, G. (2014). Compact representation of web graphs with extended functionality. Information Systems, 39:152–174.
Cruces, N., Seco, D., and Guitérrez, G. (2019). A compact representation of raster time series. In 2019 Data Compression Conference (DCC), pages 103–111.
de Oliveira, S. S. T., do Sacramento Rodrigues, V. J., and Martins, W. S. (2020). SmarT: Uso de aprendizado de máquina para filtragem e recuperação eficiente de dados espaciais e temporais em big data. In Proceedings of the 35th Brazilian Symposium on Databases (SBBD), pages 85–96.
Gonzalez, R. and Woods, R. (2008). Digital Image Processing. Prentice Hall.
Ladra, S., Paramá, J. R., and Silva-Coira, F. (2016). Compact and queryable representation of raster datasets. In Proceedings of the 28th International Conference on Scientific and Statistical Database Management (SSDBM), pages 1–12.
Paiva, R. U., Oliveira, S. S., Pascoal, L. M., Parente, L. L., and Martins, W. S. (2021). Parallel processing of remote sensing time series applied to land-use and land-cover classification. Journal of Information and Data Management, 12(4).
Pinto, A., Seco, D., and Gutiérrez, G. (2017). Improved queryable representations of rasters. In 2017 Data Compression Conference (DCC), pages 320–329.
Rozante, J. R., Gutierrez, E. R., Fernandes, A. d. A., and Vila, D. A. (2020). Performance of precipitation products obtained from combinations of satellite and surface observations. International Journal of Remote Sensing, 41(19):7585–7604.
Silva-Coira, F., Paramá, J. R., and Ladra, S. (2023). Map algebra on raster datasets represented by compact data structures. Softw. Pract. Exp., 53(6):1362–1390.
Silva-Coira, F., Paramá, J. R., de Bernardo, G., and Seco, D. (2021). Space-efficient representations of raster time series. Information Sciences, 566:300–325.
Tomlin, C. (1994). Map algebra: one perspective. Landscape and Urban Planning, 30(1):3–12. Special Issue Landscape Planning: Expanding the Tool Kit.
Tomlin, C. D. et al. (1990). Geographic information systems and cartographic modeling, volume 249. Prentice Hall Englewood Cliffs, NJ.
Xia, Y., Mitchell, K., Ek, M., Sheffield, J., Cosgrove, B., Wood, E., Luo, L., Alonge, C., Wei, H., Meng, J., et al. (2012). Continental-scale water and energy flux analysis and validation for the north american land data assimilation system project phase 2 (NLDAS-2): 1. intercomparison and application of model products. Journal of Geophysical Research: Atmospheres, 117(D3).
Brisaboa, N. R., Cerdeira-Pena, A., de Bernardo, G., Navarro, G., and Óscar Pedreira (2020). Extending general compact querieable representations to GIS applications. Information Sciences, 506:196–216.
Brisaboa, N. R., Ladra, S., and Navarro, G. (2014). Compact representation of web graphs with extended functionality. Information Systems, 39:152–174.
Cruces, N., Seco, D., and Guitérrez, G. (2019). A compact representation of raster time series. In 2019 Data Compression Conference (DCC), pages 103–111.
de Oliveira, S. S. T., do Sacramento Rodrigues, V. J., and Martins, W. S. (2020). SmarT: Uso de aprendizado de máquina para filtragem e recuperação eficiente de dados espaciais e temporais em big data. In Proceedings of the 35th Brazilian Symposium on Databases (SBBD), pages 85–96.
Gonzalez, R. and Woods, R. (2008). Digital Image Processing. Prentice Hall.
Ladra, S., Paramá, J. R., and Silva-Coira, F. (2016). Compact and queryable representation of raster datasets. In Proceedings of the 28th International Conference on Scientific and Statistical Database Management (SSDBM), pages 1–12.
Paiva, R. U., Oliveira, S. S., Pascoal, L. M., Parente, L. L., and Martins, W. S. (2021). Parallel processing of remote sensing time series applied to land-use and land-cover classification. Journal of Information and Data Management, 12(4).
Pinto, A., Seco, D., and Gutiérrez, G. (2017). Improved queryable representations of rasters. In 2017 Data Compression Conference (DCC), pages 320–329.
Rozante, J. R., Gutierrez, E. R., Fernandes, A. d. A., and Vila, D. A. (2020). Performance of precipitation products obtained from combinations of satellite and surface observations. International Journal of Remote Sensing, 41(19):7585–7604.
Silva-Coira, F., Paramá, J. R., and Ladra, S. (2023). Map algebra on raster datasets represented by compact data structures. Softw. Pract. Exp., 53(6):1362–1390.
Silva-Coira, F., Paramá, J. R., de Bernardo, G., and Seco, D. (2021). Space-efficient representations of raster time series. Information Sciences, 566:300–325.
Tomlin, C. (1994). Map algebra: one perspective. Landscape and Urban Planning, 30(1):3–12. Special Issue Landscape Planning: Expanding the Tool Kit.
Tomlin, C. D. et al. (1990). Geographic information systems and cartographic modeling, volume 249. Prentice Hall Englewood Cliffs, NJ.
Xia, Y., Mitchell, K., Ek, M., Sheffield, J., Cosgrove, B., Wood, E., Luo, L., Alonge, C., Wei, H., Meng, J., et al. (2012). Continental-scale water and energy flux analysis and validation for the north american land data assimilation system project phase 2 (NLDAS-2): 1. intercomparison and application of model products. Journal of Geophysical Research: Atmospheres, 117(D3).
Publicado
14/10/2024
Como Citar
REIS, Luana Pereira dos; KASTER, Daniel S..
CL-raster: Uma Nova Abordagem para Compactação e Processamento de Séries de Dados Raster. In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 39. , 2024, Florianópolis/SC.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 129-141.
ISSN 2763-8979.
DOI: https://doi.org/10.5753/sbbd.2024.240879.
