SBBD

Paper Registration

1

Select Book

2

Select Paper

3

Fill in paper information

4

Congratulations

Fill in your paper information

English Information

(*) To change the order drag the item to the new position.

Authors
# Name
1 Geovani Santos(geovani.pereira@uel.br)
2 Daniel Kaster(dskaster@uel.br)

(*) To change the order drag the item to the new position.

Reference
# Reference
1 Baumann, P., Dehmel, A., Furtado, P., Ritsch, R., and Widmann, N. (1998). The multidimensional database system rasdaman. In Proceedings of the 1998 ACM SIGMOD international conference on Management of data, pages 575–577. ACM.
2 de Berg, M., Cheong, O., van Kreveld, M., and Overmars, M. (2008). Computational Geometry: Algorithms and Applications. Springer-Verlag, Berlin, 3 edition.
3 do Valle Goncalves, R. R., Zullo, J., Romani, L. A. S., do Amaral, B. F., and Sousa, E. P. M. (2017). Agricultural monitoring using clustering techniques on satellite image time series of low spatial resolution. In 2017 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp), pages 1–4. IEEE.
4 Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., Hoersch, B., Isola, C., Laberinti, P., Martimort, P., Meygret, A., Spoto, F., Sy, O., Marchese, F., and Bargellini, P. (2012). Sentinel-2: Esa’s optical high-resolution mission for gmes operational services. Remote Sensing of Environment, 120:25–36. The Sentinel Missions - New Opportunities for Science.
5 GDAL/OGR (2024). GDAL/OGR Geospatial Data Abstraction software Library. Open Source Geospatial Foundation.
6 Gonçalves, R., Nascimento, C., Zullo Jr, J., and Romani, L. (2009). Relationship between the spectral response of sugar cane, based on avhrr/noaa satellite images, and the climate condition, in the state of sao paulo (brazil), from 2001 to 2008. In International Workshop on the Analysis of Multi-temporal Remote Sensing images (MultiTemp), vo- lume 5, pages 315–322.
7 Hojati, M., Roberts, S., and Robertson, C. (2024). Dstree: A spatio-temporal indexing data structure for distributed networks. Mathematical and Computational Applications, 29:42. search about DHT.
8 Hu, F., Yang, C., Jiang, Y., Li, Y., Song, W., Duffy, D. Q., Schnase, J. L., and Lee, T. (2020). A hierarchical indexing strategy for optimizing apache spark with hdfs to efficiently query big geospatial raster data. International Journal of Digital Earth, 13:410–428.
9 Kemmer, C., Reis, L., Rodrigues-Silva, J., Scolin, L., Canteri, M., and Kaster, D. (2023). Proposta de uma plataforma para o desenvolvimento e análise visual de modelos preditivos para doenças da soja. In Anais do XIV Congresso Brasileiro de Agroinformática, pages 326–333, Porto Alegre, RS, Brasil. SBC.
10 Khaki, S., Pham, H., and Wang, L. (2021). Simultaneous corn and soybean yield prediction from remote sensing data using deep transfer learning. Scientific Reports, 11:11132.
11 Knuth, D. E. (1998). The Art of Computer Programming, Volume 3: Sorting and Searching. Addison-Wesley, Reading, Massachusetts, 2nd edition.
12 PostGIS (2024). PostGIS. Open Source Geospatial Foundation. PostGIS is a spatial database extender for PostgreSQL.
13 Sakamoto, T. (2020). Incorporating environmental variables into a modis-based crop yield estimation method for United States corn and soybeans through the use of a random forest regression algorithm. ISPRS Journal of Photogrammetry and Remote Sensing, 160:208–228.
14 Tian, R., Zhai, H., Zhang, W., Wang, F., and Guan, Y. (2022). A survey of spatio-temporal big data indexing methods in distributed environment. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 15:4132–4155.
15 Villarroya, S. and Baumann, P. (2023). A survey on machine learning in array databases. Applied Intelligence, 53:9799–9822.
16 Vu, T. and Eldawy, A. (2020). R*-grove: Balanced spatial partitioning for large-scale datasets. Frontiers in Big Data, 3.
17 Vu, T., Eldawy, A., Hristidis, V., and Tsotras, V. (2021). Incremental partitioning for efficient spatial data analytics. In Proceedings of the VLDB Endowment, volume 15, pages 713–726. VLDB Endowment.
18 yun Luo, T., cheng Zou, B., hui Li, S., cheng Pang, S., and rui Zhang, C. (2023). High stability of opto-mechanical structure design and validation of Beijing No.3 camera. In Jiang, Y., Wang, X., Liu, D., Xue, B., Wang, Y., Cao, L., Wang, Q.-H., and Lu, C.-Y., editors, AOPC 2023: Optical Sensing, Imaging, and Display Technology and Applications; and Biomedical Optics, volume 12963, page 129630V. International Society for Optics and Photonics, SPIE.
19 Zalipynis, R. A. R. (2018). Chronosdb. Proceedings of the VLDB Endowment, 11:1247–1261.
20 Zhao, Q., Yu, L., Du, Z., Peng, D., Hao, P., Zhang, Y., and Gong, P. (2022). An overview of the applications of earth observation satellite data: Impacts and future trends. Remote Sensing, 14(8).