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English Information

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Authors
# Name
1 Luís Damasceno(d2022010320@unifei.edu.br)
2 Melise Veiga de Paula(melise@unifei.edu.br)
3 Vanessa Souza(vanessasouza@unifei.edu.br)
4 Flávio Belizário da Silva Mota(flavio.belizario.mota@gmail.com)

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Reference
# Reference
1 Bassoi, L. H., Inamasu, R. Y., Bernardi, A. C. d. C., Vaz, C. M. P., Speranza, E. A., and Cruvinel, P. E. (2019). Agricultura de precis˜ao e agricultura digital. TECCOGS: Revista Digital de Tecnologias Cognitivas, (20).
2 Batina, A. (2023). Data Cubes – A Modern Approach for Handling Earth Observation Data. In 2023 International Conference on Earth Observation and Geo-Spatial Information (ICEOGI), pages 1–6.
3 Choi, W. G., Kim, S., Kim, J., Song, M.-H., and Lee, S.-S. (2022). Real-Time Data Pro- cessing Framework for Things with time-series and spatial features. In 2022 13th In- ternational Conference on Information and Communication Technology Convergence (ICTC), pages 1694–1696. ISSN: 2162-1241.
4 CnosDB Documentation. Introduction | CnosDB.
5 Colosi, M., Martella, F., Parrino, G., Celesti, A., Fazio, M., and Villari, M. (2022). Time Series Data Management Optimized for Smart City Policy Decision. In 2022 22nd IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid), pages 585–594.
6 DB-Engines. DB-Engines Ranking.
7 Formaggio, A. R. and Sanches, I. D. (2017). Sensoriamento remoto em Agricultura. Oficina de Textos. Google-Books-ID: hk88DwAAQBAJ.
8 Hachimi, C. E., Belaqziz, S., Khabba, S., Sebbar, B., Dhiba, D., and Chehbouni, A. (2023). Smart Weather Data Management Based on Artificial Intelligence and Big Data Analytics for Precision Agriculture. Agriculture, 13(1):95. Number: 1 Publisher: Multidisciplinary Digital Publishing Institute.
9 InfluxDB - pivot. pivot() function | Flux Documentation.
10 InfluxDB Documentation. InfluxDB OSS v2 Documentation.
11 InfluxDB Geo Package. Experimental geo package | Flux Documentation.
12 John, P., Hynek, J., Hruska, T., and Valny, M. (2023). Application of Time Series Database for IoT Smart City Platform. In 2023 Smart City Symposium Prague (SCSP), pages 1–6. ISSN: 2691-3666.
13 Kim, S., Hoang, Y., Yu, T. T., and Kanwar, Y. S. (2023). GeoYCSB: A Benchmark Framework for the Performance and Scalability Evaluation of Geospatial NoSQL Databases. Big Data Research, 31:100368.
14 Makris, A., Tserpes, K., Spiliopoulos, G., and Anagnostopoulos, D. (2019). Performance Evaluation of MongoDB and PostgreSQL for spatio-temporal data.
15 Mehmood, N. Q., Culmone, R., and Mostarda, L. (2017). Modeling temporal aspects of sensor data for MongoDB NoSQL database. Journal of Big Data, 4(1):8.
16 MongoDB Documentation. MongoDB Documentation.
17 Mongodb Storage. Time Series - Database Manual v8.0 - MongoDB Docs.
18 OpenTSDB Documentation. OpenTSDB - A Distributed, Scalable Monitoring System.
19 Petre, I., Boncea, R., Radulescu, C. Z., Zamfiroiu, A., and Sandu, I. (2019). A Time-Series Database Analysis Based on a Multi-attribute Maturity Model. Studies in Informatics and Control, 28(2).
20 Queiroz, D. M. d., Valente, D. S. M., Pinto, F. d. A. d. C., and Borem, A. (2022). Agricultura digital. Oficina de Textos. Google-Books-ID: 9ehvEAAAQBAJ.
21 Queiroz, G. R. D., Monteiro, A. M. V., & Câmara, G. (2013). Bancos de dados geográficos e sistemas NoSQL: onde estamos e para onde vamos. Revista Brasileira de Cartografia, 65(3).
22 S2 Documentation. Documentação s2 cell.
23 Sousa, G.; Leandro, M. F. H. (2023). O papel da cafeicultura no município de Três Pontas (MG). [S.l.: s.n.].
24 Tripathi, P., Miraz, M. H., and Joshi, S. (2023). Comparative Analysis of MongoDB and InfluxDB for Time Series Data Management in IoT Environments: A Study on Performance, Scalability, and Concurrency. In 2023 International Conference on Computing, Networking, Telecommunications & Engineering Sciences Applications (CoNTESA), pages 39–42.
25 Tugores, A. and Colet, P. (2014). Mining online social networks with Python to study urban mobility. arXiv:1404.6966 [cs].
26 Zaglia, M.; Vinhas, L.; Queiroz, G.; Simões, R. (2019). Catalogação de metadados do Cubo de Dados do Brasil com o SpatioTemporal Asset Catalog. Anais..., pp. 280–285, São José dos Campos, SP, Brasil.
27 Zehra, S. N. (2017). Time Series Databases and InfluxDB.
28 Zhou, Y., De, S., Wang, W., Moessner, K., and Palaniswami, M. S. (2017). Spatial Indexing for Data Searching in Mobile Sensing Environments. Sensors, 17(6):1427. Number: 6 Publisher: Multidisciplinary Digital Publishing Institute.