Big Spatial Data Integration and Enrichment with Provenance Control
Resumo
Nos últimos anos temos assistido a um aumento do uso de dispositivos que geram vastas quantidades de dados, que normalmente são conhecidos como Big Data. Este fenómeno trouxe muitas oportunidades e desafios em termos da descoberta de conhecimento, uma vez que a combinação de todos estes dados distribuídos e heterogéneos podem levar a criação de modelos de eventos e fenómenos de alta qualidade. Contudo, a integração destes dados e as transformações sobre os mesmos levantam questões de integridade, qualidade e veracidade. Esta investigação tem como objetivo a integração de dados, com foco em dados espaciais, utilizando um modelo genérico que permita a integração e o enriquecimentos dos dados, mantendo a informação sobre a proveniência dos mesmos.
Palavras-chave:
Big data, Spatial data, Data integration, Provenance, Data Provenance
Referências
Abadi, D., Ailamaki, A., Andersen, D., Bailis, P., Balazinska, M., Bernstein, P., Boncz, P., Chaudhuri, S., Cheung, A., Doan, A. H., Dong, L., Franklin, M. J., Freire, J., Halevy, A., Hellerstein, J. M., Idreos, S., Kossmann, D., Kraska, T., Krishnamurthy, S., Markl, V., Melnik, S., Milo, T., Mohan, C., Neumann, T., Ooi, B. C., Ozcan, F., Patel, J., Pavlo, A., Popa, R., Ramakrishnan, R., Ré, C., Stonebraker, M., and Suciu, D. (2020). The Seattle Report on Database Research. SIGMOD Record, 48(4):44-53.
Arab, B. S., Feng, S., Glavic, B., Lee, S., Niu, X., and Zeng, Q. (2018). Gprom-A swiss army knife for your provenance needs. IEEE Data Engineering Bulletin, 41(1):51-62.
Benbasat, I., Goldstein, D. K., and Mead, M. (1987). The case research strategy in studies of information systems. MIS Q., 11:369-386.
Buneman, P., Khanna, S., Tan, W.-C., and Chiew, W. (2001). Why and where: A characterization of data provenance. Computer Science, 1973:316-330.
Buneman, P. and Tan, W. C. (2018). Data provenance: What next? SIGMOD Record, 47(3):5-16.
Cheney, J., Chiticariu, L., and Tan, W. C. (2007). Provenance in databases: Why, how, and where. Foundations and Trends in Databases, 1:379-474.
Closa, G., Maso, J., Proß, B., and Pons, X. (2017). W3C PROV to describe provenance at the dataset, feature and attribute levels in a distributed environment. Computers, Environment and Urban Systems, 64(July):103-117.
Glavic, B. and Alonso, G. (2009). Perm: Processing provenance and data on the same data model through query rewriting. In Proceedings of the International Conference on Data Engineering, pages 174-185, Shanghai, China. IEEE.
Pintor, P., Costa, R., and Moreira, J. (2022a). Provenance in spatial queries. In 26th International Database Engineered Applications Symposium-IDEAS 2022.
Pintor, P., Costa, R. L. d. C., and Moreira, J. (2022b). Why-and how-provenance in distributed environments. In Strauss, C., Cuzzocrea, A., Kotsis, G., Tjoa, A. M., and Khalil, I., editors, Database and Expert Systems Applications, pages 103-115, Cham. Springer International Publishing.
Senellart, P. (2017). Provenance and probabilities in relational databases: From theory to practice. SIGMOD Record, 46:5-15. 7, 5.
Senellart, P., Jachiet, L., Maniu, S., and Ramusat, Y. (2018). ProvSQL: Provenance and probability management in PostgreSQL. Proceedings of the VLDB Endowment, 11(12):2034-2037.
Wang, Y., Dos Reis, J. C., Borggren, K. M., Vaz Salles, M. A., Medeiros, C. B., and Zhou, Y. (2019). Modeling and building IoT data platforms with actor-oriented databases. Advances in Database Technology-EDBT, 2019-March(1):512-523.
Arab, B. S., Feng, S., Glavic, B., Lee, S., Niu, X., and Zeng, Q. (2018). Gprom-A swiss army knife for your provenance needs. IEEE Data Engineering Bulletin, 41(1):51-62.
Benbasat, I., Goldstein, D. K., and Mead, M. (1987). The case research strategy in studies of information systems. MIS Q., 11:369-386.
Buneman, P., Khanna, S., Tan, W.-C., and Chiew, W. (2001). Why and where: A characterization of data provenance. Computer Science, 1973:316-330.
Buneman, P. and Tan, W. C. (2018). Data provenance: What next? SIGMOD Record, 47(3):5-16.
Cheney, J., Chiticariu, L., and Tan, W. C. (2007). Provenance in databases: Why, how, and where. Foundations and Trends in Databases, 1:379-474.
Closa, G., Maso, J., Proß, B., and Pons, X. (2017). W3C PROV to describe provenance at the dataset, feature and attribute levels in a distributed environment. Computers, Environment and Urban Systems, 64(July):103-117.
Glavic, B. and Alonso, G. (2009). Perm: Processing provenance and data on the same data model through query rewriting. In Proceedings of the International Conference on Data Engineering, pages 174-185, Shanghai, China. IEEE.
Pintor, P., Costa, R., and Moreira, J. (2022a). Provenance in spatial queries. In 26th International Database Engineered Applications Symposium-IDEAS 2022.
Pintor, P., Costa, R. L. d. C., and Moreira, J. (2022b). Why-and how-provenance in distributed environments. In Strauss, C., Cuzzocrea, A., Kotsis, G., Tjoa, A. M., and Khalil, I., editors, Database and Expert Systems Applications, pages 103-115, Cham. Springer International Publishing.
Senellart, P. (2017). Provenance and probabilities in relational databases: From theory to practice. SIGMOD Record, 46:5-15. 7, 5.
Senellart, P., Jachiet, L., Maniu, S., and Ramusat, Y. (2018). ProvSQL: Provenance and probability management in PostgreSQL. Proceedings of the VLDB Endowment, 11(12):2034-2037.
Wang, Y., Dos Reis, J. C., Borggren, K. M., Vaz Salles, M. A., Medeiros, C. B., and Zhou, Y. (2019). Modeling and building IoT data platforms with actor-oriented databases. Advances in Database Technology-EDBT, 2019-March(1):512-523.
Publicado
19/09/2022
Como Citar
PINTOR, Paulo; MOREIRA, José; COSTA, Rogério Luís de Carvalho.
Big Spatial Data Integration and Enrichment with Provenance Control. In: WORKSHOP DE TESES E DISSERTAÇÕES (WTDBD) - SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 37. , 2022, Búzios.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 140-146.
DOI: https://doi.org/10.5753/sbbd_estendido.2022.21856.
