Revisão e comparação de trabalhos sobre dados heterogêneos e análise semântica em Big Data
DOI:
https://doi.org/10.5433/1679-0375.2021v42n1p113Palavras-chave:
Análise de dados, Dados heterogêneos, Big data, Heterogeneidade semântica, Heterogeneidade estruturalResumo
Nas abordagens de integração, a heterogeneidade é um dos principais fatores que dificulta a tarefa de prover integração entre diferentes fontes de dados, cuja solução encontra-se na busca de igualdades entre elas. Este trabalho descreve o estado da arte e a fundamentação teórica envolvida na análise estrutural e semântica de dados e informação heterogênea. O trabalho tem como objetivo revisar métodos e técnicas utilizados na integração de dados em Big Data, considerando a heterogeneidade dos dados, revisando técnicas que utilizam os conceitos da Web Semântica, Computação em nuvem, Análise de dados, Big Data, Data Warehouse e outras tecnologias para resolver o problema da heterogeneidade de dados. A pesquisa foi dividida em três etapas. Na primeira etapa foram selecionados artigos nas bibliotecas digitais com base no título e nas palavras-chave. Na segunda etapa passaram por um segundo filtro baseado em seu resumo, e, além disso, artigos duplicados também foram removidos. Na terceira etapa a introdução e conclusão dos trabalhos foram analisados para que se escolhesse os artigos pertencentes a esta revisão sistemática. Os artigos foram analisados, comparados, e categorizados ao longo do estudo. Ao final de cada seção, mostrou-se as interrelações e a possíveis áreas para trabalhos futuros.Referências
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ALQARNI, A. A.; PARDEDE, E. Integration of data warehouse and unstructured business documents. In: INTERNATIONAL CONFERENCE ON NETWORK-BASED INFORMATION SYSTEMS, 15., 2012, Melbourne. Proceedings [...]. Melbourne: IEEE, 2012. p. 32-37.
ARPUTHAMARY, B.; AROCKIAM, L. Data Integration in big data environment. Bonfring International Journal of Data Mining, Tamilnadu, v. 5, n. 1, p. 1-5, 2015.
ASSAF, A.; LOUW, E.; SENART, A.; FOLLENFANT, C.; TRONCY, R.; TRASTOUR, D. RUBIX: a framework for improving data integration with linked data. In: WOD 12: INTERNATIONAL WORKSHOP ON OPEN DATA, 1., 2012, Nantes. Proceedings [...]. New York: Association for Computing Machinery, 2012. p. 13-21.
AUFAURE, M.-A.; CHIKY, R.; CURÉ, O.; KHROUF, H.; KEPEKLIAN, G. From business intelligence to semantic data stream management. Future Generation Computer Systems, London, v. 63, p. 100-107, 2016.
BALA, M. BOUSSAID, O.; ALIMAZIGHI, Z. P-ETL: Parallel-ETL based on the MapReduce paradigm. In: INTERNATIONAL CONFERENCE ON COMPUTER SYSTEMS AND APPLICATIONS (AICCSA), 11., 2014, Doha. Proceedigns [...]. Doha: IEEE, 2014. p. 42-49.
BONDAREV, A.; ZAKIROV, D. Data warehouse on Hadoop platform for decision support systems in education. In: INTERNATIONAL CONFERENCE ON ELECTRONICS COMPUTER AND COMPUTATION (ICECCO), 12., 2015, Almaty. Proceedings [...].Almaty: Suleyman Demirel University, 2015. p. 1-4.
BORTOLI, S.; BOUQUET, P.; POMPERMAIER, F.; MOLINARI, A. Semantic big data for tax assessment. In: SBD 16: INTERNATIONAL WORKSHOP ON SEMANTIC BIG DATA, 16., 2016, San Francisco Califórnia. Proceedings [...]. New York: Association for Computing Machinery, 2016. p. 1-6.
CHEN, W.; WANG, R.; WU, R.; TANG, L.; FAN, J. Multi-source and heterogeneous data integration model for big data analytics in power DCS. In: INTERNATIONAL CONFERENCE ON CYBER-ENABLED DISTRIBUTED COMPUTING AND KNOWLEDGE DISCOVERY (CYBERC), 2016, Chengdu. Proceedings [...]. Chengdu: IEEE, 2016. p. 238-242.
CHOI, T.-M.; CHAN, H. K.; YUE, X. Recent development in big data analytics for business operations and risk management. IEEE transactions on cybernetics, New York, v. 47, n. 1, p. 81-92, 2016.
CUZZOCREA, A.; DIAMANTINI, C.; GENGA, L.; POTENA, D.; STORTI, E. A composite methodology for supporting collaboration pattern discovery via semantic enrichment and multidimensional analysis. In: INTERNATIONAL CONFERENCE OF SOFT COMPUTING AND PATTERN RECOGNITION (SOCPAR), 6., 2014, Tunis. Proceedings [...]. Tunis: IEEE, 2014. p. 459-464.
CUZZOCREA, A.; SACCÀ, D.; ULLMAN, J. D. Big data: a research agenda. In: IDEAS 13: INTERNATIONAL DATABASE ENGINEERING & APPLICATIONS SYMPOSIUM, 17., 2013, Barcelona. Proceedings [...]. New York: Association for Computing Machinery, 2013. p. 198-203.
CUZZOCREA, A.; SONG, I-Y.; DAVIS, K. C. Analytics over large-scale multidimensional data: the big data revolution!. In: DOLAP 11: INTERNATIONAL WORKSHOP ON DATA WAREHOUSING AND OLAP, 14., 2011, Glasgow Scotland. Proceedings [...]. New York: Association for Computing Machinery, 2011. p. 101-104.
DEB NATH, R. P.; HOSE, K.; PEDERSEN, T. B. Towards a programmable semantic extract-transform-load framework for semantic data warehouses. In: PROCEEDINGS OF THE ACM EIGHTEENTH INTERNATIONAL WORKSHOP ON DATA WAREHOUSING AND OLAP, 24., 2015, Melbourne. Proceedings [...]. New York: Association for Computing Machinery, 2015. p. 15-24.
DONG, X. L.; SRIVASTAVA, D. Big data integration. In: INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE), 29., 2013, Brisbane. Proceedings [...].Brisbane: IEEE, 2013. p. 1245-1248.
FAELDON, J.; ESPANA, K.; SABIDO, D. J. Data-centric HPC for numerical weather forecasting. In: INTERNATIONAL CONFERENCE ON PARALLEL PROCESSING WORKSHOPS, 43., 2014, Minneapolis. Proceedings [...].Minneapolis: IEEE, 2014. p. 79-84.
FATHY, N.; GAD, W.; BADR, N. A Unified Access to Heterogeneous big data through ontology-based semantic integration. In: INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING AND INFORMATION SYSTEMS (ICICIS), 9., 2019, Cairo. Proceedings [...]. Cairo: IEEE, 2019. p. 387-392.
GAO, J.; XIAO, J. Research on heterogeneous data access and integration model based on OGSA-DAI. In: INTERNATIONAL CONFERENCE ON COMPUTATIONAL AND INFORMATION SCIENCES, 2013, Shiyang. Proceedings [...].Shiyang: IEEE, 2013. p. 1690-1693.
GHOSH, R.; HAIDER, S.; SEN, S. An integrated approach to deploy data warehouse in business intelligence environment. In: INTERNATIONAL CONFERENCE ON COMPUTER, COMMUNICATION, CONTROL AND INFORMATION TECHNOLOGY (C3IT). 13., 2015, Hooghly. Proceedings [...].Hooghly: IEEE, 2015. p. 1-4.
GUO, S.; DONG, X. L.; SRIVASTAVA, D.. Record linkage with uniqueness constraints and erroneous values. Proceedings of the VLDB Endowment, [S. l.], v. 3, n. 1/2, p. 417-428, 2010.
KADADI, A; AGRAWAL, R.; NYAMFUL, C.; ATIQ, R. Challenges of data integration and interoperability in big data. In: IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2014, Washington. Proceedings [...].Washington: IEEE, 2014. p. 38-40.
KELLER, R.; RANJAN, S.; WEI, M. Y; ESHOW, M. M. Semantic representation and scale-up of integrated air traffic management data. In: SBD 16: INTERNATIONAL WORKSHOP ON SEMANTIC BIG DATA, 16., 2016, San Francisco. Proceedings [...]. New York: Association for Computing Machinery , 2016. p. 1-6.
KIMBALL, R.; ROSS, M. The data warehouse toolkit: the complete guide to dimensional modeling. 2nd. ed. New York: John Wiley & Sons, 2011.
KOMAMIZU, T.; AMAGASA, T.; KITAGAWA, H. SPOOL: a SPARQL-based ETL framework for OLAP over linked data. In: INTERNATIONAL CONFERENCE ON INFORMATION INTEGRATION AND WEBBASED APPLICATIONS & SERVICES, 15., 2015, Brussels. Proceedings [...]. New York: Association for Computing Machinery, 2015. p. 1-10.
LE-PHUOC, D.; QUOC, H. N. M.; QUOC, H. N.; NHAT, T. T.; HAUSWIRTH, M. The graph of things: a step towards the live knowledge graph of connected things. Journal of Web Semantics, London, v. 37, p. 25-35, 2016.
LI, X., DONG, L.; LYONS, K.; MENG, W.; SRIVASTAVA, D. Truth finding on the deep Web: Is the problem solved?. Proceedings of the VLDB Endowment, [S. l.], v. 6, n. 2, p. 97-108, 2012.
MADKOUR, A.; AREF, W. G.; BASALAMAH, S. Knowledge cubes: a proposal for scalable and semantically-guided management of Big Data. In: IEEE INTERNATIONAL CONFERENCE ON BIG DATA, 2013, Silicon Valley. Proceedings [...].Silicon Valley: IEEE, 2013. p. 1-7.
MALVIYA, Ayushi; UDHANI, Amit; SONI, Suryakant. R-tool: Data analytic framework for big data. In: SYMPOSIUM ON COLOSSAL DATA ANALYSIS AND NETWORKING (CDAN), 2016, Indore. Proceedings [...].Indore: IEEE, 2016. p. 1-5.
MCDANIEL, M.; STOREY, V. C. Evaluating domain ontologies: clarification, classification, and challenges. ACM Computing Surveys, New York, v. 52, n. 4, p. 1-44, 2019.
MOUNTASSER, I.; OUHBI, B.; FRIKH, B. From data to wisdom: a new multi-layer prototype for Big Data management process. In: INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS DESIGN AND APPLICATIONS (ISDA), 15., 2015, Marrakech. Proceedings [...].Marrakech: IEEE, 2015. p. 104-109.
NADAL, S.; ROMERO, O.; ABELLÓ, A.; VASSILIADIS, P.; VANSUMMEREN, S. An integration-oriented ontology to govern evolution in big data ecosystems. Information Systems, Elmsford, v. 79, p. 3-19, 2019.
NIMMAGADDA, S. L.; DREHER, H. V. Big-data integration methodologies for effective management and data mining of petroleum digital ecosystems. In: IEEE INTERNATIONAL CONFERENCE ON DIGITAL ECOSYSTEMS AND TECHNOLOGIES (DEST), 7, 2013, Menlo Park. Proceedings [...].Menlo Park: IEEE, 2013. p. 148-153.
NUGRAHENI, E.; AKBAR, S.; SAPTAWATI, G. Ayu Putri. Framework of semantic data warehouse for heterogeneous and incomplete data. In: IEEE REGION 10 SYMPOSIUM (TENSYMP), 2016, Bali. Proceedings [...]. Bali: IEEE, 2016. p. 161-166.
OSTROWSKI, D.; RYCHTYCKYJ, N.; MACNEILLE, P.; KIM, M. integration of big data using semantic web technologies. In: IEEE TENTH INTERNATIONAL CONFERENCE ON SEMANTIC COMPUTING (ICSC), 2016, Laguna Hills. Proceedings [...].Laguna Hills: IEEE, 2016. p. 382-385.
PELEKIS, N.; THEODORIDIS, Y.; JANSSENS, D. On the management and analysis of our lifesteps. ACM SIGKDD Explorations Newsletter, [S. l.], v. 15, n. 1, p. 23- 32, 2014.
QIN, H.-F.; QIAN, Z.-M.; ZHAO, Y.-C. On the research of data warehouse in big data. In: INTERNATIONAL CONFERENCE ON NETWORK AND INFORMATION SYSTEMS FOR COMPUTERS, 2015, Wuhan. Proceedings [...].Wuhan: IEEE, 2015. p. 354-357.
SAES, K. R. Abordagem para integração automática de dados estruturados e não estruturados em um contexto Big Data. 2019. Tese (Doutorado) - Universidade de São Paulo, São Paulo, 2019.
SHAFIEE, M. E.; BARKER, Z.; RASEKH, A. Enhancing water system models by integrating big data. Sustainable cities and Society, London, v. 37, p. 485-491, 2018.
YAHYA, F.; FAZLI, B. M.; ABDULLAH, M. F.; ZULKIFLI, H. Extending the national lake database of malaysia (mylake) as a central data exchange using big data integration. In: INTERNATIONAL CONFERENCE ON DATA SCIENCE AND INFORMATION TECHNOLOGY, 2., 2019, Seoul. Proceedings [...]. New York: Association for Computing Machinery, 2019. p. 30-35.
ALKHAMISI, A. O.; SALEH, M. Ontology opportunities and challenges: discussions from semantic data integration perspectives. In: CONFERENCE ON DATA SCIENCE AND MACHINE LEARNING APPLICATIONS (CDMA). 6., 2020, Riyadh. Proceedings [...]. Riyadh: IEEE, 2020. p. 134-140.
ALQARNI, A. A.; PARDEDE, E. Integration of data warehouse and unstructured business documents. In: INTERNATIONAL CONFERENCE ON NETWORK-BASED INFORMATION SYSTEMS, 15., 2012, Melbourne. Proceedings [...]. Melbourne: IEEE, 2012. p. 32-37.
ARPUTHAMARY, B.; AROCKIAM, L. Data Integration in big data environment. Bonfring International Journal of Data Mining, Tamilnadu, v. 5, n. 1, p. 1-5, 2015.
ASSAF, A.; LOUW, E.; SENART, A.; FOLLENFANT, C.; TRONCY, R.; TRASTOUR, D. RUBIX: a framework for improving data integration with linked data. In: WOD 12: INTERNATIONAL WORKSHOP ON OPEN DATA, 1., 2012, Nantes. Proceedings [...]. New York: Association for Computing Machinery, 2012. p. 13-21.
AUFAURE, M.-A.; CHIKY, R.; CURÉ, O.; KHROUF, H.; KEPEKLIAN, G. From business intelligence to semantic data stream management. Future Generation Computer Systems, London, v. 63, p. 100-107, 2016.
BALA, M. BOUSSAID, O.; ALIMAZIGHI, Z. P-ETL: Parallel-ETL based on the MapReduce paradigm. In: INTERNATIONAL CONFERENCE ON COMPUTER SYSTEMS AND APPLICATIONS (AICCSA), 11., 2014, Doha. Proceedigns [...]. Doha: IEEE, 2014. p. 42-49.
BONDAREV, A.; ZAKIROV, D. Data warehouse on Hadoop platform for decision support systems in education. In: INTERNATIONAL CONFERENCE ON ELECTRONICS COMPUTER AND COMPUTATION (ICECCO), 12., 2015, Almaty. Proceedings [...].Almaty: Suleyman Demirel University, 2015. p. 1-4.
BORTOLI, S.; BOUQUET, P.; POMPERMAIER, F.; MOLINARI, A. Semantic big data for tax assessment. In: SBD 16: INTERNATIONAL WORKSHOP ON SEMANTIC BIG DATA, 16., 2016, San Francisco Califórnia. Proceedings [...]. New York: Association for Computing Machinery, 2016. p. 1-6.
CHEN, W.; WANG, R.; WU, R.; TANG, L.; FAN, J. Multi-source and heterogeneous data integration model for big data analytics in power DCS. In: INTERNATIONAL CONFERENCE ON CYBER-ENABLED DISTRIBUTED COMPUTING AND KNOWLEDGE DISCOVERY (CYBERC), 2016, Chengdu. Proceedings [...]. Chengdu: IEEE, 2016. p. 238-242.
CHOI, T.-M.; CHAN, H. K.; YUE, X. Recent development in big data analytics for business operations and risk management. IEEE transactions on cybernetics, New York, v. 47, n. 1, p. 81-92, 2016.
CUZZOCREA, A.; DIAMANTINI, C.; GENGA, L.; POTENA, D.; STORTI, E. A composite methodology for supporting collaboration pattern discovery via semantic enrichment and multidimensional analysis. In: INTERNATIONAL CONFERENCE OF SOFT COMPUTING AND PATTERN RECOGNITION (SOCPAR), 6., 2014, Tunis. Proceedings [...]. Tunis: IEEE, 2014. p. 459-464.
CUZZOCREA, A.; SACCÀ, D.; ULLMAN, J. D. Big data: a research agenda. In: IDEAS 13: INTERNATIONAL DATABASE ENGINEERING & APPLICATIONS SYMPOSIUM, 17., 2013, Barcelona. Proceedings [...]. New York: Association for Computing Machinery, 2013. p. 198-203.
CUZZOCREA, A.; SONG, I-Y.; DAVIS, K. C. Analytics over large-scale multidimensional data: the big data revolution!. In: DOLAP 11: INTERNATIONAL WORKSHOP ON DATA WAREHOUSING AND OLAP, 14., 2011, Glasgow Scotland. Proceedings [...]. New York: Association for Computing Machinery, 2011. p. 101-104.
DEB NATH, R. P.; HOSE, K.; PEDERSEN, T. B. Towards a programmable semantic extract-transform-load framework for semantic data warehouses. In: PROCEEDINGS OF THE ACM EIGHTEENTH INTERNATIONAL WORKSHOP ON DATA WAREHOUSING AND OLAP, 24., 2015, Melbourne. Proceedings [...]. New York: Association for Computing Machinery, 2015. p. 15-24.
DONG, X. L.; SRIVASTAVA, D. Big data integration. In: INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE), 29., 2013, Brisbane. Proceedings [...].Brisbane: IEEE, 2013. p. 1245-1248.
FAELDON, J.; ESPANA, K.; SABIDO, D. J. Data-centric HPC for numerical weather forecasting. In: INTERNATIONAL CONFERENCE ON PARALLEL PROCESSING WORKSHOPS, 43., 2014, Minneapolis. Proceedings [...].Minneapolis: IEEE, 2014. p. 79-84.
FATHY, N.; GAD, W.; BADR, N. A Unified Access to Heterogeneous big data through ontology-based semantic integration. In: INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING AND INFORMATION SYSTEMS (ICICIS), 9., 2019, Cairo. Proceedings [...]. Cairo: IEEE, 2019. p. 387-392.
GAO, J.; XIAO, J. Research on heterogeneous data access and integration model based on OGSA-DAI. In: INTERNATIONAL CONFERENCE ON COMPUTATIONAL AND INFORMATION SCIENCES, 2013, Shiyang. Proceedings [...].Shiyang: IEEE, 2013. p. 1690-1693.
GHOSH, R.; HAIDER, S.; SEN, S. An integrated approach to deploy data warehouse in business intelligence environment. In: INTERNATIONAL CONFERENCE ON COMPUTER, COMMUNICATION, CONTROL AND INFORMATION TECHNOLOGY (C3IT). 13., 2015, Hooghly. Proceedings [...].Hooghly: IEEE, 2015. p. 1-4.
GUO, S.; DONG, X. L.; SRIVASTAVA, D.. Record linkage with uniqueness constraints and erroneous values. Proceedings of the VLDB Endowment, [S. l.], v. 3, n. 1/2, p. 417-428, 2010.
KADADI, A; AGRAWAL, R.; NYAMFUL, C.; ATIQ, R. Challenges of data integration and interoperability in big data. In: IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2014, Washington. Proceedings [...].Washington: IEEE, 2014. p. 38-40.
KELLER, R.; RANJAN, S.; WEI, M. Y; ESHOW, M. M. Semantic representation and scale-up of integrated air traffic management data. In: SBD 16: INTERNATIONAL WORKSHOP ON SEMANTIC BIG DATA, 16., 2016, San Francisco. Proceedings [...]. New York: Association for Computing Machinery , 2016. p. 1-6.
KIMBALL, R.; ROSS, M. The data warehouse toolkit: the complete guide to dimensional modeling. 2nd. ed. New York: John Wiley & Sons, 2011.
KOMAMIZU, T.; AMAGASA, T.; KITAGAWA, H. SPOOL: a SPARQL-based ETL framework for OLAP over linked data. In: INTERNATIONAL CONFERENCE ON INFORMATION INTEGRATION AND WEBBASED APPLICATIONS & SERVICES, 15., 2015, Brussels. Proceedings [...]. New York: Association for Computing Machinery, 2015. p. 1-10.
LE-PHUOC, D.; QUOC, H. N. M.; QUOC, H. N.; NHAT, T. T.; HAUSWIRTH, M. The graph of things: a step towards the live knowledge graph of connected things. Journal of Web Semantics, London, v. 37, p. 25-35, 2016.
LI, X., DONG, L.; LYONS, K.; MENG, W.; SRIVASTAVA, D. Truth finding on the deep Web: Is the problem solved?. Proceedings of the VLDB Endowment, [S. l.], v. 6, n. 2, p. 97-108, 2012.
MADKOUR, A.; AREF, W. G.; BASALAMAH, S. Knowledge cubes: a proposal for scalable and semantically-guided management of Big Data. In: IEEE INTERNATIONAL CONFERENCE ON BIG DATA, 2013, Silicon Valley. Proceedings [...].Silicon Valley: IEEE, 2013. p. 1-7.
MALVIYA, Ayushi; UDHANI, Amit; SONI, Suryakant. R-tool: Data analytic framework for big data. In: SYMPOSIUM ON COLOSSAL DATA ANALYSIS AND NETWORKING (CDAN), 2016, Indore. Proceedings [...].Indore: IEEE, 2016. p. 1-5.
MCDANIEL, M.; STOREY, V. C. Evaluating domain ontologies: clarification, classification, and challenges. ACM Computing Surveys, New York, v. 52, n. 4, p. 1-44, 2019.
MOUNTASSER, I.; OUHBI, B.; FRIKH, B. From data to wisdom: a new multi-layer prototype for Big Data management process. In: INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS DESIGN AND APPLICATIONS (ISDA), 15., 2015, Marrakech. Proceedings [...].Marrakech: IEEE, 2015. p. 104-109.
NADAL, S.; ROMERO, O.; ABELLÓ, A.; VASSILIADIS, P.; VANSUMMEREN, S. An integration-oriented ontology to govern evolution in big data ecosystems. Information Systems, Elmsford, v. 79, p. 3-19, 2019.
NIMMAGADDA, S. L.; DREHER, H. V. Big-data integration methodologies for effective management and data mining of petroleum digital ecosystems. In: IEEE INTERNATIONAL CONFERENCE ON DIGITAL ECOSYSTEMS AND TECHNOLOGIES (DEST), 7, 2013, Menlo Park. Proceedings [...].Menlo Park: IEEE, 2013. p. 148-153.
NUGRAHENI, E.; AKBAR, S.; SAPTAWATI, G. Ayu Putri. Framework of semantic data warehouse for heterogeneous and incomplete data. In: IEEE REGION 10 SYMPOSIUM (TENSYMP), 2016, Bali. Proceedings [...]. Bali: IEEE, 2016. p. 161-166.
OSTROWSKI, D.; RYCHTYCKYJ, N.; MACNEILLE, P.; KIM, M. integration of big data using semantic web technologies. In: IEEE TENTH INTERNATIONAL CONFERENCE ON SEMANTIC COMPUTING (ICSC), 2016, Laguna Hills. Proceedings [...].Laguna Hills: IEEE, 2016. p. 382-385.
PELEKIS, N.; THEODORIDIS, Y.; JANSSENS, D. On the management and analysis of our lifesteps. ACM SIGKDD Explorations Newsletter, [S. l.], v. 15, n. 1, p. 23- 32, 2014.
QIN, H.-F.; QIAN, Z.-M.; ZHAO, Y.-C. On the research of data warehouse in big data. In: INTERNATIONAL CONFERENCE ON NETWORK AND INFORMATION SYSTEMS FOR COMPUTERS, 2015, Wuhan. Proceedings [...].Wuhan: IEEE, 2015. p. 354-357.
SAES, K. R. Abordagem para integração automática de dados estruturados e não estruturados em um contexto Big Data. 2019. Tese (Doutorado) - Universidade de São Paulo, São Paulo, 2019.
SHAFIEE, M. E.; BARKER, Z.; RASEKH, A. Enhancing water system models by integrating big data. Sustainable cities and Society, London, v. 37, p. 485-491, 2018.
YAHYA, F.; FAZLI, B. M.; ABDULLAH, M. F.; ZULKIFLI, H. Extending the national lake database of malaysia (mylake) as a central data exchange using big data integration. In: INTERNATIONAL CONFERENCE ON DATA SCIENCE AND INFORMATION TECHNOLOGY, 2., 2019, Seoul. Proceedings [...]. New York: Association for Computing Machinery, 2019. p. 30-35.
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2021-06-30
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Campos, V. V. de S., Brancher, J. D., Farias, F. P., Mioni, J. L. V. M., & Brahim, P. L. G. (2021). Revisão e comparação de trabalhos sobre dados heterogêneos e análise semântica em Big Data. Semina: Ciências Exatas E Tecnológicas, 42(1), 113–128. https://doi.org/10.5433/1679-0375.2021v42n1p113
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