Método de Fragmentación Híbrida Dinámica para Bases de Datos Multimedia
Resumen
Las bases de datos multimedia almacenan datos de gran tamaño, provocando problemas en la recuperación eficiente de la información, aumentando los costos de ejecución y tiempos de respuesta de las consultas. Para resolver estos problemas, existen técnicas de fragmentación de datos que permiten mejorar el desempeño de las consultas, aumentar la disponibilidad de información y ejecutar eficientemente más operaciones accediendo menos a datos irrelevantes. En este artículo, se presenta una revisión exhaustiva de 34 métodos de fragmentación híbrida y posteriormente, se propone el diseño de un método de fragmentación híbrida que adapte el esquema de acuerdo con los cambios en la carga de trabajo para mantener la recuperación eficiente de datos multimedia. Las tecnologías deseadas para la implementación del diseño propuesto son el lenguaje de programación Java, el marco de trabajo JSF (JavaServer Faces), los sistemas gestores de bases de datos MySQL y MongoDB, y el entorno de desarrollo integrado NetBeans; siguiendo la metodología de ingeniería Web basada en el lenguaje unificado de modelado (UWE).
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Ahmed, Z. J., & Alluhaibi, S. T. (2022). Hybrid Data Fragmentation Using Genetic Killer Whale Optimization-Based Clustering Model. Journal of Pharmaceutical Negative Results, 13(SO2). https://doi.org/10.47750/pnr.2022.13.s02.39
Al-Kateb, M., Sinclair, P., Au, G., & Ballinger, C. (2016). Hybrid row-column partitioning in teradata ®. Proc. of the VLDB Endowment, 9(13), 1353–1364. https://doi.org/10.14778/3007263.3007273
Awad, A. S., Yousif, A., & Kadoda, G. (2019). Enhanced Model for Cloud Data Security based on Searchable Encryption and Hybrid Fragmentation. 2019 Int. Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE), 1–4. https://doi.org/10.1109/ICCCEEE46830.2019.9070918
Azila, A., Fauzi, C., Fariza, W., Rahman, W. A., Fauzi, A., & Weigelt, F. (2021). Managing Fragmented Database in Distributed Database Environment. In Journal of Mathematics and Computing Science (Vol. 7, Issue 1).
Badran, S., Arman, N., & Farajallah, M. (2020, November 28). Towards a hybrid data partitioning technique for secure data outsourcing. Proc. - 2020 21st Int. Arab Conference on Information Technology, ACIT 2020. https://doi.org/10.1109/ACIT50332.2020.9300064
Badran, S., Arman, N., & Farajallah, M. (2021). An Efficient Approach for Secure Data Outsourcing using Hybrid Data Partitioning. 2021 Int. Conference on Information Technology, ICIT 2021 - Proceedings, 418–423. https://doi.org/10.1109/ICIT52682.2021.9491745
Cantini, R., Marozzo, F., Orsino, A., Talia, D., Trunfio, P., Badia, R. M., Ejarque, J., & Vazquez, F. (2022). Block size estimation for data partitioning in HPC applications using machine learning techniques. http://arxiv.org/abs/2211.10819
Castro-Medina, F., Rodríguez-Mazahua, L., López-Chau, A., Cervantes, J., Alor-Hernández, G., & Machorro-Cano, I. (2020). Application of dynamic fragmentation methods in multimedia databases: A review. In Entropy (Vol. 22, Issue 12, pp. 1–25). MDPI AG. https://doi.org/10.3390/e22121352
Chawla, T., Singh, G., & Pilli, E. S. (2019). HYPSO: Hybrid partitioning for big RDF storage and query processing. ACM International Conference Proceeding Series, 188–194. https://doi.org/10.1145/3297001.3297025
Chbeir, R., & Laurent, D. (2010). Enhancing Multimedia Data Fragmentation. In Journal of Multimedia Processing and Technologies (Vol. 1, Issue 2).
Chen, K., Zhou, Y., & Cao, Y. (2015). Online data partitioning in distributed database systems. EDBT 2015 - 18th International Conference on Extending Database Technology, Proceedings, 1–12. https://doi.org/10.5441/002/edbt.2015.02
Chen, S., Chi, C. H., Ding, C., & Wong, R. K. (2013). Data decomposition based partial replication model for software services. Proceedings - IEEE 10th International Conference on Services Computing, SCC 2013, 256–263. https://doi.org/10.1109/SCC.2013.83
Durand, G. C., Pinnecke, M., Piriyev, R., Mohsen, M., Broneske, D., Saake, G., Sekeran, M. S., Rodriguez, F., & Balami, L. (2018). GridFormation: Towards Self-Driven Online Data Partitioning using Reinforcement Learning. Proc. of the First Int. Workshop on Exploiting Artificial Intelligence Techniques for Data Management, 1–7. https://doi.org/10.1145/3211954.3211956
Gorla, N., Ng, V., & Law, D. M. (2012). Improving database performance with a mixed fragmentation design. Journal of Intelligent Information Systems, 39(3), 559–576. https://doi.org/10.1007/s10844-012-0203-x
Harikumar, S., & Ramachandran, R. (2015, April 21). Hybridized fragmentation of very large databases using clustering. 2015 IEEE Int. Conference on Signal Processing, Informatics, Communication and Energy Systems, SPICES 2015. https://doi.org/10.1109/SPICES.2015.7091488
Jindal, A., & Dittrich, J. (2012). Relax and Let the Database Do the Partitioning Online (Vol. 126, pp. 65–80). Springer. https://doi.org/10.1007/978-3-642-33500-6_5
Kang, D., Jiang, R., & Blanas, S. (2021). Jigsaw: A Data Storage and Query Processing Engine for Irregular Table Partitioning. Proc. of the ACM SIGMOD Int. Conference on Management of Data, 898–911. https://doi.org/10.1145/3448016.3457547
Kechar, M., & Nait Bahloul, S. (2014). Hybrid Fragmentation of XML Data Warehouse Using K-MEANS Algorithm.
Kling, P., Özsu, M. T., & Daudjee, K. (2011). Scaling XML query processing: Distribution, localization and pruning. Distributed and Parallel Databases, 29(5–6), 445–490. https://doi.org/10.1007/s10619-011-7085-8
Koong, K.-L., Haw, S.-C., & Soon, L.-K. (2018). Labeling-Based Hybrid XML Fragmentation Model. Advanced Science Letters, 24(2), 1177–1181. https://doi.org/10.1166/asl.2018.10711
Kulba, V., & Somov, S. (2020). Dynamic Fragment Allocation in Distributed System with Time-Varying Parameters of its Operation. 2020 13th Int. Conference “Management of Large-Scale System Development” (MLSD), 1–4. https://doi.org/10.1109/MLSD49919.2020.9247671
Mourão, A., & Magalhães, J. (2018). Balancing search space partitions by sparse coding for distributed redundant media indexing and retrieval. Int. Journal of Multimedia Information Retrieval, 7(1), 57–70. https://doi.org/10.1007/s13735-017-0140-0
Noraziah, A., Fauzi, A. A. C., Ubaidillah, S. H. S. A., Alkazemi, B., & Odili, J. B. (2021). BVAGQ-AR for Fragmented Database Replication Management. IEEE Access, 9, 56168–56177. https://doi.org/10.1109/ACCESS.2021.3065944
Padiya, T., Kanwar, J. J., & Bhise, M. (2016). Workload aware hybrid partitioning. ACM Int. Conference Proc. Series, 21-23-October-2016, 51–58. https://doi.org/10.1145/2998476.2998479
Patel, M., Yadav, N., & Bhise, M. (2021). Workload aware Cost-based Partial loading of Raw data for Limited Storage Resources. https://www.researchgate.net/publication/357050760
Pinnecke, M., Campero Durand, G., Broneske, D., Zoun, R., & Saake, G. (2020). GridTables: A One-Size-Fits-Most H2TAP Data Store. Datenbank-Spektrum, 20(1), 43–56. https://doi.org/10.1007/s13222-019-00330-x
Rani, S., Koshley, D. K., & Halder, R. (2017). Partitioning-insensitive watermarking approach for distributed relational databases. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10720 LNCS, 172–192. https://doi.org/10.1007/978-3-662-56266-6_8
Rodríguez-Mazahua, L., Alor-Hernández, G., Cervantes, J., López-Chau, A., & Sánchez-Cervantes, J. L. (2016). A hybrid partitioning method for multimedia databases. DYNA, 83(198), 59. https://doi.org/10.15446/dyna.v83n198.50507
Saad, S., Tekli, J., Chbeir, R., & Yetongnon, K. (2006). Towards Multimedia Fragmentation. In Advances in Databases and Information Systems; Lecture Notes in Computer Science; Manolopoulos, Y., Pokorny, J., Sellis, T.K., Eds (Vol. 4152, pp. 415–429). Springer. https://doi.org/10.1007/11827252_31
Safaei, A. A. (2022). Hybrid fragmentation of medical images’ attributes for multidimensional indexing. Cluster Computing, 25(1), 215–230. https://doi.org/10.1007/s10586-021-03356-7
Schreiner, G. A., Duarte, D., Bianco, G. D., & dos Santos Mello, R. (2019). A Hybrid Partitioning Strategy for NewSQL Databases: The VoltDB Case. ACM Int. Conference Proc. Series, 353–360. https://doi.org/https://doi.org/10.1145/3366030.3366062
Schreiner, G. A., Duarte, D., Ronaldo, O. :, & Mello, S. (2018). An autonomous hybrid data partition for NewSQL DBs.
Song, S., & Chen, L. (2013). Indexing dataspaces with partitions. World Wide Web, 16(2), 141–170. https://doi.org/10.1007/s11280-012-0163-7
Sun, L., Franklin, M. J., Wang, J., & Wu, E. (2016). Skipping-oriented partitioning for columnar layouts. Proc. of the VLDB Endowment, 10(4), 421–432. https://doi.org/10.14778/3025111.3025123
Vogt, M., Stiemer, A., & Schuldt, H. (2018). Polypheny-DB: Towards a Distributed and Self-Adaptive Polystore. 2018 IEEE Int. Conference on Big Data (Big Data), 3364–3373. https://doi.org/10.1109/BigData.2018.8622353
Wang, X., Fan, X., Chen, J., & Du, X. (2014). Automatic Data Distribution in Large-scale OLTP Applications. Int. Journal of Database Theory and Application, 7(4), 37–46. https://doi.org/10.14257/ijdta.2014.7.4.04
Derechos de autor 2023 Oscar Crescencio-Rico, Lisbeth Rodríguez-Mazahua, Felipe Castro-Medina, Giner Alor-Hernández, José Luis Sánchez-Cervantes
Esta obra está bajo licencia internacional Creative Commons Reconocimiento-NoComercial-SinObrasDerivadas 4.0.
