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A Review of Horizontal Fragmentation Methods Considering Multimedia Data and Dynamic Access Patterns

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New Perspectives in Software Engineering (CIMPS 2021)

Abstract

Horizontal fragmentation methods are widely used in databases to improve query performance. This paper aims to present a systematic literature review on horizontal fragmentation methods applied to databases. Given the importance of multimedia applications today, this review aims to determine whether the methods consider multimedia data to obtain their partitioning schemes; whether they are dynamic, i.e., can adjust their output to access patterns or workload changes; and are based on a cost model focused on reducing query execution cost. We also consider the completeness (i.e., the paper presents all the information needed to implement the method) and implementation easiness. To meet this objective, we analyzed and classified 46 documents on horizontal fragmentation techniques. Subsequently, we selected the best approach from our comparative analysis. Finally, we presented the method workflow and architecture of a web application that implements a dynamic horizontal fragmentation technique for multimedia databases to optimize its performance and information availability.

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References

  1. Patil, M.M., Hanni, A., Tejeshwar, C.H., Patil, P.: A qualitative analysis of the performance of MongoDB vs MySQL database based on insertion and retriewal operations using a web/android application to explore load balancing — Sharding in MongoDB and its advantages. In: 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) (2017)

    Google Scholar 

  2. Liming, D., Weindong, L., Jie, S.: coexistence of multiple partition plan based physical database design. In: Proceedings of the 5th International Conference on Communications and Broadband Networking, New York, NY, USA (2017)

    Google Scholar 

  3. Chen, Z., et al.: The data partition strategy based on hybrid range consistent hash in NoSQL database. In: Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service, New York, NY, USA (2013)

    Google Scholar 

  4. Özsu, M.T., Valduriez, P.: Principles of Distributed Database Systems, Springer International Publishing (2020)

    Google Scholar 

  5. Curino, C., Jones, E., Zhang, Y., Madden, S.: Schism: a workload-driven approach to database replication and partitioning. Proc. VLDB Endowment. 3, 48–57 (2010)

    Article  Google Scholar 

  6. Ramachandran, R., Nair, D.P., Jasmi, J.: A horizontal fragmentation method based on data semantics. In: 2016 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC) (2016)

    Google Scholar 

  7. Guo, M., Kang, H.: The implementation of database partitioning based on streaming framework. In: 2016 13th Web Information Systems and Applications Conference (WISA) (2016)

    Google Scholar 

  8. Kamal, J.M.M., Murshed, M., Buyya, R.: Workload-aware incremental repartitioning of shared-nothing distributed databases for scalable cloud applications. In: 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing (2014)

    Google Scholar 

  9. Pavlo, A., Curino, C., Zdonik, S.: Skew-aware automatic database partitioning in shared-nothing, parallel OLTP systems. In: Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data, New York, NY, USA (2012)

    Google Scholar 

  10. Zamanian, E., Shun, J., Binnig, C., Kraska, T.: Chiller: contention-centric transaction execution and data partitioning for modern networks. In: Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data, New York, NY, USA (2020)

    Google Scholar 

  11. Castro-Medina, F., Rodriguez-Mazahua, L., López-Chau, A., Abud-Figueroa, M.A., Alor-Hernández, G.: Fragment: a web application for database fragmentation. allocation and replication over a cloud environment. IEEE Latin Am. Trans. 6, 1126–1134 (2020)

    Google Scholar 

  12. Rodríguez Arauz, M.J., Rodriguez-Mazahua, L., Arrioja-Rodríguez, M.L., Abud-Figueroa, M.A., Peláez-Camarena, S.G., Martínez-Méndez, L.d.C.: Design of a multimedia data management system that uses horizontal fragmentation to optimize content-based queries. In: IMMM 2020, The Tenth International Conference on Advances in Information Mining and Management (2020)

    Google Scholar 

  13. Taft, R., et al.: E-store: fine-grained elastic partitioning for distributed transaction processing systems. Proceedings of the VLDB Endowment. 3, 24–256 (2014)

    Google Scholar 

  14. Quamar, A., Kumar, K.A., Deshpande, A.: Sword: scalable workload-aware data placement for transactional workloads. In: Proceedings of the 16th International Conference on Extending Database Technology (2013)

    Google Scholar 

  15. Abebe, M., Glasbergen, B., Daudjee, K.: MorphoSys: automatic physical design metamorphosis for distributed database systems. Proc. VLDB Endowment. 13, 3573–3587 (2020)

    Article  Google Scholar 

  16. Tzoumas, K., Deshpande, A., Jensen, C.S.: Sharing-aware horizontal partitioning for exploiting correlations during query processing. In:Proceedings of the VLDB Endowment. 1, 542--553 (2010).

    Google Scholar 

  17. Zamanian, E., Binnig, C., Salama, A.: Locality-aware partitioning in parallel database systems. In: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, New York, NY, USA (2015)

    Google Scholar 

  18. Marcus, R., Papaemmanouil, O., Semenova, S., Garber, S.: NashDB: an End-to-End economic method for elastic database fragmentation, replication, and provisioning. In: Proceedings of the 2018 International Conference on Management of Data, New York, NY, USA (2018)

    Google Scholar 

  19. Abdalla, H.I., Amer, A.A.: Dynamic horizontal fragmentation, replication and allocation model in DDBSs. In: 2012 International Conference on Information Technology and e-Services (2012)

    Google Scholar 

  20. Peng, P., Zou, L., Chen, L., Zhao, D.: Adaptive distributed RDF graph fragmentation and allocation based on query workload. IEEE Trans. Knowl. Data Eng. 4, 670–685 (2019)

    Article  Google Scholar 

  21. Serafini, M., Taft, R., Elmore, A.J., Pavlo, A., Aboulnaga, A., Stonebraker, M.: Clay: fine-grained adaptive partitioning for general database schemas. Proc. VLDB Endowment 4, 445–456 (2016)

    Article  Google Scholar 

  22. Goli-Malekabadi, Z., Sargolzaei-Javan, M., Akbari, M.K.: An effective model for store and retrieve big health data in cloud computing. Comput. Methods Programs Biomed. 1, 75–82 (2016)

    Article  Google Scholar 

  23. Abdalla, H.I.: A synchronized design technique for efficient data distribution. Comput. Hum. Behav. 1, 427–435 (2014)

    Article  Google Scholar 

  24. Wedashwara, W., Mabu, S., Obayashi, M., Masanao, T.: Combination of genetic network programming and knapsack problem to support record clustering on distributed databases. Expert Syst. Appl. 46, 15–23 (2016)

    Google Scholar 

  25. Amer, A.A., Sewisy, A.A., Elgendy, T.M.A.: An optimized approach for simultaneous horizontal data fragmentation and allocation in Distributed Database Systems (DDBSs). Heliyon 3 (2017)

    Google Scholar 

  26. Lim, L.: Elastic data partitioning for cloud-based SQL processing systems. In: 2013 IEEE International Conference on Big Data (2013)

    Google Scholar 

  27. Wu, Q., Chen, C., Jiang, Y.: Multi-source heterogeneous Hakka culture heritage data management based on MongoDB. In: 2016 Fifth International Conference on Agro-Geoinformatics (Agro-Geoinformatics) (2016)

    Google Scholar 

  28. Oonhawat, B., Nupairoj, N.: Hotspot management strategy for real-time log data in MongoDB. In: 2017 19th International Conference on Advanced Communication Technology (ICACT) (2017)

    Google Scholar 

  29. Sauer, B., Hao, W.: Horizontal cloud database partitioning with data mining techniques In: 2015 12th Annual IEEE Consumer Communications and Networking Conference (CCNC) (2015)

    Google Scholar 

  30. Fasolin, K., et al.: Efficient execution of conjunctive complex queries on big multimedia databases. In: 2013 IEEE International Symposium on Multimedia (2013)

    Google Scholar 

  31. Lwin, N.K.Z., Naing, T.M.: Non-redundant dynamic fragment allocation with horizontal partition in distributed database system. In: 2018 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS) (2018)

    Google Scholar 

  32. Kumar, R., Gupta, N.: An extended approach to Non-Replicated dynamic fragment allocation in distributed database systems. In: 2014 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT) (2014)

    Google Scholar 

  33. Fetai, I., Murezzan, D., Schuldt, H.: Workload-driven adaptive data partitioning and distribution — The Cumulus approach. In: 2015 IEEE International Conference on Big Data (Big Data) (2015)

    Google Scholar 

  34. Herrmann, K., Voigt, H., Lehner, W.: Cinderella — Adaptive online partitioning of irregularly structured data. In: 2014 IEEE 30th International Conference on Data Engineering Workshops (2014)

    Google Scholar 

  35. Khan, S.I.: Efficient partitioning of large databases without query statistics. Database Syst. J. 7, 34–53 (2016)

    Google Scholar 

  36. Baron, C., Iacob, N.M.: A new dynamic data fragmentation and replication model in DDBMSs. Cost Functions. Knowl. Horiz. - Econ. 6, 158–161 (2014)

    Google Scholar 

  37. Elghamrawy, S.M.: An adaptive load-balanced partitioning module in cassandra using rendezvous hashing. In: Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2016, Cham (2016)

    Google Scholar 

  38. Hauglid, J.O., Ryeng, N.H., Nørvåg, K.: DYFRAM: dynamic fragmentation and replica management in distributed database systems. Distrib. Parallel Databases. 1, 157–185 (2010)

    Article  Google Scholar 

  39. Teng, D., Kong, J., Wang, F.: Scalable and flexible management of medical image big data. Distrib. Parallel Databases 37(2), 235–250 (2018). https://doi.org/10.1007/s10619-018-7230-8

    Article  Google Scholar 

  40. Elghamrawy, S.M., Hassanien, A.E.: A partitioning framework for Cassandra NoSQL database using Rendezvous hashing. J. Supercomput. 73(10), 4444–4465 (2017). https://doi.org/10.1007/s11227-017-2027-5

    Article  Google Scholar 

  41. Abdel Raouf, A.E., Badr, N.L., Tolba, M.F.: Distributed Database System (DSS) design over a cloud environment. In: Hassanien, A.E., Fouad, M.M., Manaf, A.A., Zamani, M., Ahmad, R., Kacprzyk, J. (eds.) Multimedia Forensics and Security. ISRL, vol. 115, pp. 97–116. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-44270-9_5

    Chapter  Google Scholar 

  42. Rodríguez-Mazahua, L., Alor-Hernández, G., Abud-Figueroa, M.A., Peláez-Camarena, S.G.: Horizontal partitioning of multimedia databases using hierarchical agglomerative clustering. In: Gelbukh, A., Espinoza, F.C., Galicia-Haro, S.N. (eds.) MICAI 2014. LNCS (LNAI), vol. 8857, pp. 296–309. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-13650-9_27

    Chapter  Google Scholar 

  43. Feinerer, I., Franconi, E., Guagliardo, P.: Lossless horizontal decomposition with domain constraints on interpreted attributes. In: Gottlob, G., Grasso, G., Olteanu, D., Schallhart, C. (eds.) BNCOD 2013. LNCS, vol. 7968, pp. 77–91. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-39467-6_10

    Chapter  Google Scholar 

  44. Salmi, C., Chaabani, M., Mezghiche, M.: A formalized procedure for database horizontal fragmentation in Isabelle/HOL proof assistant. In: Model and Data Engineering, Cham (2018)

    Google Scholar 

  45. Olma, M., Karpathiotakis, M., Alagiannis, I., Athanassoulis, M., Ailamaki, A.: Adaptive partitioning and indexing for in situ query processing. VLDB J. 29(1), 569–591 (2019). https://doi.org/10.1007/s00778-019-00580-x

    Article  Google Scholar 

  46. Liroz-Gistau, M., Akbarinia, R., Pacitti, E., Porto, F., Valduriez, P.: Dynamic workload-based partitioning algorithms for continuously growing databases. In: Hameurlain, A., Küng, J., Wagner, R. (eds.) Transactions on Large-Scale Data- and Knowledge-Centered Systems XII. LNCS, vol. 8320, pp. 105–128. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-45315-1_5

    Chapter  Google Scholar 

  47. Liroz-Gistau, M., Akbarinia, R., Pacitti, E., Porto, F., Valduriez, P.: Dynamic workload-based partitioning for large-scale databases. In: Liddle, S.W., Schewe, K.-D., Tjoa, A.M., Zhou, X. (eds.) DEXA 2012. LNCS, vol. 7447, pp. 183–190. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32597-7_16

    Chapter  Google Scholar 

  48. Conozca más sobre la tecnología Java. https://www.java.com/es/about/

  49. JavaServer Faces Technology. https://www.oracle.com/java/technologies/javaserverfaces.html

  50. NetBeans IDE – Overview. https://netbeans.org/features/index.html

  51. Nieves Guerrero, C., Ucán Pech, J., Menéndez Domínguez, V.: UWE en Sistema de Recomendación de Objetos de Aprendizaje. Aplicando Ingeniería Web: Un Método en Caso de Estudio. Revista Latinoamericana de Ingenieria de Software, 2, 137–143 (2014)

    Google Scholar 

  52. What Is MongoDB., https://www.mongodb.com/what-is-mongodb

  53. Deploy Cloud Applications with MySQL Database. https://www.oracle.com/mysql/

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Acknowledgments

The authors are very grateful to the Tecnológico Nacional de México (TecNM) for supporting this research work. This work was also sponsored by the Secretaria de Educación Pública (SEP) and by the Consejo Nacional de Ciencia y Tecnología (CONACYT) through the Fondo Sectorial de Investigación para la Educación, grant number A1-S-51808.

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Correspondence to Lisbeth Rodríguez-Mazahua .

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Castillo-García, A., Rodríguez-Mazahua, L., Castro-Medina, F., Olivares-Zepahua, B.A., Abud-Figueroa, M.A. (2022). A Review of Horizontal Fragmentation Methods Considering Multimedia Data and Dynamic Access Patterns. In: Mejia, J., Muñoz, M., Rocha, Á., Avila-George, H., Martínez-Aguilar, G.M. (eds) New Perspectives in Software Engineering. CIMPS 2021. Advances in Intelligent Systems and Computing, vol 1416. Springer, Cham. https://doi.org/10.1007/978-3-030-89909-7_6

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