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Active rule base development for dynamic vertical partitioning of multimedia databases

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Currently, vertical partitioning has been used in multimedia databases in order to take advantage of its potential benefits in query optimization. Nevertheless, most vertical partitioning algorithms are static; this means that they optimize a vertical partitioning scheme (VPS) according to a workload, but if this workload suffers changes, the VPS may be degraded, which would result in long query response time. This paper presents a set of active rules to perform dynamic vertical partitioning in multimedia databases. First of all, these rules collect all the information that a vertical partitioning algorithm needs as input. Then, they evaluate this information in order to know if the database has experienced enough changes to trigger a performance evaluator. In this case, if the performance of the database falls below a threshold previously calculated by the rules, the vertical partitioning algorithm is triggered, which gets a new VPS. Finally, the rules materialize the new VPS. Our active rule base is implemented in the system DYMOND, which is an active rule-based system for dynamic vertical partitioning of multimedia databases. DYMOND’s architecture and workflow are presented in this paper. Moreover, a case study is used to clarify and evaluate the functionality of our active rule base. Additionally, authors of this paper performed a qualitative evaluation with the aim of comparing and evaluating DYMOND’s functionality. The results showed that DYMOND improved query performance in multimedia databases.

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  1. Abuelyaman, E. S. (2008). An optimized scheme for vertical partitioning of a distributed database. International Journal of Computer Science and Network Security, 8(1), 310–316.

  2. Agrawal, S., Narasayya, V., & Yang, B. (2004). Integrating vertical and horizontal partitioning into automated physical database design. In Proceedings of the 2004 ACM SIGMOD International Conference on Management of Data, ACM (pp. 359–370).

  3. Amossen, R. R. (2010). Vertical partitioning of relational OLTP databases using integer programming. In Proceedings of the 2010 International Conference on Data Engineering Workshops (ICDEW), IEEE (pp. 93–98).

  4. Bhaskar, R., & Sharma, R. (2012). An analysis of vertical splitting algorithms. International Journal of Computer Applications, 52(18), 30–36. doi:10.5120/8304-1767.

  5. Chakravarthy, S., Muthuraj, J., Varadarajan, R., & Navathe, S. B. (1994). An objective function for vertically partitioning relations in distributed databases and its analysis. Distributed and Parallel Databases, 2(2), 183–207. doi:10.1007/BF01267326.

  6. Chaudhuri, S., Konig, A. C., & Narasayya, V. (2004). SQLCM: a continuous monitoring framework for relational database engines. In Proceedings of the 20th International Data Engineering Conference, IEEE (pp. 473–484).

  7. Chavarría-Báez, L., & Li, X. (2006). Structural error verification in active rule based-systems using petri nets. In Proceedings of the fifth mexican international conference on artificial intelligence (MICAI), IEEE (pp. 12–21).

  8. Chavarría-Báez, L., & Li, X. (2009). Termination analysis of active rules -a petri net based approach. In Proceedings of the IEEE international conference on systems Man and Cybernetics, IEEE (pp. 2205–2210).

  9. Chavarría-Báez, L., & Li, X. (2010). ECAPNVer: a software tool to verify active rule bases. In Proceedings of the 22nd international conference on tools with artificial intelligence (ICTAI), IEEE (pp. 138– 141).

  10. Chbeir, R., & Laurent, D. (2009). Towards a novel approach to multimedia data mixed fragmentation. In Proceedings of the int. Conf. on management of emergent digital ecosystems, ACM new york, NY, USA (pp. 200–204).

  11. Chbeir, R., & Laurent, D. (2010). Enhancing multimedia data fragmentation. Journal of Multimedia Processing and Technologies, 1(2), 112–131.

  12. Cheng, C. H., Lee, W. K., & Wong, K. F. (2002). A genetic algorithm based clustering approach for database partitioning. IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, 32(3), 215–230. doi:10.1109/TSMCC.2002.804444.

  13. Curino, C., Jones, E., Zhang, Y., & Madden, S. (2010). Schism: a workload-driven approach to database replication and partitioning. In Proceedings of the VLDB endowment, (Vol. 3 pp. 48–57), DOI doi:10.14778/1920841.1920853.

  14. Dageville, B., Das, D., Dias, K., & et al (2004). Automatic SQL tuning in oracle 10g. In Proceedings of the 30th International Conference on Very Large Data Bases, VLDB Endowment (pp. 1098–1109).

  15. Darmont, J., Fromantin, C., Régnier, S., Gruenwald, L., & Schneider, M. (2001). Dynamic clustering in object-oriented databases: an advocacy for simplicity. In Dittrich K, et al (eds) Objects and databases, LNCS, (Vol. 1994 pp. 71–85).

  16. Eadon, G., Chong, E. I., Shankar, S., Raghavan, A., Srinivasan, J., & Das, S. (2008). Supporting table partitioning by reference in oracle. In Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data ACM (pp. 1111–1122).

  17. Ezeife, C., & Zheng, J. (1999). Measuring the performance of database object horizontal fragmentation schemes. In Proceedings of the International Database Engineering and Applications Symposium, IEEE (pp. 408–414).

  18. Fung, C. -W., Karlapalem, K., & Li, Q. (1997). Cost-driven evaluation of vertical class partitioning in object-oriented databases.

  19. Fung, C. W., Karlapalem, K., & Li, Q. (2002). An evaluation of vertical class partitioning for query processing in object-oriented databases. IEEE Transactions on Knowledge and Data Engineering, 14(5), 1095–1118. doi:10.1109/TKDE.2002.1033777.

  20. Fung, C -W, Karlapalem, K., & Li, Q. (2003). Cost-driven vertical class partitioning for methods in object oriented databases. The VLDB Journal, 12(3), 187–210. doi:10.1007/s00778-002-0084-7.

  21. Fung, C.-W., Leung, W.-C., & Li, Q. (2003). Efficient query execution techniques in a 4Dis video database system for eLearning. Multimedia Tools and Applications, 20 (1), 25–49. doi:10.1023/A:1023418316038.

  22. Gay, J.-Y., & Gruenwald, L. (1997). A clustering technique for object-oriented databases. In Proceedings of the 8th International Conference on Database and Expert Systems Applications, Springer-Verlag (pp. 81–90).

  23. Getahun, F., Tekli, J., Atnafu, S., & Chbeir, R. (2007a). The use of semantic-based predicates implication to improve horizontal multimedia database. In Proceeding of the MS’07 workshop on multimedia information retrieval on the many faces of multimedia semantics, ACM new york, NY, USA (pp. 29–38).

  24. Getahun, F., Tekli, J., Atnafu, S., & Chbeir, R. (2007b). Towards efficient horizontal multimedia database fragmentation using semantic-based predicates implication. In Proceedings of the XXII Simposió Brasileiro de Banco de Dados (SBBD), SBC (pp. 68–82).

  25. Gorla, N., Ng, V., & Law, D. M. (2012). Improving database performance with a mixed fragmentation design. Journal of Intelligent Information System, 39, 559–576. doi:10.1007/s10844-012-0203-x.

  26. Gu, X., Yang, X., Wang, W., Jin, Y., & Meng, D. (2012). CHAC: an effective attribute clustering algorithm for large-scale data processing. In Proceedings of the 2012 IEEE seventh international conference on networking, Architecture, and Storage, IEEE (pp. 94–98).

  27. Guinepain, S., & Gruenwald, L. (2005). Research issues in automatic database clustering. SIGMOD Record, 34(1), 33–38. doi:10.1145/1058150.1058157.

  28. Guinepain, S., & Gruenwald, L. (2006). Automatic database clustering using data mining. In Proceedings of the 17th International Conference on Database and Expert Systems Applications. IEEE Computer Society (pp. 124–128).

  29. Hauglid, J. O., Ryeng, N. H., & Norvag, K. (2010). DYFRAM: Dynamic fragmentation and replica management in distributed database systems. Distributed and Parallel Databases, 28(2-3), 157–185. doi:10.1007/s10619-010-7068-1.

  30. Herodotou, H., Borisov, N., & Babu, S. (2011). Query optimization techniques for partitioned tables. In Proceedings of the 2011 ACM SIGMOD International Conference on Management of Data ACM (pp. 49–60).

  31. Hoffer, J. A., & Severance, D. G. (1975). The use of cluster analysis in physical database design. In Proceedings of the 1st international conference on very large data bases, ACM new york, NY, USA (pp. 69–86).

  32. Holze, M. (2012). Self-management concepts for relational database systems. PhD Thesis Fakultät für Mathematik, Informatik und Naturwissenchaften, Fachbereich Informatik der Universität Hamburg.

  33. Holze, M., & Ritter, N. (2011). Systems models for goal-driven self-management in autonomic databases. Data & Knowledge Engineering, 70(8), 685–701. doi:10.1016/j.datak.2011.03.001.

  34. Huang, Y. -F., & Lai, C. -J. (2016). Integrating frequent pattern clustering branch-and-bound approaches for data partitioning. Information Sciences, 328, 288–301.

  35. Ji, M. (2002). Affinity-based management of main memory database clusters. ACM Transactions on Internet Technology (TOIT), 2(4), 307–339. doi:10.1145/604596.604599.

  36. Jindal, A., & Dittrich, J. (2012). Relax and let the database do the partitioning online. In Castellanos M, Dayal Umeshwar, Lehner W (eds) Enabling real-time business intelligence, lecture notes in business information processing, (Vol. 126 pp. 65–80): springer.

  37. Kwok, Y., Karlapalem, K., Ahmad, I., & Pun, N. M. (1996). Design and evaluation of data allocation algorithms for distributed multimedia database systems. IEEE Journal on Selected Areas and Communications, 14(7), 1332–1348. doi:10.1109/49.536483.

  38. Li, L., & Gruenwald, L. (2013). Self-managing online partitioner for databases (SMOPD): a vertical database partitioning system with a fully automatic online approach. In Proceedings of the 17th International Database Engineering & Applications Sysmposium (IDEAS 13), ACM (pp. 168–173).

  39. Li, L., & Gruenwald, L. (2014). SMOPD-C: An autonomous vertical partitioning technique for distributed databases on cluster computers. In IEEE 15th International Conference on Information Reuse (IRI) (pp. 171–178).

  40. Lin, X., Orlowska, M., & Zhang, Y. (1993). A graph based cluster approach for vertical partitioning in database design. Data & Knowledge Engineering, 11(2), 151–169. doi:10.1016/0169-023X(93)90003-8.

  41. Lu, G. (1999). Multimedia Database Management Systems. Artech House computing library.

  42. Ma, H. (2007). Distribution design for complex value databases. PhD Thesis: Massey University.

  43. Marir, F., Najjar, Y., AlFaress, M., & Abdalla, H. (2007). An enhanced grouping algorithm for vertical partitioning problem in DDBS. In Proceedings of the 22nd International Symposium on Computer and Information Sciences, IEEE (pp. 1–6).

  44. McIver, W. J. J., & King, R. (1994). Self-adaptive, on line reclustering of complex object data. In Proceedings of the 1994 ACM SIGMOD International Conference on Management of Data. ACM New York, NY, USA (pp. 407–418).

  45. Navathe, S., & Ra, M. (1989). Vertical partitioning for database design: a graphical algorithm. In Proceedings of the 1989 ACM SIGMOD International Conference on Management of Data, ACM New York, NY, USA (pp. 440–450).

  46. Navathe, S. B., Ceri, S., Wiederhold, G., & Dou, J. (1984). Vertical partitioning algorithms for database design. ACM Transactions on Database Systems, 9(4), 680–710. doi:10.1145/1994.2209.

  47. Papadomanolakis, S., & Ailamaki, A. (2004). Autopart: automating schema design for large scientific databases using data partitioning. In Proceedings of the 16th International Scientific and Statistical Database Management Conference, IEEE (pp. 383–392).

  48. Paton, N. W., & Díaz, O. (1999). Active Database Systems. ACM Computing Surveys, 31(1), 63–103. doi:10.1145/311531.311623.

  49. Rahimi, H. (2015). Parand F-A. Hierarchical simultaneous vertical fragmentation and allocation using modified Bond Energy Algorithm in distributed databases. Applied Computing and Informatics: Riahi D.

  50. Ramakrishnan, R., & Gehrke, J. (1998). Database management systems. Boston: McGraw-Hill.

  51. Rodríguez, L., & Li, X. (2011a). A vertical partitioning algorithm for distributed multimedia databases. In Hameurlain A others (eds) Database and Expert Systems Applications, LNCS, (Vol. 6861 pp. 544–558).

  52. Rodríguez, L., & Li, X. (2011b). A dynamic vertical partitioning approach for distributed database system. In Proceedings of the 2011 IEEE International Conference on Systems, Man, and Cybernetics (SMC), IEEE (pp. 1853–1858).

  53. Rodríguez, L., & Li, X. (2012). Dynamic vertical partitioning of multimedia databases using active rules. In Liddle S W et al (eds) Database and Expert Systems Applications, LNCS, (Vol. 7447 pp. 191–198): Springer.

  54. Rodríguez, L., Li, X., Cervantes, J., & García-Lamont, F. (2012). DYMOND: an active system for dynamic vertical partitioning of multimedia databases. In Proceedings of the 16th International Database Engineering & Applications Sysmposium, ACM (pp. 71–80).

  55. Rodríguez, L., Li, X., Cuevas-Rasgado, A., & García-lamont, F. (2013). DYVEP: an active database system with vertical partitioning functionality. In Proceedings of the 10th international conference on networking, sensing and control ICNSC, IEEE (pp. 457–462).

  56. Rodríguez-Mazahua, L., Alor-Hernández, G., Abud-Figueroa, M. -A., & Pelëz-Camarena, S. -G. (2014). Horizontal partitioning of multimedia databases using hierarchical agglomerative clustering, MICAI 2014, Part II, LNAI 8857, 296–309.

  57. Saad, S., Tekli, J., Atnafu, S., Chbeir, R., & Yetongnon, K. (2006). Towards multimedia fragmentation. In Manolopoulos Y, Pokorný J, Sellis T K (eds) Advances in databases and information systems, LNCS, (Vol. 4152 pp. 415–429).

  58. Sleit, A., AlMobaideen, W., Al-Areqi, S., & Yahya, A. (2007). A dynamic object fragmentation and replication algorithm in distributed database systems. American Journal of Applied Sciences, 4(8), 613–618. doi:10.3844/ajassp.2007.613.618.

  59. Son, J. H., & Kim, M. H. (2004). An adaptable vertical partitioning method in distributed systems. Journal of Systems and Software, 73(3), 551–561. doi:10.1016/j.jss.2003.04.002.

  60. Song, S. (2015). Design of distributed database systems: an iterative genetic algorithm. Journal of Intelligent Information System, 45, 29–59. doi:10.1007/s10844-013-0269-0.

  61. Van Doorn, M G. L. M., & de Vries, A. P. (2000). The psychology of multimedia databases. In Proceedings of the fifth ACM conference on digital libraries, ACM new york, NY, USA (pp. 1–9).

  62. Verma, V., Bhaskar, R., & Karla, M. (2012). An analysis of vertical splitting algorithms in telecom databases. International Journal of Scientific & Engineering Research, 3(8), 1330–1335.

  63. Weikum, G., Hasse, C., Monkeberg, A., & Zabback, P. (1994). The comfort automatic tuning project. Information Systems, 19(5), 381–432.

  64. Yu, C., & Brandenburg, T. (2011). Multimedia database implications: issues and concerns for classroom teaching. The International Journal of Multimedia and its Applications (IJMA), 3(1).

  65. Zhenjie, L. (2007). Adaptive reorganization of database structures through dynamic vertical partitioning of relational tables. Master Thesis, University of Wollongong.

  66. Zilio, D. C., Rao, J., Lightstone, S., Lohman, G., Storm, A., García-Arellano, C., & Fadden, S. (2004). DB2 design advisor: integrated automatic physical database design. In Proceedings of the 30th International Conference on Very Large Data Bases, VLDB Endowment (pp. 1087–1097).

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The authors are very grateful to National Technological of Mexico for supporting this work. Also, this research paper was sponsored by the National Council of Science and Technology (CONACYT), as well as by the Public Education Secretary (SEP) through PRODEP.

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Correspondence to Giner Alor-Hernández.

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Rodríguez-Mazahua, L., Alor-Hernández, G., Li, X. et al. Active rule base development for dynamic vertical partitioning of multimedia databases. J Intell Inf Syst 48, 421–451 (2017). https://doi.org/10.1007/s10844-016-0420-9

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  • Database design
  • Query optimization
  • Reactive systems
  • Vertical fragments