Implementation of FPGA in Index Data Storage as A Database

  • Authors

    • Ferry Wahyu Wibowo
    • . .
    https://doi.org/10.14419/ijet.v7i4.40.24083

    Received date: December 16, 2018

    Accepted date: December 16, 2018

    Published date: December 16, 2018

  • Database, FPGA, Index, Storage.

  • Abstract

    Nowadays, the database applications have been built on the top of file systems. Although this system is very tough it has a side that should be aware and concerned. If the database doesn’t manage well it will cause some problems i.e. data redundancy and inconsistency, difficult to access data, data isolation, and integrity problems. The data inconsistency could be emerged by duplication of information from one or many different files or multiple file formats. the hardware-based implementation provides benefits in modifying and reconfiguring, meanwhile, the acceleration using this approaching has a good performance than software-based. In major cases, both of them using a bridge to connect each other which is called co-hardware/software.

  • References

    1. Manoj V, Comparative study of NoSQL document, column store databases and evaluation of Cassandra. International Journal of Da-tabase Management Systems (IJDMS), Vol. 6, No. 4, (2014), pp. 11-26, DOI: 10.5121/ijdms.2014.6402.
    2. Dahlan A & Wibowo FW, Transformation of data warehouse using snowflake scheme method. International Journal of Simulation Sys-tems, Science & Technology (IJSSST), Vol. 17, No. 35, (2016), pp. 16.1-16.10, available online: http://ijssst.info/Vol-17/No-35/paper16.pdf, last visit 03.08.2018.
    3. Madhuri DK, A novel approach for processing big data. Interna-tional Journal of Database Management Systems (IJDMS), Vol. 8, No. 5, (2016), pp. 15-24, DOI: 10.5121/ijdms.2016.8502.
    4. Dahlan A & Wibowo FW, “Design of Library Data Warehouse Us-ing SnowFlake Scheme Method: Case Study: Library Database of Campus XYZ”, Proceedings of IEEE 2016 7th International Con-ference on Intelligent Systems, Modelling and Simulation (ISMS), (2016), pp. 318-322, DOI: 10.1109/ISMS.2016.71.
    5. Ailamaki A, “Databases and hardware: the beginning and sequel of a beautiful friendship”, Proceedings of the VLDB Endowment, Vol. 8, No. 12, (2015), pp. 2058-2061.
    6. Wibowo FW, “Interoperability of reconfiguring system on FPGA using a design entry of hardware description language”, Proceed-ings of the 2011 computation and communication technologies: 3rd international conference on advances in computing, control, and tel-ecommunication technologies, ACT 2011 – Computer Science Series 1, (2011), pp. 79-83.
    7. Papaphilippou, Philippos and Wayne Luk. “Accelerating database systems using FPGAs: A survey.” (2018).
    8. Wibowo FW, Implementation of viterbi algorithm based-on field programmable gate array for wireless sensor network. Advanced Science Letters, Vol. 21, Issue 11, (2015), pp. 3521-3525.
    9. Sukhwani B, Min H, Thoennes M, Dube P, Brezzo BIB, Dillen-berger D & Asaad S, “Database analytics acceleration using FPGAs”, Proceedings of the 21st international conference on Paral-lel architectures and compilation techniques, (2012), pp 411-420, http://dx.doi.org/10.1145/2370816.2370874.
    10. Mahajan D, Kim JK, Sacks J, Ardalan A, Kumar A & Esmaeilzadeh H, “In-RDBMS hardware acceleration of advanced analytics”, Pro-ceedings of the VLDB Endowment, Vol. 11, No. 11, (2018), pp. 1317-1331, http://doi.org/10.14778/3236187.3236188.
    11. Wibowo FW, Sudarmawan & Sulistiyono M (2018), Hardware plat-form design analysis of k-means clustering algorithm implementa-tion, International Journal of Engineering & Technology.

  • Downloads

  • How to Cite

    Wahyu Wibowo, F., & ., . (2018). Implementation of FPGA in Index Data Storage as A Database. International Journal of Engineering and Technology, 7(4.40), 94-97. https://doi.org/10.14419/ijet.v7i4.40.24083