Eco-friendly Database Space Saving Using Proxy Attributes

• Emran, Nurul ^[1] ; Abdullah, Noraswaliza ^[2] ; Harum , Norharyati ^[2] ; R. Ismail, Amelia ; Nordin, Azlin ; Caballero, Ismael
  1. [1] Universiti Teknikal Malaysia Melaka (UTeM)
  2. [2] Universiti Teknikal Malaysia Melaka
• Localización: Journal of Computer Science and Technology, ISSN-e 1666-6038, Vol. 22, Nº. 1, 2022 (Ejemplar dedicado a: Fifty-Fifth Issue; e07)
• Idioma: inglés
• DOI: 10.24215/16666038.22.e04
• Títulos paralelos:
  • Ahorro sostenible de espacio de almacenamiento de bases de datos usando atributos de proxy
• Enlaces
  • Texto completo
• Resumen
  • español

    El rápido crecimiento de los datos y un almacenamiento ineficaz de los valores de datos son dos cuestiones que están en la agenda de Green Computing. Las decisiones de la tecnología sostenible deben sustentarse en la cantidad de huella de carbono generada durante la computación producida. En este sentido, cuando se trata de almacenar datos, es preciso minimizar el consumo energético empleado en las operaciones de almacenamiento de datos, sobre todo cuando se trata de grandes volúmenes de datos. Este artículo presenta un informe sobre la implementación de atributos proxy para reducir el espacio optimizando el espacio disponible en la base de datos mediante la sustitución de atributos. Para ello, se examina un conjunto de atributos que funcionarán como proxies recuperados de la base de datos pública Comprehensive Microbial Resource (CMR), teniendo en cuenta aspectos de precisión y ahorro de espacio. Los resultados demostraron que realmente esta técnica supone un ahorro considerable de espacio al tiempo que mantiene los niveles de  precisión de los datos.

  • English

    Rapid data growth and inefficient data storage are two concerning issues in green computing. The decision on the eco-friendly technology to use often relies on the amount of carbon footprint produced. Thus, it would be valuable to avoid inefficient electric power utilization by minimizing physical data storages to store large data volumes. This paper reported the implementation of proxy attributes to reduce space by optimizing the available database space through attributes substitution. We examine a set of proxies retrieved from the Comprehensive Microbial Resource (CMR) public database regarding their space-saving and accuracy properties.  The results indicated that the proxies understudy offer space-saving while maintaining accuracy.

• Referencias bibliográficas
  • B. Anthony, M. Abdul Majid, and A. Romli, “A Descriptive Study towards Green Computing Practice Application for Data Centers in IT Based Industries,”...
  • A. Sabban, “Introductory Chapter: Green Computing Technologies and Industry in 2021,” in Green Computing Technologies and Computing Industry...
  • R. R. Schmidt, E. E. Cruz, and M. K. Iyengar, “Challenges of data center thermal management,” IBM J. Res. Dev., vol. 49, no. 4–5, pp. 709–723,...
  • N. A. Ali and M. Abu-Elkheir, “Data management for the Internet of Things: Green directions,” 2012 IEEE Globecom Work. GC Wkshps 2012, pp....
  • J. Yuventi and R. Mehdizadeh, “A critical analysis of Power Usage Effectiveness and its use in communicating data center energy consumption,”...
  • D. Mukherjee, S. Roy, R. Bose, and D. Ghosh, “A Practical Approach to Measure Data Centre Efficiency Usage Effectiveness,” in Lecture Notes...
  • R. Rahmani, I. Moser, and A. L. Cricenti, “Modelling and optimisation of microgrid configuration for green data centres: A metaheuristic approach,”...
  • M. Shirer and J. Rydning, “IDC’s Global DataSphere Forecast Shows Continued Steady Growth in the Creation and Consumption of Data,” International...
  • J. Bughin, “Big data, Big bang?,” J. Big Data, vol. 3, no. 1, p. 2, Dec. 2016.
  • J. F. Molina-Azorín, E. Claver-Cortés, M. D. López-Gamero, and J. J. Tarí, “Green management and financial performance: A literature review,”...
  • EPA Energy Star, “Top 12 Ways to Decrease the Energy Consumption of Your Data Centre,” 2021. https://www.energystar.gov/buildings/tools-and-resources/top-12-ways-decrease-energy-consumption-your-data-center...
  • S. Greenberg and M. Herrlin, “Small Data Centers, Big Energy Savings: An Introduction for Owners and Operators FINAL REPORT,” 2017.
  • E. Ayanoglu, “Energy Efficiency in Data Centers | IEEE Communications Society,” IEEE ComSoc Technical Committees Newsletter, 2019. https://www.comsoc.org/publications/tcn/2019-nov/energy-efficiency-data-centers...
  • Oracle Corporation, “Oracle Advanced Compression Proof-of-Concept (POC) Insights and Best Practices,” 2018. http://www.oracle.com/technetwork/databas...
  • S. Alen, “Comparison on DB2 10.1 Vs SQL Server 2012 Vs Oracle 11g R2 latest features to suite SAP Products,” 2013. http://scn.sap.com/docs/DOC-45542...
  • S. Aghav, “Database compression techniques for performance optimization,” in ICCET 2010 - 2010 International Conference on Computer Engineering...
  • T. Kim, N. S. Artan, J. Viventi, and H. J. Chao, “Spatiotemporal compression for efficient storage and transmission of high-resolution electrocorticography...
  • S. Nosratian, M. Moradkhani, and M. B. Tavakoli, “Hybrid data compression using fuzzy logic and huffman coding in secure iot,” Iran. J. Fuzzy...
  • Y. Wang, M. Miao, J. Wang, and X. Zhang, “Secure deduplication with efficient user revocation in cloud storage,” Comput. Stand. Interfaces,...
  • W. Tian, R. Li, C. Z. Xu, and Z. Xu, “Sed-Dedup: An efficient secure deduplication system with data modifications,” Concurr. Comput. Pract....
  • W. Lup Low, M. Li Lee, and T. Wang Ling, “A knowledge-based approach for duplicate elimination in data cleaning,” Inf. Syst., vol. 26, no....
  • S. M. Randall, A. M. Ferrante, J. H. Boyd, and J. B. Semmens, “The effect of data cleaning on record linkage quality,” BMC Med. Informatics...
  • A. Ali, N. A. Emran, and S. A. Asmai, “Missing Values Compensation in Duplicates Detection Using Hot Deck,” J. Big Data, vol. 8, no.112, pp....
  • A. K. Elmagarmid, P. G. Ipeirotis, and V. S. Verykios, “Duplicate record detection: a survey,” {IEEE} Trans. Knowl. Data Eng., vol. 19, no....
  • G. Beskales, M. A. Soliman, I. F. Ilyas, and S. Ben-David, “Modeling and Querying Possible Repairs in Duplicate Detection,” Publ. Very Large...
  • N. A. Emran, N. Abdullah, and M. N. M. Isa, “Storage space optimisation for green data center,” in Procedia Engineering, 2013, vol. 53, pp....
  • V. M. Markowitz, “Microbial genome data resources,” Current Opinion in Biotechnology, vol. 18, no. 3. pp. 267–272, 2007.
  • Y. Huhtala, J. Kärkkäinen, P. Porkka, and H. Toivonen, “TANE: An Efficient Algorithm for Discovering Functional and Approximate Dependencies,”...
  • M. Buranosky, E. Stellnberger, E. Pfaff, D. Diaz-Sanchez, and C. Ward-Caviness, “FDTool: a Python application to mine for functional dependencies...
  • H. Yao, H. J. Hamilton, and C. J. Butz, “FD mine: Discovering functional dependencies in a database using equivalences,” in Proceedings -...
  • Z. Abedjan, P. Schulze, and F. Naumann, “DFD: Efficient functional dependency discovery,” in CIKM 2014 - Proceedings of the 2014 ACM International...
  • T. Papenbrock et al., “Functional dependency discovery: An experimental evaluation of seven algorithms,” in Proceedings of the VLDB Endowment,...