Eco-friendly Database Space Saving Using Proxy Attributes

Authors

DOI:

https://doi.org/10.24215/16666038.22.e04

Keywords:

eco-friendly, proxy, green computing, green data center, space-saving

Abstract

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.

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References

B. Anthony, M. Abdul Majid, and A. Romli, “A Descriptive Study towards Green Computing Practice Application for Data Centers in IT Based Industries,” MATEC Web Conf., vol. 150, pp. 1–8, 2018.

A. Sabban, “Introductory Chapter: Green Computing Technologies and Industry in 2021,” in Green Computing Technologies and Computing Industry in 2021, 2021, pp. 1–16.

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, 2005.

N. A. Ali and M. Abu-Elkheir, “Data management for the Internet of Things: Green directions,” 2012 IEEE Globecom Work. GC Wkshps 2012, pp. 386–390, 2012.

J. Yuventi and R. Mehdizadeh, “A critical analysis of Power Usage Effectiveness and its use in communicating data center energy consumption,” Energy Build., vol. 64, pp. 90–94, Sep. 2013.

D. Mukherjee, S. Roy, R. Bose, and D. Ghosh, “A Practical Approach to Measure Data Centre Efficiency Usage Effectiveness,” in Lecture Notes on Data Engineering and Communications Technologies, 2022, pp. 113–122.

R. Rahmani, I. Moser, and A. L. Cricenti, “Modelling and optimisation of microgrid configuration for green data centres: A metaheuristic approach,” Futur. Gener. Comput. Syst., vol. 108, pp. 742–750, 2020.

M. Shirer and J. Rydning, “IDC’s Global DataSphere Forecast Shows Continued Steady Growth in the Creation and Consumption of Data,” International Data Corporation (IDC), 2020. https://www.idc.com/getdoc.jsp?containerId=prUS46286020 (accessed Aug. 13, 2021).

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,” Manag. Decis., vol. 47, no. 7, pp. 1080–1100, 2009.

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 (accessed Aug. 13, 2021).

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 (accessed Aug. 13, 2021).

Oracle Corporation, “Oracle Advanced Compression Proof-of-Concept (POC) Insights and Best Practices,” 2018. http://www.oracle.com/technetwork/databas (accessed Aug. 14, 2021).

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 (accessed Aug. 14, 2021).

S. Aghav, “Database compression techniques for performance optimization,” in ICCET 2010 - 2010 International Conference on Computer Engineering and Technology, Proceedings, 2010, vol. 6.

T. Kim, N. S. Artan, J. Viventi, and H. J. Chao, “Spatiotemporal compression for efficient storage and transmission of high-resolution electrocorticography data,” in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2012.

S. Nosratian, M. Moradkhani, and M. B. Tavakoli, “Hybrid data compression using fuzzy logic and huffman coding in secure iot,” Iran. J. Fuzzy Syst., vol. 18, no. 1, pp. 101–116, 2021.

Y. Wang, M. Miao, J. Wang, and X. Zhang, “Secure deduplication with efficient user revocation in cloud storage,” Comput. Stand. Interfaces, vol. 78, 2021.

W. Tian, R. Li, C. Z. Xu, and Z. Xu, “Sed-Dedup: An efficient secure deduplication system with data modifications,” Concurr. Comput. Pract. Exp., vol. 33, no. 15, 2021.

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. 8, pp. 585–606, 2001.

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 Decis. Mak. 2013 131, vol. 13, no. 1, pp. 1–10, Jun. 2013.

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. 1–19, 2021.

A. K. Elmagarmid, P. G. Ipeirotis, and V. S. Verykios, “Duplicate record detection: a survey,” {IEEE} Trans. Knowl. Data Eng., vol. 19, no. 1, pp. 1–16, 2007.

G. Beskales, M. A. Soliman, I. F. Ilyas, and S. Ben-David, “Modeling and Querying Possible Repairs in Duplicate Detection,” Publ. Very Large Database Endow., vol. 2, no. 1, pp. 598–609, 2009.

N. A. Emran, N. Abdullah, and M. N. M. Isa, “Storage space optimisation for green data center,” in Procedia Engineering, 2013, vol. 53, pp. 483–490.

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,” Comput. J., vol. 42, no. 2, pp. 100–111, 1999.

M. Buranosky, E. Stellnberger, E. Pfaff, D. Diaz-Sanchez, and C. Ward-Caviness, “FDTool: a Python application to mine for functional dependencies and candidate keys in tabular data,” F1000Research, 2019.

H. Yao, H. J. Hamilton, and C. J. Butz, “FD mine: Discovering functional dependencies in a database using equivalences,” in Proceedings - IEEE International Conference on Data Mining, ICDM, 2002, pp. 729–732.

Z. Abedjan, P. Schulze, and F. Naumann, “DFD: Efficient functional dependency discovery,” in CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management, 2014, pp. 949–958.

T. Papenbrock et al., “Functional dependency discovery: An experimental evaluation of seven algorithms,” in Proceedings of the VLDB Endowment, vol. 8, no. 10, 2015, pp. 1082–1093.

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Published

2022-04-21

How to Cite

Emran, N., Abdullah, N. ., Harum , N. ., R. Ismail, A. ., Nordin, A. ., & Caballero, I. . (2022). Eco-friendly Database Space Saving Using Proxy Attributes. Journal of Computer Science and Technology, 22(1), e04. https://doi.org/10.24215/16666038.22.e04

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Original Articles