Development and Optimization of NoSQL Database in Food Insecurity Early Warning System Based on Local Community Participation
Abstract views: 87

Development and Optimization of NoSQL Database in Food Insecurity Early Warning System Based on Local Community Participation

Yani Nurhadryani, Wiradani Ramadhan, Auzi Asfarian

Abstract

As a part of the food insecurity early warning system based on local participation, a robust and scalable database service is required. This necessity caused by the large area of services which include 34 provinces, 416 districts, 7,215 sub-districts and 80,534 villages in Indonesia. The abundant number of the expected daily transaction might not be handled properly using the traditional model. In this research, we design, implement, and optimize the NoSQL database to create scalable, dynamic, and flexible database service for the early warning system. The cohesion of the model is then measured, resulting in 5 entities with high cohesion, 16 with moderate cohesion, and 3 with low cohesion. After refactoring, we reduced the number of the low-cohesion entity into one and increased the average cohesion from 0.62 to 0.67. An empirical experiment was conducted to compare the response time before and after the refactoring. As the results, the average response time is decreased from 11.0 ms to 7.99 ms or equal to 1.38 in speedup.The resulting database is then used as a part of database services in our early warning system.

Keywords

Database Design, Database Optimization, NoSQL, Early Warning System, Information System

References

[1] A. P. Panatagama, “Analysis and Design of Patriot Pangan : Towards Electronic Participation and Initiative Platform to Help Reduce Food Insecurity in Indonesia,” in Proceedings of IEEE Region 10 Humanitarian Technology Conference (R10-HTC) 2019, 2019.

[2] APJII, “Penetrasi & Profil Perilaku Pengguna Internet Indonesia Tahun 2018,” Apjii, p. 51, 2019.

[3] N. Ameya, P. Anil, and P. Dikshay, “Type of NOSQL databases and its comparison with relational databases.,” Int. J. Appl. Inf. Syst., vol. 5, no. 4, pp. 16–19, 2013.

[4] N. Leavitt, “Will NoSQL databases live up to their promise?,” Computer (Long. Beach. Calif)., vol. 43, no. 2, pp. 12–14, 2010.

[5] R. Cattell, “Scalable SQL and NoSQL data stores,” SIGMOD Rec., vol. 39, no. 4, pp. 12–27, 2010, doi: 10.1145/1978915.1978919.

[6] J. Han, E. Haihong, G. Le, and J. Du, “Survey on NoSQL database,” Proc. - 2011 6th Int. Conf. Pervasive Comput. Appl. ICPCA 2011, pp. 363–366, 2011, doi: 10.1109/ICPCA.2011.6106531.

[7] Y. Li and S. Manoharan, “A performance comparison of SQL and NoSQL databases,” IEEE Pacific RIM Conf. Commun. Comput. Signal Process. - Proc., no. August 2013, pp. 15–19, 2013, doi: 10.1109/PACRIM.2013.6625441.

[8] C. Győrödi, R. Gyorodi, G. Pecherle, and A. Olah, “A Comparative Study: MongoDB vs. MySQL,” no. June, 2015, doi: 10.13140/RG.2.1.1226.7685.

[9] A. Boicea, F. Radulescu, and L. I. Agapin, “MongoDB vs Oracle - Database comparison,” Proc. - 3rd Int. Conf. Emerg. Intell. Data Web Technol. EIDWT 2012, no. September 2012, pp. 330–335, 2012, doi: 10.1109/EIDWT.2012.32.

[10] Z. Parker, S. Poe, and S. V Vrbsky, “Comparing NoSQL MongoDB to an SQL DB,” 2013.

[11] K. Chodorow, MongoDB: The Definitive Guide., Third Edit. Sebastopol (CA): O’Reilly, 2019.

[12] M. M. Patil, A. Hanni, C. H. Tejeshwar, and P. Patil, “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 Proceedings of the International Conference on IoT in Social, Mobile, Analytics and Cloud, I-SMAC 2017, 2017, pp. 325–330, doi: 10.1109/I-SMAC.2017.8058365.

[13] S. Kanoje, V. Powar, and D. Mukhopadhyay, “Using MongoDB for social networking website deciphering the pros and cons,” in ICIIECS 2015 - 2015 IEEE International Conference on Innovations in Information, Embedded and Communication Systems, 2015, doi: 10.1109/ICIIECS.2015.7192924.

[14] Z. Wei-Ping, L. Ming-Xin, and C. Huan, “Using MongoDB to implement textbook management system instead of MySQL,” in 2011 IEEE 3rd International Conference on Communication Software and Networks, ICCSN 2011, 2011, pp. 303–305, doi: 10.1109/ICCSN.2011.6013720.

[15] Y. Widyani, H. Laksmiwati, and E. D. Bangun, “Mapping spatio-temporal disaster data into MongoDB,” in Proceedings of 2016 International Conference on Data and Software Engineering, ICoDSE 2016, 2017, doi: 10.1109/ICODSE.2016.7936157.

[16] N. Yilmaz, O. Alatli, B. Ciloglugil, and R. C. Erdur, “Evaluation of storage and query performance of sensor based Internet of Things data with MongoDB,” in 2018 International Conference on Artificial Intelligence and Data Processing, IDAP 2018, 2019, doi: 10.1109/IDAP.2018.8620837.

[17] M. G. Jung, S. A. Youn, J. Bae, and Y. L. Choi, “A study on data input and output performance comparison of MongoDB and PostgreSQL in the big data environment,” in Proceedings - 8th International Conference on Database Theory and Application, DTA 2015, 2016, pp. 14–17, doi: 10.1109/DTA.2015.14.

[18] S. Hoberman, Data Modeling for MongoDB: Building Well-Designed and Supportable MongoDB Database, First Edit. New Jersey (US): Technics Publication, 2014.

[19] Wilson da Rocha França, MongoDB Data Modeling: Focus on Data Usage and Better Design Schemas with the Help of MongoDB, First Edit. Birmingham (UK): Packt Publishing, 2015.

[20] M. Paixao, M. Harman, Y. Zhang, and Y. Yu, “An Empirical Study of Cohesion and Coupling : Balancing Optimisation and Disruption,” 2017, no. c, pp. 1–21, doi: 10.1109/TEVC.2017.2691281.

[21] J. Al Dallal and L. C. Briand, “An object-oriented high-level design-based class cohesion metric,” Inf. Softw. Technol., vol. 52, no. 1, pp. 1346–1361, 2010, doi: 10.1016/j.infsof.2010.08.006.

[22] A. Silberschatz, H. F. Korth, and S. S, Database System Concepts, Sixth Edit. New York (US): McGraw-Hill Education, 2010.

[23] J. Al Dallal and L. C. Briand, “A precise method-method interaction-based cohesion metric for object-oriented classes,” ACM Transactions on Software Engineering and Methodology, vol. 21, no. 2. pp. 1–34, 01-Mar-2012, doi: 10.1145/2089116.2089118.

[24] A. Kumar and S. Kaur Khalsa, “DETERMINE COHESION AND COUPLING FOR CLASS DIAGRAM THROUGH SLICING TECHNIQUES,” IJACE, vol. 4, no. 1, pp. 19–24, 2012.

[25] I. Baig, “Measuring Cohesion and Coupling of Object-Oriented Systems-Derivation and Mutual Study of Cohesion and Coupling,” Blekinge Institute of Technology, Karlskrona, 2004.

[26] mongodb.com. Last access: 02 Feb 2020.

[27] db-engines.com. Last access: 02 Feb 2020.

Refbacks

  • There are currently no refbacks.

Indexed by: 

    

Referencing Software:

Checked by:

Supervised by:

Statistic:

View My Stats


Creative Commons License Kinetik : Game Technology, Information System, Computer Network, Computing, Electronics, and Control by http://kinetik.umm.ac.id is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.