Gas Store Data Analysis Using ERD Method and Constitutional Data Warehouse Model
Article Sidebar
-
Data Warehouse, ERD, Konseptual Model
Abstract
A data warehouse is a data storage system that plays a crucial role in business analysis. It collects, integrates, and stores data from multiple sources in a structured format, providing holistic insight into organizational performance. Entity-Relationship Model (ERD) is a visual tool for designing database structures. It uses entities to represent real-world objects and the relationships between them. ERD helps plan an efficient and coherent database design. A conceptual model is an abstract visual representation of information structures and relationships within a domain. It covers key concepts and business rules, assisting in building a solid foundation of understanding before technical designing begins. All three are interrelated in the development of successful information systems. Data warehouses use conceptual models to direct effective data storage design, while ERD helps describe the entities and relationships to be stored in the data warehouse. The combination of all three enables organizations to design, develop, and maintain adequate information systems, based on a deep understanding of data and its relationships. This results in better decision making, more efficient innovation, and optimal utilization of resources. The purpose of this study is to produce optimal data using the ERD method. The main objective is to explain how much data in an information system in the Company and how data management is crucial for effective decision making.
Full text article
References
Al-Fraihat, D., Joy, M., Masa’deh, R., & Sinclair, J. (2020). Evaluating E-learning systems success: An empirical study. Computers in Human Behavior, 102, 67–86. https://doi.org/10.1016/j.chb.2019.08.004
Balia, R., Barra, S., Carta, S., Fenu, G., Podda, A. S., & Sansoni, N. (2021). A Deep Learning Solution for Integrated Traffic Control Through Automatic License Plate Recognition. Dalam O. Gervasi, B. Murgante, S. Misra, C. Garau, I. Ble?i?, D. Taniar, B. O. Apduhan, A. M. A. C. Rocha, E. Tarantino, & C. M. Torre (Ed.), Computational Science and Its Applications – ICCSA 2021 (Vol. 12951, hlm. 211–226). Springer International Publishing. https://doi.org/10.1007/978-3-030-86970-0_16
Bao, Y., Tang, Z., Li, H., & Zhang, Y. (2019). Computer vision and deep learning–based data anomaly detection method for structural health monitoring. Structural Health Monitoring, 18(2), 401–421. https://doi.org/10.1177/1475921718757405
Boje, C., Guerriero, A., Kubicki, S., & Rezgui, Y. (2020). Towards a semantic Construction Digital Twin: Directions for future research. Automation in Construction, 114, 103179. https://doi.org/10.1016/j.autcon.2020.103179
Chen, H., Yamaguchi, S., Morita, Y., Nakao, H., Zhai, X., Shimizu, Y., Mitsunuma, H., & Kanai, M. (2021). Data-driven catalyst optimization for stereodivergent asymmetric synthesis by iridium/boron hybrid catalysis. Cell Reports Physical Science, 2(12), 100679. https://doi.org/10.1016/j.xcrp.2021.100679
Fitri, V. A., Andreswari, R., & Hasibuan, M. A. (2019). Sentiment Analysis of Social Media Twitter with Case of Anti-LGBT Campaign in Indonesia using Naïve Bayes, Decision Tree, and Random Forest Algorithm. Procedia Computer Science, 161, 765–772. https://doi.org/10.1016/j.procs.2019.11.181
Gibb, R., Browning, E., Glover?Kapfer, P., & Jones, K. E. (2019). Emerging opportunities and challenges for passive acoustics in ecological assessment and monitoring. Methods in Ecology and Evolution, 10(2), 169–185. https://doi.org/10.1111/2041-210X.13101
Giudicessi, J. R., Noseworthy, P. A., Friedman, P. A., & Ackerman, M. J. (2020). Urgent Guidance for Navigating and Circumventing the QTc-Prolonging and Torsadogenic Potential of Possible Pharmacotherapies for Coronavirus Disease 19 (COVID-19). Mayo Clinic Proceedings, 95(6), 1213–1221. https://doi.org/10.1016/j.mayocp.2020.03.024
Guerrini-Rousseau, L., Varlet, P., Colas, C., Andreiuolo, F., Bourdeaut, F., Dahan, K., Devalck, C., Faure-Conter, C., Genuardi, M., Goldberg, Y., Kuhlen, M., Moalla, S., Opocher, E., Perez-Alonso, V., Sehested, A., Slavc, I., Unger, S., Wimmer, K., Grill, J., & Brugières, L. (2019). Constitutional mismatch repair deficiency–associated brain tumors: Report from the European C4CMMRD consortium. Neuro-Oncology Advances, 1(1), vdz033. https://doi.org/10.1093/noajnl/vdz033
Jarvis, K. B., LeBlanc, M., Tulstrup, M., Nielsen, R. L., Albertsen, B. K., Gupta, R., Huttunen, P., Jónsson, Ó. G., Rank, C. U., Ranta, S., Ruud, E., Saks, K., Trakymiene, S. S., Tuckuviene, R., & Schmiegelow, K. (2019). Candidate single nucleotide polymorphisms and thromboembolism in acute lymphoblastic leukemia – A NOPHO ALL2008 study. Thrombosis Research, 184, 92–98. https://doi.org/10.1016/j.thromres.2019.11.002
Khawaja, W., Guvenc, I., Matolak, D. W., Fiebig, U.-C., & Schneckenburger, N. (2019). A Survey of Air-to-Ground Propagation Channel Modeling for Unmanned Aerial Vehicles. IEEE Communications Surveys & Tutorials, 21(3), 2361–2391. https://doi.org/10.1109/COMST.2019.2915069
Lei, Y., Yang, B., Jiang, X., Jia, F., Li, N., & Nandi, A. K. (2020). Applications of machine learning to machine fault diagnosis: A review and roadmap. Mechanical Systems and Signal Processing, 138, 106587. https://doi.org/10.1016/j.ymssp.2019.106587
Lu, B., Dao, P., Liu, J., He, Y., & Shang, J. (2020). Recent Advances of Hyperspectral Imaging Technology and Applications in Agriculture. Remote Sensing, 12(16), 2659. https://doi.org/10.3390/rs12162659
Lyu, M., Wang, H., Li, G., Zheng, S., & Situ, G. (2019). Learning-based lensless imaging through optically thick scattering media. Advanced Photonics, 1(03), 1. https://doi.org/10.1117/1.AP.1.3.036002
Messina, G., Praticò, S., Badagliacca, G., Di Fazio, S., Monti, M., & Modica, G. (2021). Monitoring Onion Crop “Cipolla Rossa di Tropea Calabria IGP” Growth and Yield Response to Varying Nitrogen Fertilizer Application Rates Using UAV Imagery. Drones, 5(3), 61. https://doi.org/10.3390/drones5030061
Naparstek, O., & Cohen, K. (2019). Deep Multi-User Reinforcement Learning for Distributed Dynamic Spectrum Access. IEEE Transactions on Wireless Communications, 18(1), 310–323. https://doi.org/10.1109/TWC.2018.2879433
Nurazizah, N., Halimatusadiah, I., Tabroni, I., Nitin, M., & Bradford, S. (2023). History of Islamic Civilization in Post-Independence Indonesia. International Journal of Educational Narratives, 1(5), 231–239. https://doi.org/10.55849/ijen.v1i5.339
Qi, X., Chen, G., Li, Y., Cheng, X., & Li, C. (2019). Applying Neural-Network-Based Machine Learning to Additive Manufacturing: Current Applications, Challenges, and Future Perspectives. Engineering, 5(4), 721–729. https://doi.org/10.1016/j.eng.2019.04.012
Roy, Y., Banville, H., Albuquerque, I., Gramfort, A., Falk, T. H., & Faubert, J. (2019). Deep learning-based electroencephalography analysis: A systematic review. Journal of Neural Engineering, 16(5), 051001. https://doi.org/10.1088/1741-2552/ab260c
Verdoliva, L. (2020). Media Forensics and DeepFakes: An Overview. IEEE Journal of Selected Topics in Signal Processing, 14(5), 910–932. https://doi.org/10.1109/JSTSP.2020.3002101
Wang, M., & Deng, W. (2021). Deep face recognition: A survey. Neurocomputing, 429, 215–244. https://doi.org/10.1016/j.neucom.2020.10.081
Wang, S., Chen, H., & Sun, B. (2020). Recent progress in food flavor analysis using gas chromatography–ion mobility spectrometry (GC–IMS). Food Chemistry, 315, 126158. https://doi.org/10.1016/j.foodchem.2019.126158
Zamora-Izquierdo, M. A., Santa, J., Martínez, J. A., Martínez, V., & Skarmeta, A. F. (2019). Smart farming IoT platform based on edge and cloud computing. Biosystems Engineering, 177, 4–17. https://doi.org/10.1016/j.biosystemseng.2018.10.014
Zhang, S., Zhang, S., Wang, B., & Habetler, T. G. (2020). Deep Learning Algorithms for Bearing Fault Diagnostics—A Comprehensive Review. IEEE Access, 8, 29857–29881. https://doi.org/10.1109/ACCESS.2020.2972859
Authors
Copyright (c) 2023 Fahmi Risaldi, Vany Terisia, Shevty Arbekti Arman, Diana Yusuf
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.