Transformasi Ontologi ke Model Dimensional

Penulis

Parmonangan R. Togatorop, Christina Simanjuntak, Christine Nababan, Genii Silitonga

Abstrak

Data warehouse  adalah salah satu komponen penting untuk analisis bisnis yang efektif. Salah satu model pada data warehouse adalah dimensional model yang banyak digunakan karena performa permrosesan yang lebih cepat dari model lain. Dua faktor utama pada perancangan dimensional model adalah adalah sumber data dan kebutuhan bisnis.Salah satu sumber data yang banyak digunakan adalah ontologi karena mampu merepresentasikan data menjadi informasi yang koheren yang dapat dimasukkan ke dalam Data Warehouse. Dalam penelitian ini dihasilkan sebuah tools berbasis ontologi yang digunakan untuk secara otomatis mendapatkan dimensional model untuk data warehouse dari sumber data dan kebutuhan bisnis. Tahapan yang dilakukan mengidentifikasi semua informasi pada file ontology yang dimasukkan pengguna, kemudian daftar dimensi dan fact dihasilkan berdasarkan aturan perancangan dimensional model. Pembuatan fact tabel dan dimensi tabel dirancang berdasarkan rule perancangan dimensional model yang diperkenalkan oleh Kimball. Setelah tabel fact dan dimensidiidentifikasi, maka tabel fact dan dimensi tersebut diubah ke dalam bentuk kueri yang dapat dieksekusi pada MySQL. Penelitian ini berhasil menghasilkan dimensional model dari sumber data ontology.


Abstract

The data warehouse is one of the essential components for effective business analysis. One of the models in the data warehouse is the dimensional model that is widely used because the processing performance is faster than other models. The two main factors in designing a dimensional model are data sources and business requirements. One of the most widely used data sources is ontology because it is able to represent data into coherent information that can be entered into the Data Warehouse. In this research, an ontology-based tool is produced which is used to automatically obtain dimensional models for the data warehouse from data sources and business needs. The step taken identifies all the information in the ontology file entered by the user, then a list of dimensions and facts is generated based on the design rules for the dimensional model. The creation of fact tables and dimension tables are designed based on the dimensional model design rules introduced by Kimball. After the fact and dimension tables are identified, the fact and dimension tables are converted into queries that can be executed in MySQL. This study succeeded in producing dimensional models from the ontology data source.

 


Teks Lengkap:

PDF

Referensi


ALEKSIC, S., CELIKOVIC, M. & LINK, . S., 2010. Faceoff: Surrogate vs. Natural Keys. ADBIS 2010, p. 543–546.

BOJIČIĆ, I. et al., 2016. A Comparative Analysis of Data Warehouse Data Models of Data Warehouse Data Models. 2016 6th International Conference on Computers Communications and Control (ICCCC).

FEKI, J. & HACHAICHI, Y., 2013. An Automatic Method For The Design Of Multidimensional Schemas From Object Oriented Databases. International Journal of Information Technology & Decision Making, Volume 12, pp. 1223-1259.

G, B., POŠCIC, P. & JAKŠIC, D., 2017. Data Warehouse Architecture Classification. MIPRO.

KIMBALL & ROSS, 2013. Kimball Dimensional Modeling Techniques Overview”, “The Data Warehouse Toolkit : The Definitive Guide to Dimensional Modeling”. s.l.:John Wiley & Sons Inc..

KIRMANI, M. M., 2017. Dimensional modeling using Star Schema for Data Creation. Oriental Journal of Computer Science and Technology, Volume 10, pp. 745-754.

LI, J., XIAO, M., SUN, Y. & CHEN, . Y., 2021. Knowledge Modeling of Airborne Missile Management System Based on Multi-dimensional Fuzzy Ontology. Journal of Physics: Conference Series.

MOULAI, H. & DRIAS, H., 2018. From Data Warehouse to Information Warehouse: Application to Social Media. New York, International Conference on Learning and Optimization Algo- rithms: Theory and Applications.

REN, S. & WANG, T., 2018. Dimensional Modeling of Medical Data Warehouse Based on Ontology Shuxia. 2018 IEEE 3rd International Conference on Big Data Analysis, ICBDA 2018.

RUS, A. M. M. & OTHMAN, Z. A., 2019. ONTODB : Aplikasi untuk Transformasi Ontologi OWL ke Basis Data Relasi SQL. Journal of Data Analysis, 1(2).

SAUTOT, L., BIMONTE, S. & JOURNAUX, L., 2015. A semi-automatic design methodology for ( Big ) Data Warehouse transforming facts into dimensions. JOURNAL OF IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING.

SCHOLTZ, I., 2016. Inmon versus Kimball: The agile development of a data warehouse, Potchefstroom: North-West University.

SEBAA, A., CHIKH, F., NOUICER, A. & TARI, A., 2018. Medical Big Data Warehouse: Architecture and System Design, a Case Study: Improving Healthcare Resources Distribution. Transactional Processing Systems.

THENMOZHI, M. A. V. K., 2013. A Tool for Data Warehouse Multidimensional Schema Design using Ontology. International Journal of Computer Science I, p. 161–169.

WAMARS, H. L. H. S. & RANDRIATOAMANANA, R., 2016. Datawarehouser: A Data Warehouse artist who have ability to understand data warehouse schema pictures. 10 Conference (TENCON) -.

YESSAD, L. & LABIOD, A., 2016. Comparative Study of Data Warehouses Modeling Approaches: Inmon, Kimball and Data Vault. International Conference on System Reliability and Science.




DOI: http://dx.doi.org/10.25126/jtiik.2022915725