Review of The Data Modeling Standards and Data Model Transformation Techniques

• Autorzy: Jaskierny Leszek

Identyfikatory

DOI

10.23743/acs-2018-32

• Języki publikacji: EN

Abstrakty

EN

Manual data transformations that result in high error rates are a big problem in complex integration and data warehouse projects, resulting in poor quality of data and delays in deployment to production. Automation of data trans-formations can be easily verified by humans; the ability to learn from past decisions allows the creation of metadata that can be leveraged in future mappings. Significant improvement of the quality of data transformations can be achieved, when at least one of the models used in transformation is already analyzed and understood. Over recent decades, particular industries have defined data models that are widely adopted in commercial and open source solutions. Those models (often industry standards, accepted by ISO or other organizations) can be leveraged to increase reuse in integration projects resulting in a) lower project costs and b) faster delivery to production. The goal of this article is to provide a comprehensive review of the practical applications of standardization of data formats. Using use cases from the Financial Services Industry as examples, the author tries to identify the motivations and common elements of particular data formats, and how they can be leveraged in order to automate process of data transformations between the models.

Słowa kluczowe

EN

business standards; interoperability; canonical data models; graphs; graph databases; graph transformation

PL

standardy biznesowe; interoperacyjność; modele danych kanonicznych; wykresy; bazy danych wykresów; transformacje grafowe

• Wydawca: Polskie Towarzystwo Promocji Wiedzy ; Lublin University of Technology
• Czasopismo: Applied Computer Science
• Rocznik: 2018
• Tom: Vol. 14, no 4
• Strony: 93--108
• Opis fizyczny: Bibliogr. 13 poz., fig.

Twórcy

autor

Jaskierny Leszek

• AGH University of Science and Technology, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, Department of Computer Science

Bibliografia

• Manual data transformations that result in high error rates are a big problem in complex integration and data warehouse projects, resulting in poor quality of data and delays
• in deployment to production. Automation of data trans-formations can be easily verified by humans; the ability to learn from past decisions allows the creation of metadata that can be leveraged in future mappings. Significant improvement of the quality of data transformations can be achieved, when at least one of the models used in transformation is already analyzed and understood. Over recent decades, particular industries have defined data models that are widely adopted in commercial and open source solutions. Those models (often industry standards, accepted by ISO or other organizations) can be leveraged to increase reuse
• in integration projects resulting in a) lower project costs and b) faster delivery to production. The goal of this article is to provide a comprehensive review of the practical applications of standardization of data formats. Using use cases from the Financial Services Industry
• as examples, the author tries to identify the motivations and common elements of particular data formats, and how they can be leveraged in order to automate process of data transformations between the models.
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• [2] Angles, R., & Gutierrez, C. (2008). Survey of graph database models. New York, USA: ACM.
• [3] Bray, T., Paoli, J., Sperberg-McQueen, C. M., Maler, E., & Yergeau, F. (2008). Extensible Markup Language (XML) 1.0 (Fifth Edition). Retrieved from https://www.w3.org/TR/xml
• [4] Cortet, M. (2014). Access to the Account (XS2A): accelerating the API-economy for banks? Retrieved from https://innopay.com/blog/access-to-the-account-xs2a-accelerating-the-api-economy-for-banks
• [5] Holman, K. (2018). OASIS Universal Business Language (UBL) TC. Retrieved from https://www.oasis-open.org/committees/tc_home.php?wg_abbrev=ubl
• [6] ISO 20022. (2018, August 1). Universal financial industry message scheme. Retrieved from https://www.iso20022.org
• [7] Kotulski, L. (2013). Rozproszone transformacje grafowe. Kraków, Poland: Wydawnictwo AGH.
• [8] McKnight, W. (2014). IBM Industry Data Models in the Enterprise. Retrieved from https://www-01.ibm.com/software/data/industry-models/
• [9] Roman, D. (2006). Canonical Data & Process Models for B2B Integration. Retrieved from http://ceur-ws.org/Vol-170/paper3.pdf
• [10] Skinner, Ch. (2015). How will Banks organise themselves to manage APIs built for PSD2/XS2A? Retrieved from http://thefinanser.com/2015/11/how-will-banks-organise-themselves-to-manage-apis-built-for-psd2-xs2a.html/
• [11] Sleger, G. (2010). Data Transformation Mapping – Can it be Automated? Retrieved from https://www.cleo.com/blog/data-transformation-mapping-can-it-be-automated
• [12] SWIFT. (2018, August 1). Financial messaging services. Retrieved from https://www.swift.com/about-us/discover-swift/messaging-standards
• [13] Thompson, H. & Lilley, C. (2014). XML Media Types, RFC 7303. Retrieved from https://tools.ietf.org/html/rfc7303