Skip to content

Governance issues on heavy models in an industrial context

SWIFT is a member-owned cooperative providing secure messaging capabilities to the financial services industry. One critical mission of SWIFT is the standardization of the message flows between the industry players. The model-driven approach naturally came as a solution to the management of these message definitions. However, one of the most important challenges that SWIFT has been facing is the global governance of the message repository and the management of each element. Nowadays modeling tools exist but none of them enables the management of the complete life-cycle of the message models. In this paper wepresent the challenges that SWIFT had to face in the development of a dedicated platform.

 

Sabri Skhiri, Marc Delbaere, Yves Bontemps, Grégoire de Hemptinne, and Nam-Luc Tran, Governance issues on heavy models in an industrial context. Advances in Conceptual Modeling. Recent Developments and New Directions ER 2011, Brussels, Belgium, November 2011.

Click here to access the paper.

Releated Posts

Calibrate to Interpret

Trustworthy machine learning is driving a large number of the ML community works in order to improve ML acceptance and adoption. In this paper, we show a first link between uncertainty and explainability, by studying the relation between calibration and interpretation.
Read More

Mass Estimation of Planck Galaxy Clusters using Deep Learning

Galaxy cluster masses can be inferred indirectly using measurements from X-ray band, Sunyaev-Zeldovich (SZ) effect signal or optical observations. Unfortunately, all of them are affected by some bias. Alternatively, we provide an independent estimation of the cluster masses from the Planck PSZ2 catalogue of galaxy clusters using a machine-learning method.
Read More