Entrepôts, Représentation et Ingénierie des Connaissances
Séminaires d'ERIC
Pour toute question concernant les séminaires, vous pouvez contacter Nadia Kabachi (Nadia.Kabachi@univ-lyon1.fr) et Jairo Cugliari (Jairo.Cugliari@univ-lyon2.fr)

Recherche approfondie

par Année
par Type
par Thème

(1) Séminaire(s)


Lun. 23/03/2015 09:30 K71, Bâtiment K, RdC

Integration and Analysis of Data from Heterogeneous Web Services


The role of data warehouse for business analytics cannot be undermined for any enterprise, irrespective of its size. But the growing dependence on web services has resulted in a situation where the enterprise data is managed by multiple autonomous and heterogeneous service providers. We present our approach and its associated prototype DaWeS, a DAta warehouse fed with data coming from WEb Services to extract, transform and store enterprise data from web services and to build performance indicators from them (stored enterprise data) hiding from the end users the heterogeneity of the numerous underlying web services. Its ETL process is grounded on a mediation approach usually used in data integration. This enables DaWeS (i) to be fully configurable in a declarative manner only (XML, XSLT, SQL, datalog) and (ii) to make part of the warehouse schema dynamic so it can be easily updated. (i) and (ii) allow DaWeS managers to shift from development to administration when they want to connect to new web services or to update the APIs (Application programming interfaces) of already connected ones. The aim is to make DaWeS scalable and adaptable to smoothly face the ever-changing and growing web services offer. It should be noted that this also enables DaWeS to be used with the vast majority of actual web service interfaces defined with basic tech- nologies only (HTTP, REST, XML and JSON) and not with more advanced standards (WSDL, WADL, hRESTS or SAWSDL) since these more advanced standards are not widely used yet to describe real web services. DaWeS also enables the easy design (as SQL Queries) of personalized performance indicators. Finally a visual approach to perform multidimensional analysis is explored, considering the situations where multidisciplinary research teams are involved.

Pour plus d'informations, merci de contacter Bentayeb F.