Belmehdi, Chahrazed B. BachirChahrazed B. BachirBelmehdiKhiat, AbderrahmaneAbderrahmaneKhiatKeskes, NabilNabilKeskes2022-12-212022-12-212022-07-26https://publica.fraunhofer.de/handle/publica/43030010.1007/978-3-031-12670-3_18OPTIMA is a framework that enables querying the original data on-the-fly without any materialization. It implements two different virtual data models, GRAPH and TABULAR, to join and aggregate data. OPTIMA leverages ontology-based data access and calls the deep learning method to predict the optimal virtual data model using the features extracted from SPARQL queries. Extensive experiments show a reduction in query execution time of over 40% for the TABULAR model selection, and over 30% for the GRAPH model selection.enData VirtualizationOBDABig DataDeep LearningDDC::000 Informatik, Informationswissenschaft, allgemeine Werkeconference paper