Data as a Service (DaaS) is among the latest kind of services being investigated in the Cloud computing community. The main aim of DaaS is to overcome limitations of state-of-the-art approaches in data technologies, according to which data is stored and accessed from repositories whose location is known and is relevant for sharing and processing. Besides limitations for the data sharing, current approaches also do not achieve to fully separate/decouple software services from data and thus impose limitations in inter-operability. In this paper we propose a DaaS approach for intelligent sharing and processing of large data collections with the aim of abstracting the data location (by making it relevant to the needs of sharing and accessing) and to fully decouple the data and its processing. The aim of our approach is to build a Cloud computing platform, offering DaaS to support large communities of users that need to share, access, and process the data for collectively building knowledge from data. We exemplify the approach from large data collections from health and biology domains. © 2013 IEEE.

Data as a Service (DaaS) for sharing and processing of large data collections in the cloud / Terzo, O.; Ruiu, P.; Bucci, E.; Xhafa, F.. - (2013), pp. 475-480. ((Intervento presentato al convegno 2013 7th International Conference on Complex, Intelligent, and Software Intensive Systems, CISIS 2013 tenutosi a Taichung, twn nel 2013 [10.1109/CISIS.2013.87].

Data as a Service (DaaS) for sharing and processing of large data collections in the cloud

Ruiu P.;
2013

Abstract

Data as a Service (DaaS) is among the latest kind of services being investigated in the Cloud computing community. The main aim of DaaS is to overcome limitations of state-of-the-art approaches in data technologies, according to which data is stored and accessed from repositories whose location is known and is relevant for sharing and processing. Besides limitations for the data sharing, current approaches also do not achieve to fully separate/decouple software services from data and thus impose limitations in inter-operability. In this paper we propose a DaaS approach for intelligent sharing and processing of large data collections with the aim of abstracting the data location (by making it relevant to the needs of sharing and accessing) and to fully decouple the data and its processing. The aim of our approach is to build a Cloud computing platform, offering DaaS to support large communities of users that need to share, access, and process the data for collectively building knowledge from data. We exemplify the approach from large data collections from health and biology domains. © 2013 IEEE.
978-0-7695-4992-7
978-0-7695-4992-7
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2897019