In this paper we focus on big data compression paradigms within reference data-intensive IoT frameworks, which are currently recognized as one of the emerging scientific in a rich interdisciplinary field that comprises service-oriented infrastructures, Cloud computing, big data management and analytics. Basically, big data compression techniques allow to tame the complexity of big data management tasks within such frameworks, hence beneficially influencing all the other activities, perhaps delivered as services in a reference Cloud architecture. Inspired by these considerations, in this paper we provide an overview on noticeable state-of-the-art big data compression techniques, and depict future research directions on the investigated scientific topic to be considered during future years.
Big data compression paradigms for supporting efficient and scalable data-intensive IoT frameworks
CUZZOCREA, Alfredo Massimiliano
2016-01-01
Abstract
In this paper we focus on big data compression paradigms within reference data-intensive IoT frameworks, which are currently recognized as one of the emerging scientific in a rich interdisciplinary field that comprises service-oriented infrastructures, Cloud computing, big data management and analytics. Basically, big data compression techniques allow to tame the complexity of big data management tasks within such frameworks, hence beneficially influencing all the other activities, perhaps delivered as services in a reference Cloud architecture. Inspired by these considerations, in this paper we provide an overview on noticeable state-of-the-art big data compression techniques, and depict future research directions on the investigated scientific topic to be considered during future years.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
