DYNAMIC AND DISTRIBUTED DATA IN SCIENTIFIC APPLICATIONS: CHALLENGES AND SCENARIOS

Abstract

The NERC Virtual Observatory initiative aims to create a cloud-based pilot environment for the management and processing of various types of hydrological data on UK’s rivers and soils. It’s a challenge to combine fluid and dispersed data from numerical models and different sensors along with a possibility of retasking sensors or adapting models in real-time. In this paper we look at the issues that make it difficult to manage data that is either dynamic or distributed. The context of our study is scientific applications, particularly environmental and the biosciences domain. It describes dynamic data, it discusses different types of dynamic data and its use in web environments and related protocols. Also, the article mentions a couple of cases such as the applications of biosciences in next-generation sequencing and ATLAS physics experiment in CERN to show the usage of dynamic data across infrastructures. The article looks at issues regarding data provenance, versioning and making decisions on the fly too. Every challenge is then connected to the bigger picture. This picture consists of trends type of data intensive research and the EU’s 2030 agenda.