Big Data Analytics is an emerging field since massive storage and computing capabilities have been made available by advanced e-infrastructures. Earth and Environmental sciences are likely to benefit from Big Data Analytics techniques supporting the processing of the large number of Earth Observation datasets currently acquired and generated through observations and simulations. However, Earth Science data and applications present specificities in terms of relevance of the geospatial information, wide heterogeneity of data models and formats, and complexity of processing. Therefore, Big Earth Data Analytics requires specifically tailored techniques and tools. The EarthServer Big Earth Data Analytics engine offers a solution for coverage-type datasets, built around a high performance array database technology, and the adoption and enhancement of standards for service interaction (OGC WCS and WCPS). The EarthServer solution, led by the collection of requirements from scientific communities and international initiatives, provides a holistic approach that ranges from query languages and scalability up to mobile access and visualization. The result is demonstrated and validated through the development of lighthouse applications in the Marine, Geology, Atmospheric, Planetary and Cryospheric science domains.
Pessimistic quorum-based data replication strategies have strictly upper-bounded operation availabilities. The probabilistic relaxation of their strict consistency notion along different dimensions permits to overcome this upper-bound. The resulting probabilistic replication strategies allow the exploitation of the data consistency versus operation availabilities trade-off. In our approaches, we add a certain number of probabilistically intersecting quorums to the originally strictly intersecting partially ordered quorum system in order to 'closely conserve' its original characteristic behaviour. As one expects, the number and nature of probabilistic quorums added have a strong impact on the particular trade-off. We present two algorithms for the generation of probabilistic quorums based on input strict quorums. Furthermore, the order in which probabilistic and strict quorums are to be probed and used for executing operations is also very important. We relate three general strategies of ordering the probabilistic and strict quorums of those probabilistic quorum systems (PQSs), evaluate their impact on the data consistency versus operation availabilities trade-off and compare them in terms of this trade-off. The evaluation uses Markov chain-based steady-state analysis applied to a representative instance of PQSs with adequate partial orderings.
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