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Many systems require access to very large amounts of data to properly function, like systems allowing to visualize or predict meteorological changes in a country over a given period of time, or any other system holding, processing and displaying scientific or sensor data. However, filling out a database with large amounts of valuable data can be a difficult, costly and time-consuming task. In this paper, we present techniques to create large amounts of data by combining crowdsourcing, data generation models, mobile computing, and big data analytics. We have implemented our methods in a system, NoizCrowd, allowing to crowdsource noise levels in a given region and to generate noise models by using state-of-the-art noise propagation models and array data management techniques. The resulting models and data can then be accessed using a visual interface.

The proliferation of semantic data on the Web requires RDF database systems to constantly improve their scalability and transactional efficiency. At the same time, users are increasingly interested in investigating or visualizing large collections of online data by performing complex analytic queries. This paper introduces a novel database system for RDF data management called dipLODocus[RDF], which supports both transactional and analytical queries efficiently. dipLODocus[RDF] takes advantage of a new hybrid storage model for RDF data based on recurring graph patterns. In this paper, we describe the general architecture of our system and compare its performance to state-of-the-art solutions for both transactional and analytic workloads.