One of the most hyped activities in modern-day computer science is the collection of unique, interesting and valuable big data resources. Popular examples are patient records, individual genomes, star cluster data, financial transaction histories, and shopping habits. Collecting and storing these data are simple problems with established solutions, the true challenge lies in analysing big data for informed decision-making. […]
One of the most hyped activities in modern-day computer science is the collection of unique, interesting and valuable big data resources. Popular examples are patient records, individual genomes, star cluster data, financial transaction histories, and shopping habits. Collecting and storing these data are simple problems with established solutions, the true challenge lies in analysing big data for informed decision-making. […]
Engineers in industry spend a significant amount of their time searching for data that they require for their core taks. E.g., in the oil&gas industry, 30–70% of engineers time is spent looking for and assessing the quality of data. (Crompton, 2008)
Numbers provided by Optique partners Siemens and Statoil suggest that Optique could free up a substantial amount of expert time annually in these two companies alone. The potential benefit from a more effective analysis of the data will be even higher.
The project will provide a semantic end-to-end connection between users and data sources. The Optique platform will feature an intuitive, general, but adaptable end-user interface. Data can be integrated from multiple, also streaming, data sources, with the possibility for large-scale parallel cloud processing and storage.
The Optique project brings a unique combination of technologies to bear on Big Data challenges: An end-user-oriented query interface; scalable query rewriting from the end-user to the source vocabulary; temporal and real-time continuous stream processing; scalable storage and query evaluation using elastic clouds.