Aspects of Stream Processing Stream processing has been and is still a highly relevant research topic in computer science. There are quite a few research paper titles hinting concisely to various important aspects of stream processing, be it the ubiquity of streams due to the temporality of most data (“It’s a streaming world!”, ), or […]
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.