Abstract:

Important decision making requires allsource data support. However, in the age of big data, collecting decision information becomes even more difficult due to data variety and heterogeneity. To get precise search results, users spend much time on information requirement expression, which causes low efficiency. We design a general language for information requirement description, along with its translation methods for common resources such as databases, publishsubscribe systems and search engines. Thus allsource heterogeneous information can be collected by one request in a unified format. Test results demonstrate that its ability to express requirement semantics can solve the crossarea heterogeneity problem of big data with the aid of ontology techniques, and therefore improves the precision and comprehension of information collection.

Key words: big data;decision support;information collection;requirement description