A Method to Identify Relevant Information Sufficient to Answer Situation Dependent Queries
作者： Shan Lu , Mieczyslaw M. Kokar
DOI: 10.1109/COGSIMA.2018.8423973
关键词: Data stream mining 、 Data science 、 Semantics 、 Information overload 、 Order (exchange) 、 Computer science 、 Situation analysis 、 Inference 、 Relevance (information retrieval) 、 Application domain
摘要: In various complex and dynamic environments, having a good understanding of the current situation in hand is foundation for successful decision-making. Several frameworks have been proposed information gathering interpretation assessment. However, decision makers nowadays face an overload challenge during When maker deals with specific situation, usually large volumes are delivered to him or her real time, which only few relevant. It practically impossible them deal such huge data streams time. Additionally, if needs be communicated others, it not clear what relevant thus would need sent over (sometimes over-loaded) communication links order convey description situation. Therefore, method needed support human identify this paper, we develop inference-based relevance reasoning assessment automatically characterizing that dealing with. By using method, following two basic questions will answered: (1) kind characterize situation? (2) how automatically? take cyber security as application domain, evaluate our dataset generated by Skaion corporation. We use four metrics method.
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