Stream Reasoning on Expressive Logics
This is an incremental survey that addresses the problem of efficient reasoning for developers and researchers working with streaming data in applications like IoT or real-time analytics.
The paper surveys existing research on reasoning with expressive logics for streaming knowledge bases, highlighting the need for efficient continuous reasoning techniques due to the high computational cost of traditional methods on streaming data.
Data streams occur widely in various real world applications. The research on streaming data mainly focuses on the data management, query evaluation and optimization on these data, however the work on reasoning procedures for streaming knowledge bases on both the assertional and terminological levels is very limited. Typically reasoning services on large knowledge bases are very expensive, and need to be applied continuously when the data is received as a stream. Hence new techniques for optimizing this continuous process is needed for developing efficient reasoners on streaming data. In this paper, we survey the related research on reasoning on expressive logics that can be applied to this setting, and point to further research directions in this area.