A three-way decisions model with probabilistic rough sets for stream computing

- Present stream computing learning method to solve the stream mining tasks.
- Formalize the problem knowledge updating of stream computing in the semantic of three-way decisions.
- Present a new method SS3WD for knowledge updating of stream computing.
- Correctness and Completeness of SS3WD is demonstrated theoretically and practically.
- Extend the application domain of three-way decisions to stream computing.

Stream computing paradigm, with the characteristics of real-time arrival and departure, has been admitted as a major computing paradigm in big data. Relevant theories are flourishing recently with the surge development of stream computing platforms such as Storm, Kafka and Spark. Rough set theory is an effective tool to extract knowledge with imperfect information, however, related discussions on synchronous immigration and emigration of objects have not been investigated. In this paper, stream computing learning method is proposed on the basis of existing incremental learning studies. This method aims at solving challenges resulted from simultaneous addition and deletion of objects. Based on novel learning method, a stream computing algorithm called single-object stream-computing-based three-way decisions (SS3WD) is developed. In this algorithm, the probabilistic rough set model is applied to approximate the dynamic variation of concepts. Three-way regions can be determined without multiple scans of existing information granular. Extensive experiments not only demonstrate better efficiency and robustness of SS3WD in the presence of objects streaming variation, but also illustrate that stream computing learning method is an effective computing strategy for big data.