Processing system and method for sensing context by moving based on stream calculation

Abstract

The invention discloses a processing system and method for sensing a context by moving based on stream calculation. A context filtering module based on a rule is provided by the processing system. The filtering module will match context information according to the Rete rule matching algorithm, thus the context information is rapidly and effectively filtered. The processing system provides a context converging and optimizing module which is characterized in that a document type internal memory database is called based on a document object model and a converging computation unit in a stream calculation platform is organically combined with the document type internal memory database. The number of times of reading and writing a disc in operation can be effectively reduced and the converging efficiency of the context information is improved. In addition, the processing system provides an incident capturing module based on scheduling. The incident capturing module organically combines cluster sensing and individual sensing, individual behavior can be obtained in real time, thus the situation of a certain individual in a cluster can be effectively known.

Description

technical field [0001] The present invention relates to the technical field of mobile cloud computing, in particular to a flow computing-based mobile perception context processing system and method. Background technique [0002] The mobile perception context refers to the sensing information obtained by the mobile terminal device from the surrounding environment through its sensors. Stream computing, or data stream processing, is a computing model that extracts effective knowledge and information from huge and diverse continuous data streams. [0003] Since the present invention relates to the processing of large-scale mobile sensing context, unless otherwise specified, the mobile sensing application in the present invention specifically refers to the mobile group sensing application. In mobile sensing applications, sensing action is a continuous event, and sensor data collection is a continuous process, so the data uploaded by mobile devices is a kind of streaming data tha...

AI Technical Summary

Problems solved by technology

Storm meets the actual needs of Twitter through simple implementation, but the replay-based recovery mechanism is difficult to guarantee high availability and computing consistency, and the development based on the functional language Clojure also makes it difficult to be widely used as open source; Borealis has great influence on all aspects of data flow computing The research is extensive and in-depth, and has good reference value, but because it is a product of academia, it has not been used on a large scale in the commercial world; and although important functions such as dynamic load balancing and online service migration have not yet been realized, the version update of S4 is slow, but It uses Java development to provide a simple programming interface, supports state persistence, and has high fault recovery capabilities. In addition, it uses a decentralized and symmetrical architecture, which can fully meet the needs of simple development, dynamic deployment, and symmetrical scalability.

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