基于数据世系管理的精准农业不确定性复杂事件处理

伴随物联网采集数据流的增加和对复杂事件匹配的准确性、可靠性要求的提高,不确定性复杂事件处理的研究得到广泛的关注。精准农业中存在大量的RFID及传感器监测数据,但是监测硬件和无线通信技术不能保证100%的可信数据,因此需要一个能应用在精准农业中处理不确定事件的流处理引擎。本文在现有流处理引擎SASE基础上,加入概率流理论和数据世系管理理论,提出一种新型的复杂事件处理引擎PUCEP,可以计算输出复杂事件的概率,同时提高不确定性复杂事件的匹配效率,从而减少计算资源消耗和反应时间,提高整个系统的实时性。实验使用温室传感器采集数据,结果表明所提方法对处理概率事件流的复杂事件是有效的。

Uncertain Complex Event Processing in Precision Agriculture Based on Data Provenance Management

With the increase of event flow generated from sensor kind electronic devices in IOT(Internet of things) and increasing demand of matching accuracy/confidence of more complex events, uncertain complex event processing is becoming more and more been concerned. A large number of RFID or sensor monitoring data exist in precision agriculture, but current hardware and wireless communication techniques cannot support 100% confident data. One stream processing engine which can process uncertain data in precision agriculture is needed. In this paper, a new type of complex event processing engine PUCEP(Provenance uncertain complex event processing) was proposed, in which probability flow theory and data provenance management theory were added based on the existing flow processing engine SASE. Sufficient approximate lineage query algorithm is used to calculate the probability of an event in order to improve the efficiency of probability calculation of large amount of data and the pattern matching was carried out by using the two fork tree and NFA. This optimized method can not only calculate the probability of outputs of compound events but also improve the matching efficiency of uncertain complex events, thereby reducing the computation cost and response time to a realistic degree. The experiment uses sensor data acquired from an agricultural greenhouse and shows that this method is efficient in processing complex events over probabilistic event streams.