Xtream: A System for Continuous Querying over Uncertain Data Streams

摘要: Data stream and probabilistic data have been recently considered noticeably in isolation. However, there are many applications including sensor management systems object monitoring which need both issues tandem. The existence of complex correlations lineages prevents Probabilistic DBMSs (PDBMSs) from continuously querying temporal positioning sensed data. Our main contribution is developing a new system to run queries on streams with satisfactory fast speed, while being faithful uncertainty aspects We designed model for streams. also presented query operators implement threshold SPJ aggregation (SPJA queries). In addition most importantly, we build java-based working system, called Xtream, supports input final results. Unlike databases, the data-driven design Xtream makes it possible high-volumes bursty this paper, after reviewing characteristics motivating streams, present our model. Then focus algorithms approximations basic (select, project, join, aggregate). Finally, compare prototype Orion only existing DBMS that continuous distributions. experiments demonstrate how outperforms w.r.t. efficiency metrics such as tuple latency (response time) throughput well accuracy, critical parameters any system.