CLARO: modeling and processing uncertain data streams

• Published in: The VLDB journal Vol. 21; no. 5; pp. 651 - 676
• Main Authors: Tran, Thanh T. L., Peng, Liping, Diao, Yanlei, McGregor, Andrew, Liu, Anna
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer-Verlag 01.10.2012; Springer
• Subjects: Applied sciences; Computer Science; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Database Management; Exact sciences and technology; Information systems. Data bases; Memory organisation. Data processing; Regular Paper; Software; Data models; Query processing; Continuous uncertainty; Uncertain data streams; Streaming; High performance; Statistical analysis; Database query; High precision; Data processing; Modeling; Relevance; Missing data; Uncertain system; Performance requirement; Planning; Sampling; Incomplete information; Random variable; Monitoring
• ISSN: 1066-8888; 0949-877X
• DOI: 10.1007/s00778-011-0261-7

Uncertain data streams, where data are incomplete and imprecise, have been observed in many environments. Feeding such data streams to existing stream systems produces results of unknown quality, which is of paramount concern to monitoring applications. In this paper, we present the claro system that supports stream processing for uncertain data naturally captured using continuous random variables. claro employs a unique data model that is flexible and allows efficient computation. Built on this model, we develop evaluation techniques for relational operators by exploring statistical theory and approximation. We also consider query planning for complex queries given an accuracy requirement. Evaluation results show that our techniques can achieve high performance while satisfying accuracy requirements and outperform state-of-the-art sampling methods.