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Capturing Data Uncertainty in HighVolume Stream Processing
Mathematics and Statistics Department Faculty Publication Series
  • Yanlei Diao, University of Massachusetts - Amherst
  • Boduo Li, University of Massachusetts - Amherst
  • Anna Liu, University of Massachusetts - Amherst
  • Liping Peng, University of Massachusetts - Amherst
  • Charles Sutton, University of California - Berkeley
  • Thanh Tran, University of Massachusetts - Amherst
  • Michael Zink, University of Massachusetts - Amherst
Publication Date
We present the design and development of a data stream system that captures data uncertainty from data collection to query processing to final result generation. Our system focuses on data that is naturally modeled as continuous random variables such as many types of sensor data. To provide an end-to-end solution, our system employs probabilistic modeling and inference to generate uncertainty description for raw data, and then a suite of statistical techniques to capture changes of uncertainty as data propagates through query operators. To cope with high-volume streams, we explore advanced approximation techniques for both space and time efficiency. We are currently working with a group of scientists to evaluate our system using traces collected from real-world applications for hazardous weather monitoring and for object tracking and monitoring.
This paper was harvested from and ArXiv identifier is arXiv:0909.1777
Citation Information
Yanlei Diao, Boduo Li, Anna Liu, Liping Peng, et al.. "Capturing Data Uncertainty in HighVolume Stream Processing" (2009)
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