Leveraging Spatio-Temporal Redundancy for RFID Data Cleansing
- LecturerDr. Wei-Shinn Ku (Assistant professor of Auburn University)
Host: Dr. Ling-Jyh Chen - Time2010-08-11 (Wed.) 10:30 – 12:00
- LocationAuditorium 106 at new IIS Building
Abstract
Abstract: Radio Frequency Identification (RFID) technologies
are used in many applications for data collection. However,
raw RFID readings are usually of low quality and may contain
many anomalies. An ideal solution for RFID data cleansing
should address the following issues. First, in many
applications, duplicate readings (by multiple readers
simultaneously or by a single reader over a period of time)
of the same object are very common. The solution should take
advantage of the resulting data redundancy for data cleaning.
Second, prior knowledge about the readers and the
environment (e.g., prior data distribution, false negative
rates of readers) may help improve data quality and remove
data anomalies, and a desired solution must be able to
quantify the degree of uncertainty based on such knowledge.
Third, the solution should take advantage of given
constraints in target applications (e.g., the number of
objects in a same location cannot exceed a given value) to
elevate the accuracy of data cleansing. There are a number
of existing RFID data cleansing techniques. However, none of
them support all the aforementioned features. In this talk,
I will introduce a Bayesian inference based approach for
cleaning RFID raw data. The approach takes full advantage of
data redundancy. To capture the likelihood, we design an n-
state detection model and formally prove that the 3-state
model can maximize the system performance. Moreover, in
order to sample from the posterior, we devise a Metropolis-
Hastings sampler with Constraints (MH-C), which incorporates
constraint management to clean RFID raw data with high
efficiency and accuracy.
Bio: Wei-Shinn Ku received his Ph.D. degree in computer
science from the University of Southern California (USC) in
2007. He also obtained both the M.S. degree in computer
science and the M.S. degree in Electrical Engineering from
USC in 2003 and 2006 respectively. He is an Assistant
Professor with the Department of Computer Science and
Software Engineering at Auburn University, USA. His research
interests include spatial and temporal data management,
mobile data management, geographic information systems, and
security and privacy. He has published more than 40 research
papers in refereed international journals and conference
proceedings. He is a member of the ACM and the IEEE.