Probabilistic Event EXtractor for RFID Data
Radio-Frequency Identification (RFID) technology is increasingly
being used to support various industrial and
ubiquitous computing applications. Although this technology
holds the promise to facilitate many of our every
day activities, the noisy and low-level data produced by
RFID readers today is extremely difficult to use or comprehend
in most but the simplest settings.
PEEX is a system that enables applications to easily
define, extract, and manage meaningful probabilistic high-level
events from low-level RFID data. By using a declarative
query language, the system simplifies definitions of new
events. By using probabilities, the system copes with the
noise and errors in the data and the inherent ambiguity in
the event extraction.
We have designed PEEX as a layer on top of a traditional
RDBMS (currently Microsoft SQL Server).
This design enables us to demonstrate the
benefits of a probabilistic RFID DBMS, while leveraging
all the features of an existing RDBMS. As illustrated in the figure below,
the core components of PEEX are
- Event Detector (extracts events specified by the event definitions)
- Confidence Learner (learns from training data the historical probability that different
combinations of tag sightings correspond to a high-level event)
- Partial Event Generator (generates multiple partial events from one definition thus helping
in coping with false negatives in the input data.)
The PEEX project is partially supported by NSF Grants IIS-0627585, IIS-0513877, IIS-0713576, CNS-0454425,
CRI-0454394, the UW College of Engineering, and Gifts from Microsoft including a gift under the SensorMap RFP.
Any opinions, findings, and conclusions or recommendations expressed
in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
Evan Welbourne, Garret Cole, Nodira Khoussainova, Julie Letchner, Yang Li, Magdalena Balazinska, Gaetano Borriello, Dan Suciu.
Specification, Detection, and Notification of RFID Events with Cascadia. MobiSys Demo 2008.
Evan Welbourne, Nodira Khoussainova, Julie Letchner, Yang Li, Magdalena Balazinska, Gaetano Borriello, Dan Suciu.
Cascadia: A System for Specifying, Detecting, and Managing RFID Events. MobiSys 2008.
Nodira Khoussainova, Evan Welbourne, Magdalena Balazinska, Gaetano Borriello, Julie Letchner, Christopher Re, Dan Suciu, Jordan Walke.
A Demonstration of Cascadia Through a Digital Diary Application. SIGMOD Demo 2008.
Nodira Khoussainova, Magdalena Balazinska, Dan Suciu. PEEX: Extracting Probabilistic Events from RFID Data. ICDE 2008. [Full version]
Nodira Khoussainova, Magdalena Balazinska, Dan Suciu. Probabilistic RFID Data Management. UW CSE Technical Report UW-CSE-07-03-01. March 1, 2007.
Last Modified at 2008-09-07 11:02:21 Pacific Daylight Time