November 14, 2012
Dr. Nebiyou Tilahun, UPP, presents a seminar entitled "An agent based model of origin destination estimation (ADOBE)" Wednesday, November 14th at 4:00 pm in Rm 1127 SEO
November 7, 2012
Mr. Thomas Murtha, CMAP, will address the CTS-IGERT community at 4:00 p.m. in Room 1127 SEO.
October 24, 2012
Please join us in welcoming Dr. Bo Zou, CME, on Wednesday, October 24th, Room 1127 SEO, 4:00 p.m.
September 25, 2012
Award Received by Joshua Auld, CTS-IGERT alumnus.
April 20, 2012
Congratulations to James Biagioni, CTS Fellow and CS PhD candidate, winner of the Dean's Scholar award.
January 2, 2012
James Biagioni, CTS Fellow, receives "Best Presentation Award" at SenSys2011
July 30, 2010
Dr. Ouri Wolfson, Dr. Phillip Yu, and Leon Stenneth, CS student and CTS Associate, recently had a paper accepted to the 6th IEEE International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob 2010).
April 22, 2009
CTS-IGERT Trainee Chad A. William's collaborative work accepted for publication! "Attribute Constrained Rules For Partially Labeled Sequence Completion" Authors: Chad A. Williams, Peter C. Nelson, and Abolfazl Mohammadian.
This paper will appear in the Proceedings of the 9th Industrial Conference on Data Mining (ICDM 2009), Leipzig, Germany, July 2009. Abstract: Sequential pattern and rule mining have been the focus of much research in the data mining community, however predicting missing sets of elements within a sequence remains a challenge. Recent work in survey design suggests that if these missing elements can be inferred with a higher degree of certainty, it would greatly reduce the time burden on survey participants. To address this problem and the more general problem of missing sensor data, we introduce a new form of constrained sequential rules that use attribute presence to better capture rule confidence in sequences with missing data than previous constraint based techniques. Specifically we examine the problem of given a partially labeled sequence of sets of attributes, how well can the missing attributes be inferred. Our study shows this technique significantly improves prediction robustness when even large amounts of sequence data are missing compared to traditional techniques, as demonstrated on a publicly available travel survey data set.