Recent Career Highlights
I passed my oral qualifying exam (May 2007),
making me a Ph.D. candidate,
with dissertation title "Supporting knowledge discovery in data stream
management systems". A copy of my qual proposal can be found
here(pdf). I also got married
to my not-so-techie wife in Dec 2006.
I started my Ph.D. in September 2003 and I have been doing research with
Professor Zaniolo since then.
I worked
as a research intern at Google for summer 2007 on click stream analysis. I also worked as a research intern at
IBM Almaden Research Center for summer 2005 and 2006,
where I focused on different research areas such as
sequence query langugages, Web 2.0 enterprise applications,
RFID data analysis and cleansing, etc. I received my M.S. and B.S.
degrees in Computer
Science from UCLA in March 2005 and September 2003, respectively.
For the last year and half of my B.S. I was also working
at Citibank as a Quality Assuarance intern. I transferred to UCLA in January 2002 from
Wright State University, Ohio.
Jun Rao, Sangeeta Doraiswamy, Hetal Thakkar, and Latha Colby. A deferred cleansing method for RFID data analytics. VLDB 2006.
(pdf)
Xin Zhou., Hetal Thakkar, Carlo Zaniolo. Unifying the processing
of XML streams and relational data. ICDE 2006.
(pdf)
Chang Luo, Hetal Thakkar, Haixun Wang, and Carlo Zaniolo. A native
extension of SQL for mining data streams. ACM SIGMOD 2005 demo paper.
(pdf)
Yijian Bai, Chang Luo, Hetal Thakkar, and Carlo Zaniolo.
Efficient support for time series queries in data stream
management systems. In Stream Data Management-Chapter
6. N. Chaudhry, K. Shaw and M. Abdelguerfi (EDs.),
Kluwer, Vol. 30, 2004.
(pdf)
Projects
Stream Mill Stream Mill brings power and generality to data stream management systems. Stream Mill supports Expressive Stream Language, which allows complex queries on data streams through minimal extensions to SQL. Furthermore, Stream Mill aims to unify the processing of relational streams and XML streams.
ATLaS ATLaS is a powerful Database Systems that supports simple extensions to SQL that allow efficient data mining.