The FLAMINGO Project on Data Cleaning Department of Computer Science, UC Irvine

Objective

The Flamingo Project focuses on data cleaning, i.e., how to deal with errors and inconsistencies in information systems. As an example, in many applications such as data integration, commercial organizations need to collect data from various sources to conduct analysis and make decisions. Often, the data from these different sources can have inconsistencies. For instance, we use first name, last name, SSN, and birthday to identify a person. However, the same name, e.g., "Schwarzenegger", may be misspelled as "Swarzzengaer" or other forms. Such errors make it more challenging to link records from different places and answer queries approximately. We are developing algorithms in order to make query answering and information retrieval efficient in the presence of such inconsistencies and errors.

News

• (4/1/2008) We are glad to release the PSearch Prototype to support interactive, fuzzy search for UCI Directory.
• (4/17/2007) We are glad to release the first version of our Flamingo Package on approximate string matching.

Releases

• 1.0 (April 17th, 2007)

People

• Alexander Behm (Ph.D. Student)
• Shengyue Ji (Ph.D. Student)
• Chen Li (Faculty)
• Rares Vernica (Ph.D. Student)

Alumni and Visitors

• Guoliang Li, spring of 2008, visitor from Tsinghua University, China.
• Jiaheng Lu, postdoc, 2006-2008
• Yiming Lu, graduated from UC Irvine in 2008
• Bin Wang and Xiaochun Yang, summers of 2006, 2007, and 2008, visitors from Northeastern University, China
• Liang Jin, graduated from UC Irvine in 2005

Publications

• Cost-Based Variable-Length-Gram Selection for String Collections to Support Approximate Queries Efficiently PDF PPT
  Xiaochun Yang, Bin Wang, Chen Li.
  SIGMOD 2008.
• Efficient Merging and Filtering Algorithms for Approximate String Searches PDF PPT
  Chen Li, Jiaheng Lu, and Yiming Lu.
  ICDE 2008.
• SEPIA: Estimating Selectivities of Approximate String Predicates in Large Databases
  Liang Jin, Chen Li, and Rares Vernica.
  VLDB Journal 2007. It's an extended version of the SEPIA paper in VLDB05.
• VGRAM: Improving Performance of Approximate Queries on String Collections Using Variable-Length Grams. PDF PPT
  Chen Li, Bin Wang, and Xiaochun Yang.
  VLDB 2007, Vienna, Austria
• Relaxing Join and Selection Queries. PDF PPT
  Nick Koudas, Chen Li, Anthony Tung, and Rares Vernica.
  VLDB 2006, Seoul, Korea.
• Selectivity Estimation for Fuzzy String Predicates in Large Data Sets. PDF PPT
  Liang Jin and Chen Li.
  VLDB 2005, Trondheim, Norway.
• Indexing Mixed Types for Approximate Retrieval. PDF PPT
  Liang Jin, Nick Koudas, Chen Li, Anthony K.H. Tung.
  VLDB 2005, Trondheim, Norway.
• NNH: Improving Performance of Nearest-Neighbor Searches Using Histograms. PDF Full Version PPT
  Liang Jin, Nick Koudas, Chen Li.
  EDBT 2004, Heraklion - Crete, Greece.
• Efficient Record Linkage in Large Data Sets. PDF, PPT
  Liang Jin, Chen Li, and Sharad Mehrotra.
  8th International Conference on Database Systems for Advanced Applications (DASFAA) 2003, Kyoto, Japan.
• Supporting Efficient Record Linkage for Large Data Sets Using Mapping Techniques
  Chen Li, Liang Jin, and Sharad Mehrotra
  World Wide Web Journal, Volume 9, Number 4, pages 557-584, December 2006.
  This journal article is an extended version of the DASFAA03 paper.

Acknowledgements: This release is partially supported by the NSF CAREER Award No. IIS-0238586, the NSF award No. IIS-0742960, the NSF-funded RESCUE project, a Google Research Award, and a fund from CalIt2.

For any questions regarding this project, please send email to flamingo AT ics.uci.edu