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Data Quality, Data Cleaning and Treatment of Noisy Data

Data Quality, Data Cleaning and Treatment of Noisy Data

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Data Quality, Data Cleaning and Treatment of Noisy Data

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  1. Data Quality, Data Cleaning and Treatment of Noisy Data DIMACS Workshop November 3-4, 2003 Organizer: Tamraparni Dasu, AT&T Labs - Research

  2. Workshop • Talks cover different aspects of the complex DQ issue • Outstanding set of speakers from academia, industrial labs and industry • Cover theoretical, methodological, applied aspects – case studies! • From a wide range of disciplines and areas

  3. Welcome!

  4. Rene Miller • University of Toronto • Renee is an Associate Professor of Computer Science at the University of Toronto. S.B., Mathematics, MIT. S.B., Cognitive Science, MIT. Ph.D., Computer Science, U. Wisconsin-Madison. • Heterogeneous databases, data mining, and data warehousing. • “Managing Inconsistency in Data Exchange and Integration”

  5. Grace Zhang • Morgan Stanley Institutional Equity Division IT. Master of Philosophy in Computer Science from Columbia University, and a Master and B.S. in Computer Science from Zhongshan University,China. • Develop tools to check data quality issues in equity trading data, design and build the standard destination referential data repository. • “Data Quality in Trading Surveillance”

  6. Ted Johnson • AT&T Labs – Research • Database Research department. B.S. in Mathematics, Johns Hopkins University, Ph.D. in Computer Science, New York University, 1990. • Data warehousing and data mining • “Bellman - A Data Quality Browser “

  7. Ron Pearson • Daniel Baugh Institute for Functional Genomics and Computational Biology, Thomas Jefferson University. B.S. in physics from the University of Arkansas at Monticello and M.S.E.E. and PhD in electrical engineering from M.I.T. in 1982. • Design and analysis of nonlinear digital filters, exploratory data analysis and the validation of analytical results. • “The Data Cleaning Problem -- Some Key Issues and Practical Approaches”

  8. Dhammika Amaratunga, Javier Cabrera, Nandini Raghavan • Johnson & Johnson, Rutgers University, Johnson & Johnson • “Pre-processing of Microarray Data”

  9. S. Muthukrishnan • Rutgers University, AT&T Labs – Research • Associate Professor of Computer Science • Design and analysis of algorithms • “Checks and Balances: Monitoring Data Quality Problems in Network Traffic Databases”

  10. T. Bonates, P. Hammer, A. Kogan, and I. Lozina • RutCOR, Rutgers University • Operations Research • Maximum Patterns and Outliers in the Logical Analysis of Data (LAD)

  11. Jiawei Han • Professor, Simon Fraser University. Currently at University of Illinois, UC. Ph. D. from University of Wisconsin, Madison in 1985. • Data mining (knowledge discovery in databases), data warehousing, spatial databases, multimedia databases, deductive and object-oriented databases, and logic programming • “Data Mining: A Powerful Tool for Data Cleaning”

  12. Jon Hill • British Telecommunications • Jon leads a team of information experts to deliver solutions within asset management, process control and billing assurance. Jon uses a wide range of information quality tools within projects and has extensive experience in investigation and solving IQ problems. • “A $220 Million Success Story”

  13. G. Vesonder, J. Wright & T. Dasu • AT&T Labs - Research • Head of Adaptive Systems research • AI, Knowledge Engineering, Expert Systems • “Life Cycle Datamining”

  14. Andrew Hume • AT&T Labs – Research • Very large data systems, string searching, performance measurement • Tamed many legacy systems • “Managing Data Streams”

  15. Bing Liu • Associate Professor at National Singapore University, on leave at University of Illinois at Chicago • Data mining and knowledge discovery; web, text and image mining; Bioinformatics • Web page cleaning for web data mining

  16. R.K. Pearson and M. Gabbouj • Collaboration with Moncef Gabbouj from the Tampere University of Technology in Finland. • “Relational Nonlinear FIR Filters”

  17. Thank you!