Data Quality, Data Cleaning and Treatment of Noisy Data

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Data Quality, Data Cleaning and Treatment of
Noisy Data

• DIMACS Workshop
• November 3-4, 2003
• Organizer Tamraparni Dasu, ATT Labs - Research

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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

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Welcome!
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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

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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

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Ted Johnson

• ATT 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

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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

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Dhammika Amaratunga, Javier Cabrera, Nandini
Raghavan

• Johnson Johnson, Rutgers University, Johnson
  Johnson
• Pre-processing of Microarray Data

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S. Muthukrishnan

• Rutgers University, ATT Labs Research
• Associate Professor of Computer Science
• Design and analysis of algorithms
• Checks and Balances Monitoring Data Quality
  Problems in Network Traffic Databases

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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)

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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

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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

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G. Vesonder, J. Wright T. Dasu

• ATT Labs - Research
• Head of Adaptive Systems research
• AI, Knowledge Engineering, Expert Systems
• Life Cycle Datamining

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Andrew Hume

• ATT Labs Research
• Very large data systems, string searching,
  performance measurement
• Tamed many legacy systems
• Managing Data Streams

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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

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R.K. Pearson and M. Gabbouj

• Collaboration with Moncef Gabbouj from the
  Tampere University of Technology in Finland.
• Relational Nonlinear FIR Filters

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Thank you!