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Predictive Modeling in Data Management. Byung S. Lee Computer Science University of Vermont http://www.emba.uvm.edu/~bslee/homepage/. Cost UDF Overview. Funding: US Department of Energy. Title: Generating Cost Functions of User-Defined Functions. Phase 1: preliminary studies.

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predictive modeling in data management

Predictive Modeling in Data Management

Byung S. Lee

Computer Science

University of Vermont

http://www.emba.uvm.edu/~bslee/homepage/

cost udf overview
Cost UDF Overview
  • Funding: US Department of Energy.
  • Title: Generating Cost Functions of User-Defined Functions.
  • Phase 1: preliminary studies.
  • Phase 2: core modeling techniques.
  • Phase 3: applications.
phase 1
Phase 1
  • Approaches:
    • Off-line training with cost data sets generated in the same batch.
    • On-line training with cost data sets generated in incremental batches. (a.k.a. self-tuning)
  • Models:
    • parametric or nonparametric regression.
phase 11
Phase 1
  • UDFs:
    • Financial time series aggregate functions:
      • median(time series, start date, end date)
      • nth moving window average(time series, start date, end date, window size)
    • Keyword-based text search functions:
      • “dog AND cat”
      • “dog OR cat”
      • “dog cat” within w words apart.
    • Spatial search operators:
      • range(ref_point, distance)
      • Window(lower_left_point, upper_right_point)
      • KNN(ref_point, K)
phase 2
Phase 2
  • Approaches:
    • On-line training with cost data points generated one at a time.
    • Assume limited main memory.
  • Models:
    • Nonparametric techniques using multidimensional index structures.
phase 21
Phase 2
  • Core modeling techniques:
    • Incremental edited k nearest neighbors.
    • Memory limited quadtrees.
    • Dr. Zhen He will give a quick overview of the recent phase 2 efforts.
phase 3
Phase 3
  • Additional core modeling techniques.
  • Abstraction of the problem to “efficient adaptive predictive modeling of incremental data.”
  • Applications that need
    • Value predictions.
    • Class predictions.