ALG Overview : Knowledge Discovery in Databases

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ALG Overview Knowledge Discovery in Databases
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ALG Mission

• The specific mission of the Automated Learning
  Group is
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• To collaborate with researchers to develop novel
  computer methods and the scientific foundation
  for using historical data to improve future
  decision making
• To work closely with industrial, government, and
  academic partners to explore new application
  areas for such methods, and
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• To transfer the resulting software technology
  into real world applications

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Group Core Activities
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Knowledge Discovery In Databases Process
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D2K - Data To Knowledge

• D2K is a rapid, flexible data mining system that
  integrates effective analytical data mining
  methods for prediction, discovery, and anomaly
  detection with data management and information
  visualization.
• Visual Programming Environment
• Robust Computational Infrastructure
• Flexible And Extensible Architecture
• Rapid Application Development Environment
• Integrated Environment for Models and
  Visualization
• Workflow and Group Use Interface

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D2K Infrastructure, Toolkit, Modules, and
Applications

• Data Selection
• Distributed Knowledge Sources
• Data Transformation
• Feature selection/ construction
• Example selection
• Data Modeling
• Scalable Algorithms
• Predictive
• Discovery
• Anomaly Detection
• Bias Optimization
• Layer Learning
• Model Evaluation
• Information Visualization

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D2K/T2K/I2K - Data, Text, and Image Analysis
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TRECC Projects - ThemeWeaver

• Motivation There is a need for gathering
  knowledge from text data.
• Text Data Mining is the extraction of
  meta-knowledge from many large collections of
  documents.
• Such knowledge includes
• Organizational patterns based on similarity
  (clustering)
• Topic detection, evolution and monitoring
• Multi-document summarization
• Knowledge extraction

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TRECC Projects HyperComputiCations

• Wireless Augmented Reality (HyperComputiCations
  Motorola)

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Research Project Evolutionary Multi-objective
Optimization

• These tradeoffs are identified using an
  evolution-based multi-objective optimization
  (EMO) algorithm called the non-dominated sorted
  genetic algorithm (NSGA)
• Provides the ability to compare trade-offs in a
  multi-objective systems
• Long term management of contaminated ground water
  sites

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I2K - Image to Knowledge

• Motivation The need for fast and reliable image
  analysis is ubiquitous.
• Image examples
• digital photographs, digital video, magnetic
  resonance images, computer tomography images,
  hyperspectral images, satellite images,
  microscope images, infrared images, radar images.
• Applications
• machine vision in semiconductor industry,
  precision farming, plant biology, medicine,
  automobile industry, database retrieval,
  surveillance, biometrics for identification,
  topography.

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Futures Proposed Research Projects

• Interactive Evolutionary Computation Fusion of
  the Capabilities of Evolutionary Computation
  Optimization and Human Evaluation (IGAs, HBGAs)
• A Visual Metaphor for Knowledge Discovery in
  Databases Visualization within the Knowledge
  Discovery in Databases process
• Network Security intrusion detection using
  network flow data, system log data, etc
• Reveal Automated Information Collection,
  Archival, Analysis, and Visualization
• Monitoring Computer Desktop Activity in a Secure
  Environment
• Integrating MEMS, Electro-Optical, Infrared, and
  Hyperspectral Sensor Data for Security
  Applications
• Bio-Terrorism Outbreak Detection System

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Summary

• Broad Range Of Data Mining Expertise
• 17 Full-time Staff, 10 Adjunct Researchers, and
  35 Industrial, Government, and Academic
  Technology/Application Stakeholders
• Active Summer REU Program
• Industrial and Government Investment Into
  Continuing Software Development Efforts
• Reoccurring Research Dollars
• Vehicle For Integrating Specific Industrial,
  Governmental, and Academic Activities Into the
  Automated Learning Group