Overview

The Intelligent Data Understanding (IDU) Group collaborates with domain scientists in the fields of Integrated System Health Management, Aeronautics, Space Exploration, Earth Sciences and Space Sciences to answer pressing scientific questions in the field of machine learning, knowledge discovery, and related areas. We also do fundamental research to create tools and methods to aid in the assimilation and understanding of scientific and engineering data to best advance NASA's missions.

Technical Overviews

Technical Overviews
Orca: A Program for Mining Distance-Based Outliers
The Inductive Monitoring System (IMS) (PDF)
Recurring Anomaly Detection System (PDF)
Using sequenceMiner to Discover Anomalous Flights (PDF)
Intelligent Data Understanding for Earth and Space Science (PDF)
Probability Collectives for Science and Engineering (PDF)
Intelligent Data Understanding for Integrated Systems Health Management (PDF)

SIAM Text Mining Contest

Project List (Active)

DASHlink

DASHlink is a virtual laboratory for scientists and engineers to disseminate results and collaborate on research problems in health management technologies for aeronautics systems.

+ Visit DASHlink

Detecting Recurring Anomalies in Text Reports

Project Lead: Dawn McIntosh The Recurring Anomaly Detection System (ReADS) team is developing a family of novel methods to mine text documents and identify recurring anomalies across reports. ReADS analyzes text reports, such as aviation reports and problem or maintenance records, uses text clustering algorithms to group loosely related reports and documents, and identifies interconnected reports. The tool provides a visualization of the clusters and recurring anomalies, and has been integrated into a secure web-based search platform to allow users to perform their own text mining.

+ Visit Detecting Recurring Anomalies in Text Reports

Inductive Monitoring System

Project Lead: David L Iverson The Inductive Monitoring System (IMS) software was developed to provide a technique to automatically produce health monitoring knowledge bases for systems that are either difficult to model (simulate) with a computer or which require computer models that are too complex to use for real time monitoring.
Integrated Vehicle Health Management

Project Lead: Ashok N. Srivastava, Ph.D.
Liquid Propulsion System Health Management

Project Lead: Ashok N. Srivastava, Ph.D. Data mining researchers at NASA Ames Research Center's IDU Group are working with rocket propulsion experts at other NASA centers and at Pratt & Whitney Rocketdyne to apply data mining algorithms to historical data from the Space Shuttle Main Engine (SSME) for real time prognostics and diagnostics.

+ Visit Liquid Propulsion System Health Management

Mixture Density Mercer Kernels

Project Lead: Ashok N. Srivastava, Ph.D. A method of generating Mercer Kernels from an ensemble of probabilistic mixture models, where each mixture model is generated from a Bayesian mixture density estimate.
Modeling Spatial and Temporal Covariability using machine learni

Project Lead: Ashok N. Srivastava, Ph.D. Use self-organizing map (SOM) neural networks to identify year-to-year variability of terrestrial ecosystems associated with fluctuation in global circulation and climate.
Orca: A Program for Mining Distance-Based Outliers

Project Lead: Mark Schwabacher, Ph.D. Orca mines distance-based outliers. That is, Orca uses the distance from a given example to its nearest neighbors to determine its unusualness.
Probability Collectives

Project Lead: David Wolpert, Ph.D. Unify game theory and statistical physics via the mathematics of information theory, with applications in distributed control for multi-agent systems.

+ Visit Probability Collectives

Using sequenceMiner to Discover Anomalous Flights

Project Lead: Ashok N. Srivastava, Ph.D. An approach to model the behavior of discrete switch sequences in an aircraft using flight data, in order to discover atypical behavior of possible operational significance.
Virtual Sensors for Earth Science

Project Lead: Ashok N. Srivastava, Ph.D. Methods of predicting missing remote sensing spectra using other relevant data: Virtual Sensors uses models trained on spectrally rich data to "fill in" unmeasured spectral channels in spectrally poor data, thereby extracting more information from spectrally poor data.

+ Visit Virtual Sensors for Earth Science

Virtual Sensors for Space Science

Project Lead: Ashok N. Srivastava, Ph.D.

+ Visit Virtual Sensors for Space Science