sequenceMiner algorithm

Detecting and describing anomalies in large repositories of discrete symbol sequences.

sequenceMiner has been open-sourced! Download the file below to try it out.

sequenceMiner was developed to address the problem of detecting and describing anomalies in large sets of high-dimensional symbol sequences. sequenceMiner works by performing unsupervised clustering (grouping) of sequences using the normalized longest common subsequence (LCS) as a similarity measure, followed by a detailed analysis of outliers to detect anomalies. sequenceMiner utilizes a new hybrid algorithm for computing the LCS that has been shown to outperform existing algorithms by a factor of five.

sequenceMiner also includes new algorithms for outlier analysis that provide comprehensible indicators as to why a particular sequence was deemed to be an outlier. This provides analysts with a coherent description of the anomalies identified in the sequence, and why they differ from more “normal” sequences.

sequenceMiner was developed with funding from the NASA Aviation Safety Program. In the commercial aviation domain, sequenceMiner can be used to discover atypical behavior in airline performance data that may have possible operational significance for safety analysts. But because the sequenceMiner approach is general and not restricted in any way to a domain, and these algorithms can be applied in other fields where anomaly detection and event mining would be useful.

Data and Resources

Field Value
accessLevel public
accrualPeriodicity irregular
bureauCode {026:00}
catalog_@context https://project-open-data.cio.gov/v1.1/schema/catalog.jsonld
catalog_@id https://data.nasa.gov/data.json
catalog_conformsTo https://project-open-data.cio.gov/v1.1/schema
catalog_describedBy https://project-open-data.cio.gov/v1.1/schema/catalog.json
identifier DASHLINK_115
issued 2010-09-10
landingPage https://c3.nasa.gov/dashlink/resources/115/
modified 2020-01-29
programCode {026:029}
publisher Dashlink
resource-type Dataset
source_datajson_identifier true
source_hash 2c2f1cfdd43adac7e0d87586f86ac4405c4753eb
source_schema_version 1.1
Groups
  • AmeriGEOSS
  • National Provider
  • North America
Tags
  • amerigeo
  • amerigeoss
  • ames
  • ckan
  • dashlink
  • geo
  • geoss
  • nasa
  • national
  • north-america
  • united-states
isopen False
license_id notspecified
license_title License not specified
maintainer Suratna Budalakoti
maintainer_email suratna_b@yahoo.com
metadata_created 2025-11-22T02:42:34.408478
metadata_modified 2025-11-22T02:42:34.408482
notes Detecting and describing anomalies in large repositories of discrete symbol sequences. **sequenceMiner has been open-sourced! Download the file below to try it out.** sequenceMiner was developed to address the problem of detecting and describing anomalies in large sets of high-dimensional symbol sequences. sequenceMiner works by performing unsupervised clustering (grouping) of sequences using the normalized longest common subsequence (LCS) as a similarity measure, followed by a detailed analysis of outliers to detect anomalies. sequenceMiner utilizes a new hybrid algorithm for computing the LCS that has been shown to outperform existing algorithms by a factor of five. sequenceMiner also includes new algorithms for outlier analysis that provide comprehensible indicators as to why a particular sequence was deemed to be an outlier. This provides analysts with a coherent description of the anomalies identified in the sequence, and why they differ from more “normal” sequences. sequenceMiner was developed with funding from the NASA Aviation Safety Program. In the commercial aviation domain, sequenceMiner can be used to discover atypical behavior in airline performance data that may have possible operational significance for safety analysts. But because the sequenceMiner approach is general and not restricted in any way to a domain, and these algorithms can be applied in other fields where anomaly detection and event mining would be useful.
num_resources 5
num_tags 11
title sequenceMiner algorithm