Aviation Multivariate Time Series for Subsequence Search Algorithm Testing

Two large Multivariate Time-Series (MTS) databases from the aviation domain, each containing several millions of observations, are used to benchmark search algorithms. The data was provided by the National Aeronautics and Space Administration and the associated paper was last updated on 2026-04-10. The research focuses on a flexible subsequence search framework that enables querying on any subset of variables with arbitrary time delays.

Use Cases

• Benchmarking time series subsequence search algorithms based on the described R-tree and List Based Search methods.
• Testing flexible querying on variable subsets and time delays as mentioned in the algorithmic framework.
• Evaluating algorithm prune rates on large-scale sensor data, referencing the >95% prune rate reported.
• Researching pattern discovery in high-volume sensor recordings from aerospace systems.

Strengths

• Contains several millions of observations per database, indicating a large scale.
• Algorithm tests demonstrated high prune rates (>95%), suggesting efficient indexing for the data structure.
• Data originates from the aviation domain via the National Aeronautics and Space Administration.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The primary file format is APPLICATION/X-PDF, which may contain a paper rather than the raw data files.

Provenance

Source

National Aeronautics and Space Administration

Collection Method

Likely sensor recordings from aerospace systems, as mentioned in the description.

Time Range

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Freshness

Last updated 2026-04-10 19:41:50.558164; freshness should be verified.

Geography

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