{"@type": "dcat:Dataset", "accessLevel": "public", "accrualPeriodicity": "irregular", "bureauCode": ["026:00"], "contactPoint": {"@type": "vcard:Contact", "fn": "Dennis Koga", "hasEmail": "mailto:dennis.koga@nasa.gov"}, "description": "The 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.", "distribution": [{"@type": "dcat:Distribution", "downloadURL": "http://ti.arc.nasa.gov/m/opensource/downloads/SequenceMiner.tar.gz", "format": "TAR", "mediaType": "application/x-tar"}], "identifier": "OCIO-Fitara-137", "issued": "2015-01-07", "keyword": ["algorithm", "cluster", "detection", "lcs", "longest-common-sequence", "outlier", "sequenceminer"], "modified": "2025-07-14", "programCode": ["026:046"], "publisher": {"@type": "org:Organization", "name": "Ames Research Center"}, "theme": ["Management/Operations"], "title": "ARC Code TI: sequenceMiner"}