{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/anomaly-detection-in-multivariate-non","title":"Anomaly Detection in Multivariate Non-stationary Time Series for Automatic DBMS Diagnosis","arxiv_id":"1708.02635","date":"2017-08-08","proceeding":null,"authors":["Doyup Lee"],"abstract":"Anomaly detection in database management systems (DBMSs) is difficult because\nof increasing number of statistics (stat) and event metrics in big data system.\nIn this paper, I propose an automatic DBMS diagnosis system that detects\nanomaly periods with abnormal DB stat metrics and finds causal events in the\nperiods. Reconstruction error from deep autoencoder and statistical process\ncontrol approach are applied to detect time period with anomalies. Related\nevents are found using time series similarity measures between events and\nabnormal stat metrics. After training deep autoencoder with DBMS metric data,\nefficacy of anomaly detection is investigated from other DBMSs containing\nanomalies. Experiment results show effectiveness of proposed model, especially,\nbatch temporal normalization layer. Proposed model is used for publishing\nautomatic DBMS diagnosis reports in order to determine DBMS configuration and\nSQL tuning.","url_abs":"http://arxiv.org/abs/1708.02635v2","url_pdf":"http://arxiv.org/pdf/1708.02635v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"anomaly-detection-in-multivariate-non","repo_url":"https://github.com/leedoyup/anogan-tf","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"},{"task_slug":"management","task_name":"Management"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}