{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T21:45:02Z","timestamp":1778535902876,"version":"3.51.4"},"reference-count":14,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2022,8]]},"abstract":"<jats:p>Anomaly detection is a critical task in applications like preventing financial fraud, system malfunctions, and cybersecurity attacks. While previous research has offered a plethora of anomaly detection algorithms, effective anomaly detection remains challenging for users due to the tedious manual tuning process. Currently, model developers must determine which of these numerous algorithms is best suited for their particular domain and then must tune many parameters by hand to make the chosen algorithm perform well. This demonstration showcases AutoOD, the first unsupervised self-tuning anomaly detection system which frees users from this tedious manual tuning process. AutoOD outperforms the best un-supervised anomaly detection methods it deploys, with its performance similar to those of supervised anomaly classification models, yet without requiring ground truth labels. Our easy-to-use visual interface allows users to gain insights into AutoOD's self-tuning process and explore the underlying patterns within their datasets.<\/jats:p>","DOI":"10.14778\/3554821.3554880","type":"journal-article","created":{"date-parts":[[2022,9,29]],"date-time":"2022-09-29T22:28:39Z","timestamp":1664490519000},"page":"3706-3709","source":"Crossref","is-referenced-by-count":9,"title":["A demonstration of AutoOD"],"prefix":"10.14778","volume":"15","author":[{"given":"Dennis","family":"Hofmann","sequence":"first","affiliation":[{"name":"Worcester Polytechnic Institute"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peter","family":"VanNostrand","sequence":"additional","affiliation":[{"name":"Worcester Polytechnic Institute"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huayi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Worcester Polytechnic Institute"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yizhou","family":"Yan","sequence":"additional","affiliation":[{"name":"Meta"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Cao","sequence":"additional","affiliation":[{"name":"Massachusetts Institute of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Samuel","family":"Madden","sequence":"additional","affiliation":[{"name":"Massachusetts Institute of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elke","family":"Rundensteiner","sequence":"additional","affiliation":[{"name":"Worcester Polytechnic Institute"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,9,29]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-47578-3"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3035918.3035928"},{"key":"e_1_2_1_3_1","unstructured":"Vic Barnett Toby Lewis etal 1994. Outliers in statistical data. Vol. 3. Wiley New York.  Vic Barnett Toby Lewis et al. 1994. Outliers in statistical data. Vol. 3. Wiley New York."},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/342009.335388"},{"key":"e_1_2_1_5_1","volume-title":"Multi-Tactic Distance-Based Outlier Detection","author":"Cao Lei","unstructured":"Lei Cao , Yizhou Yan , Caitlin Kuhlman , Qingyang Wang , Elke A Rundensteiner , and Mohamed Eltabakh . 2017. Multi-Tactic Distance-Based Outlier Detection . In ICDE. IEEE , 959--970. Lei Cao, Yizhou Yan, Caitlin Kuhlman, Qingyang Wang, Elke A Rundensteiner, and Mohamed Eltabakh. 2017. Multi-Tactic Distance-Based Outlier Detection. In ICDE. IEEE, 959--970."},{"key":"e_1_2_1_6_1","unstructured":"Dheeru Dua and Casey Graff. 2017. UCI Machine Learning Repository. http:\/\/archive.ics.uci.edu\/ml visited on 07\/05\/2022.  Dheeru Dua and Casey Graff. 2017. 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