Arrays of (locality-sensitive) Count Estimators (ACE): High-Speed Anomaly Detection via Cache Lookups. Luo, C. & Shrivastava, A. arXiv:1706.06664 [cs, stat], June, 2017. arXiv: 1706.06664![Arrays of (locality-sensitive) Count Estimators (ACE): High-Speed Anomaly Detection via Cache Lookups [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper abstract bibtex Anomaly detection is one of the frequent and important subroutines deployed in large-scale data processing systems. Even being a well-studied topic, existing techniques for unsupervised anomaly detection require storing significant amounts of data, which is prohibitive from memory and latency perspective. In the big-data world existing methods fail to address the new set of memory and latency constraints. In this paper, we propose ACE (Arrays of (locality-sensitive) Count Estimators) algorithm that can be 60x faster than the ELKI package\textasciitilde\textbackslashcite\DBLP:conf/ssd/AchtertBKSZ09\, which has the fastest implementation of the unsupervised anomaly detection algorithms. ACE algorithm requires less than \$4MB\$ memory, to dynamically compress the full data information into a set of count arrays. These tiny \$4MB\$ arrays of counts are sufficient for unsupervised anomaly detection. At the core of the ACE algorithm, there is a novel statistical estimator which is derived from the sampling view of Locality Sensitive Hashing(LSH). This view is significantly different and efficient than the widely popular view of LSH for near-neighbor search. We show the superiority of ACE algorithm over 11 popular baselines on 3 benchmark datasets, including the KDD-Cup99 data which is the largest available benchmark comprising of more than half a million entries with ground truth anomaly labels.
@article{luo_arrays_2017,
title = {Arrays of (locality-sensitive) {Count} {Estimators} ({ACE}): {High}-{Speed} {Anomaly} {Detection} via {Cache} {Lookups}},
shorttitle = {Arrays of (locality-sensitive) {Count} {Estimators} ({ACE})},
url = {http://arxiv.org/abs/1706.06664},
abstract = {Anomaly detection is one of the frequent and important subroutines deployed in large-scale data processing systems. Even being a well-studied topic, existing techniques for unsupervised anomaly detection require storing significant amounts of data, which is prohibitive from memory and latency perspective. In the big-data world existing methods fail to address the new set of memory and latency constraints. In this paper, we propose ACE (Arrays of (locality-sensitive) Count Estimators) algorithm that can be 60x faster than the ELKI package{\textasciitilde}{\textbackslash}cite\{DBLP:conf/ssd/AchtertBKSZ09\}, which has the fastest implementation of the unsupervised anomaly detection algorithms. ACE algorithm requires less than \$4MB\$ memory, to dynamically compress the full data information into a set of count arrays. These tiny \$4MB\$ arrays of counts are sufficient for unsupervised anomaly detection. At the core of the ACE algorithm, there is a novel statistical estimator which is derived from the sampling view of Locality Sensitive Hashing(LSH). This view is significantly different and efficient than the widely popular view of LSH for near-neighbor search. We show the superiority of ACE algorithm over 11 popular baselines on 3 benchmark datasets, including the KDD-Cup99 data which is the largest available benchmark comprising of more than half a million entries with ground truth anomaly labels.},
urldate = {2019-03-23TZ},
journal = {arXiv:1706.06664 [cs, stat]},
author = {Luo, Chen and Shrivastava, Anshumali},
month = jun,
year = {2017},
note = {arXiv: 1706.06664},
keywords = {⛔ No DOI found}
}
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