ConfSeer: Leveraging Customer Support Knowledge Bases for Automated Misconfiguration Detection
Summary: ConfSeer mines natural-language customer-support KBs to detect configuration deviations and recommend fixes, addressing high-dimensional parameter matching via NLP, IR, and interactive learning. Deployed on tens of thousands of servers, it achieves 80–97.5% accuracy with low overhead.
(summarized by gpt-5.6-luna on Jul 24 2026)
@article{potharaju_vldb15,
title = {{ConfSeer: Leveraging Customer Support Knowledge Bases for Automated Misconfiguration Detection}},
author = {Potharaju, Rahul and Chan, Joseph and Hu, Luhui and Nita-Rotaru, Cristina and Wang, Mingshi and Zhang, Liyuan and Jain, Navendu},
journal = {PVLDB},
series = {{VLDB} '15},
volume = {8},
number = {12},
pages = {1828--1839},
doi = {10.14778/2824032.2824036},
url = {https://doi.org/10.14778/2824032.2824036},
year = {2015}
}
Incoming Citations (Sorted by Pagerank)
Showing 0 of 0 citing papers.
Rank
Citing Paper
Year
Venue
Pagerank
PreviousPage 1 / 1Next
Outgoing Citations (Sorted by Pagerank)
Showing 11 of 11 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.