Welcome to AIOps@NKU lab!
Our lab mainly focuses on artificial intelligence operations (AIOps), which applies AI methods, including traditional machine learning methods (e.g., decision tree, SVM, logistic regression), deep learning methods (e.g., Variational Autoencoder, LSTM, CNN), reinforcement learning methods (e.g., MCTS), knowledge graph methods, graph computing methods, to analyze the application-level and machine-level data of Internet-based services or Web-based services (say search engine, online shopping, online video, instant messaging). We conduct anomaly detection, root cause analysis, failure localization, failure prediction, etc., on the large volume of data to keep services reliable and improve user experience. We are now collaborating with Alibaba, Baidu, Huawei, Bytedance, Huya, and CERNET. Thus the data of these companies can be used to evaluate and optimize the model proposed by us.
We welcome more undergraduate students to join our lab. You can use real-world data to prove your idea and deploy your model to world-class companies to improve the experience of millions of users! We will recommend you to top global Internet/IT companies such as MSRA, Alibaba, Baidu, Tencent, Bytedance, Huawei, Huya, etc.
Our lab is organized by Dr. Shenglin Zhang and Dr. Yongqian sun. We have published a series of papers, which can be found here. In addition, here are the courses taught by Dr. Shenglin Zhang and Dr. Yongqian Sun.
- Dr. Shenglin Zhang will serve as the TPC member of WWW 2022. Welcome to submit your work to WWW 2022!
- Our paper “Jump-Starting Multivariate Time Series Anomaly Detection for Online Service Systems” is accepted by USENIX ATC (CCF A). Congratulations!
- Our paper, “Detecting Outlier Machine Instances through Gaussian Mixture Variational Autoencoder with One Dimensional CNN”, is accepted by IEEE Transactions on Computers (CCF A). Congratulations!
- Dr. Shenglin Zhang will serve as the TPC member of ISSRE 2021. Welcome to submit your work to ISSRE 2021!
- Our paper “LogClass: Anomalous Log Identification andClassification with Partial Labels” is accepted by IEEE TNSM.
- Our papers “Cross-System Log Anomaly Detection for Software Systems” and “Unsupervised Detection of Microservice Trace Anomalies through Service-Level Deep Bayesian Networks” are accepted by IEEE ISSRE 2020.
- Our paper, “Localizing Failure Root Causes in a Microservice through Causality Inference”, is accepted by IEEE/ACM IWQoS 2020.
- Our paper, “Diagnosing Root Causes of Intermittent Slow Queries in Cloud Databases”, is accepted by VLDB 2020.
- Our paper, “Efficient and Robust Syslog Parsing for Network Devices in Datacenter Networks”, is accepted by IEEE Access (JCR Zone 2). Congratulations!
- Our work is reported by Nature Research in the article entitled “Finding order in a swirl of information”, which introduces College of Software, Nankai University.
- Our paper, ” LogAnomaly: Unsupervised Detection of Sequential and Quantitative Anomalies in Unstructured Logs”, is accepted by IJCAI 2019 (CCF A). Congratulations to Ms. Yuqing Liu, who is in her junior year at College of Software, Nankai University now.
- Our paper, “Causal Analysis of the Unsatisfying Experience in Realtime Mobile Multiplayer Games in the Wild”, is accepted by IEEE ICME 2019 (CCF B). Congratulations!
- Our paper, “Robust and Rapid Adaption for Concept Drift in Software System Anomaly Detection”, of which Dr. Shenglin Zhang is the corresponding author, win the “Best Research Paper Award” at IEEE ISSRE conference (CCF B). Congratulations!