Fetching the paper…
Reading the bibliography…
Effective root cause analysis (RCA) is vital for swiftly restoring services, minimizing losses, and ensuring the smooth operation and management of complex systems.
Cross correlation
Paul Bourke. 1996 · 1996
Earlier work this paper cites.
Vector autoregressions
James H Stock and Mark W Watson. 2001 · 2001
Earlier work this paper cites.
Causation, Prediction, and Search
Tom Burr. 2003 · 2003
Earlier work this paper cites.
Fast Random Walk with Restart and Its Applications. In Proceedings of the 6th IEEE International Conference on Data Mining (ICDM 2006), 18-22 December 2006, Hong Kong, China . IEEE Computer Society, 613–622
Hanghang Tong, Christos Faloutsos, and Jia-Yu Pan. 2006 · 2006
Earlier work this paper cites.
Principal component analysis
Hervé Abdi and Lynne J Williams. 2010 · 2010
Earlier work this paper cites.
A Survey on Multi-view Learning
Chang Xu, Dacheng Tao, and Chao Xu. 2013 · 2013
Earlier work this paper cites.
Constraint-based Causal Discovery: Conflict Resolution with Answer Set Programming. In Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence, UAI 2014, Quebec City, Quebec, Canada, July 23-27, 2014 , Nevin L. Zhang and Jin Tian (Eds.). AUAI Press, 340–349
Antti Hyttinen, Frederick Eberhardt, and Matti Järvisalo. 2014 · 2014
Earlier work this paper cites.
Fault detection analysis using data mining techniques for a cluster of smart office buildings
Alfonso Capozzoli, Fiorella Lauro, and Imran Khan. 2015 · 2015
Earlier work this paper cites.
Identification of time-dependent causal model: A gaussian process treatment. In Twenty-Fourth international joint conference on artificial intelligence
Biwei Huang, Kun Zhang, and Bernhard Schölkopf. 2015 · 2015
Earlier work this paper cites.
Constraint-based causal discovery from multiple interventions over overlapping variable sets
Sofia Triantafillou and Ioannis Tsamardinos. 2015 · 2015
Earlier work this paper cites.
Deep Multimodal Hashing with Orthogonal Regularization. In Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI 2015, Buenos Aires, Argentina, July 25-31, 2015 , Qiang Yang and Michael J. Wooldridge (Eds.). AAAI Press, 2291–2297
Daixin Wang, Peng Cui, Mingdong Ou, and Wenwu Zhu. 2015 · 2015
Earlier work this paper cites.
Ranking causal anomalies via temporal and dynamical analysis on vanishing correlations. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . 805–814
Wei Cheng, Kai Zhang, Haifeng Chen, Guofei Jiang, Zhengzhang Chen, and Wei Wang. 2016 · 2016
Earlier work this paper cites.
Efficient discovery of abnormal event sequences in enterprise security systems. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management . 707–715
Boxiang Dong, Zhengzhang Chen, Hui Wang, Lu-An Tang, Kai Zhang, Ying Lin, Zhichun Li, and Haifeng Chen. 2017 · 2017
Earlier work this paper cites.
DeepLog: Anomaly Detection and Diagnosis from System Logs through Deep Learning. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, CCS 2017, Dallas, TX, USA, October 30 - November 03, 2017 , Bhavani Thuraisingham, David Evans, Tal Malkin, and Dongyan Xu (Eds.). ACM, 1285–1298
Min Du, Feifei Li, Guineng Zheng, and Vivek Srikumar. 2017 · 2017
Earlier work this paper cites.
Inductive Representation Learning on Large Graphs. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA . 1024–1034
William L. Hamilton, Zhitao Ying, and Jure Leskovec. 2017 · 2017
Earlier work this paper cites.
Semi-Supervised Classification with Graph Convolutional Networks. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings . OpenReview.net
Thomas N. Kipf and Max Welling. 2017 · 2017
Earlier work this paper cites.
Identification and estimation of non-Gaussian structural vector autoregressions
Markku Lanne, Mika Meitz, and Pentti Saikkonen. 2017 · 2017
Earlier work this paper cites.
Multi-modal Summarization for Asynchronous Collection of Text, Image, Audio and Video. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017, Copenhagen, Denmark, September 9-11, 2017 , Martha Palmer, Rebecca Hwa, and Sebastian Riedel (Eds.). Association for Computational Linguistics, 1092–1102
Haoran Li, Junnan Zhu, Cong Ma, Jiajun Zhang, and Chengqing Zong. 2017 · 2017
Earlier work this paper cites.
Log-based Abnormal Task Detection and Root Cause Analysis for Spark. In 2017 IEEE International Conference on Web Services, ICWS 2017, Honolulu, HI, USA, June 25-30, 2017 . IEEE, 389–396
Siyang Lu, BingBing Rao, Xiang Wei, Byung-Chul Tak, Long Wang, and Liqiang Wang. 2017 · 2017
Earlier work this paper cites.
Elements of causal inference: foundations and learning algorithms
Jonas Peters, Dominik Janzing, and Bernhard Schölkopf. 2017 · 2017
Earlier work this paper cites.
Survey on Models and Techniques for Root-Cause Analysis
Marc Solé, Victor Muntés-Mulero, Annie Ibrahim Rana, and Giovani Estrada. 2017 · 2017
Cited alongside, same era.
Contextual Inter-modal Attention for Multi-modal Sentiment Analysis. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, October 31 - November 4, 2018 , Ellen Riloff, David Chiang, Julia Hockenmaier, and Jun’ichi Tsujii (Eds.). Association for Computational Linguistics, 3454–3466
Deepanway Ghosal, Md. Shad Akhtar, Dushyant Singh Chauhan, Soujanya Poria, Asif Ekbal, and Pushpak Bhattacharyya. 2018 · 2018
Cited alongside, same era.
Learning Joint Embedding with Multimodal Cues for Cross-Modal Video-Text Retrieval. In Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval, ICMR 2018, Yokohama, Japan, June 11-14, 2018 , Kiyoharu Aizawa, Michael S. Lew, and Shin’ichi Satoh (Eds.). ACM, 19–27
Niluthpol Chowdhury Mithun, Juncheng Li, Florian Metze, and Amit K. Roy-Chowdhury. 2018 · 2018
Cited alongside, same era.
Deep multi-view learning methods: A review
Xiaoqiang Yan, Shizhe Hu, Yiqiao Mao, Yangdong Ye, and Hui Yu. 2021 · 2021
Later among the works it cites.
Deep Co-Attention Network for Multi-View Subspace Learning. In WWW ’21: The Web Conference 2021, Virtual Event / Ljubljana, Slovenia, April 19-23, 2021 , Jure Leskovec, Marko Grobelnik, Marc Najork, Jie Tang, and Leila Zia (Eds.). ACM / IW3C2, 1528–1539
Lecheng Zheng, Yu Cheng, Hongxia Yang, Nan Cao, and Jingrui He. 2021a · 2021
Later among the works it cites.
Deeper-GXX: deepening arbitrary GNNs
Lecheng Zheng, Dongqi Fu, Ross Maciejewski, and Jingrui He. 2021b · 2021
Later among the works it cites.
Heterogeneous Contrastive Learning
Lecheng Zheng, Yada Zhu, Jingrui He, and Jinjun Xiong. 2021c · 2021
Later among the works it cites.
Root Cause Analysis of Failures in Microservices through Causal Discovery. In NeurIPS
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Graph Attention Networks. In 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings . OpenReview.net
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018 · 2018
Cited alongside, same era.
Dags with no tears: Continuous optimization for structure learning
Xun Zheng, Bryon Aragam, Pradeep K Ravikumar, and Eric P Xing. 2018 · 2018
Cited alongside, same era.
Root cause analysis on corrosive potential-induced degradation effects at the rear side of bifacial silicon PERC solar cells
Kai Sporleder, Volker Naumann, Jan Bauer, Susanne Richter, Angelika Hähnel, Stephan Großer, Marko Turek, and Christian Hagendorf. 2019 · 2019
Cited alongside, same era.
Attentional heterogeneous graph neural network: Application to program reidentification. In Proceedings of the 2019 SIAM International Conference on Data Mining . SIAM, 693–701
Shen Wang, Zhengzhang Chen, Ding Li, Zhichun Li, Lu-An Tang, Jingchao Ni, Junghwan Rhee, Haifeng Chen, and Philip S Yu. 2019 · 2019
Cited alongside, same era.
Deep Multimodality Model for Multi-task Multi-view Learning. In Proceedings of the 2019 SIAM International Conference on Data Mining, SDM 2019, Calgary, Alberta, Canada, May 2-4, 2019 , Tanya Y. Berger-Wolf and Nitesh V. Chawla (Eds.). SIAM, 10–18
Lecheng Zheng, Yu Cheng, and Jingrui He. 2019 · 2019
Cited alongside, same era.
Anomalous event sequence detection
Boxiang Dong, Zhengzhang Chen, Lu-An Tang, Haifeng Chen, Hui Wang, Kai Zhang, Ying Lin, and Zhichun Li. 2020 · 2020
Cited alongside, same era.
Root cause analysis approach based on reverse cascading decomposition in QFD and fuzzy weight ARM for quality accidents
Panting Duan, Zhenzhen He, Yihai He, Fengdi Liu, Anqi Zhang, and Di Zhou. 2020 · 2020
Cited alongside, same era.
Multi-modal Transformer for Video Retrieval. In Computer Vision - ECCV 2020 - 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part IV (Lecture Notes in Computer Science, Vol. 12349) , Andrea Vedaldi, Horst Bischof, Thomas Brox, and Jan-Michael Frahm (Eds.). Springer, 214–229
Valentin Gabeur, Chen Sun, Karteek Alahari, and Cordelia Schmid. 2020 · 2020
Cited alongside, same era.
Localizing Failure Root Causes in a Microservice through Causality Inference. In 28th IEEE/ACM International Symposium on Quality of Service, IWQoS 2020, Hangzhou, China, June 15-17, 2020 . IEEE, 1–10
Yuan Meng, Shenglin Zhang, Yongqian Sun, Ruru Zhang, Zhilong Hu, Yiyin Zhang, Chenyang Jia, Zhaogang Wang, and Dan Pei. 2020 · 2020
Cited alongside, same era.
Azam Ikram, Sarthak Chakraborty, Subrata Mitra, Shiv Kumar Saini, Saurabh Bagchi, and Murat Kocaoglu. 2022 · 2022
Later among the works it cites.
Causal Inference-Based Root Cause Analysis for Online Service Systems with Intervention Recognition. In KDD ’22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14 - 18, 2022 , Aidong Zhang and Huzefa Rangwala (Eds.). ACM, 3230–3240
Mingjie Li, Zeyan Li, Kanglin Yin, Xiaohui Nie, Wenchi Zhang, Kaixin Sui, and Dan Pei. 2022 · 2022
Later among the works it cites.
FLAVA: A Foundational Language And Vision Alignment Model. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, LA, USA, June 18-24, 2022 . IEEE, 15617–15629
Amanpreet Singh, Ronghang Hu, Vedanuj Goswami, Guillaume Couairon, Wojciech Galuba, Marcus Rohrbach, and Douwe Kiela. 2022 · 2022
Later among the works it cites.
Neural Granger Causality
Alex Tank, Ian Covert, Nicholas J. Foti, Ali Shojaie, and Emily B. Fox. 2022 · 2022
Later among the works it cites.
CAT: Beyond Efficient Transformer for Content-Aware Anomaly Detection in Event Sequences. In KDD ’22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14 - 18, 2022 , Aidong Zhang and Huzefa Rangwala (Eds.). ACM, 4541–4550
Shengming Zhang, Yanchi Liu, Xuchao Zhang, Wei Cheng, Haifeng Chen, and Hui Xiong. 2022 · 2022
Later among the works it cites.
Contrastive Learning with Complex Heterogeneity. In KDD ’22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14 - 18, 2022 , Aidong Zhang and Huzefa Rangwala (Eds.). ACM, 2594–2604
Lecheng Zheng, Jinjun Xiong, Yada Zhu, and Jingrui He. 2022 · 2022
Later among the works it cites.
Survey and Evaluation of Causal Discovery Methods for Time Series (Extended Abstract). In Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, IJCAI 2023, 19th-25th August 2023, Macao, SAR, China . ijcai.org, 6839–6844
Charles K. Assaad, Emilie Devijver, and Éric Gaussier. 2023 · 2023
Later among the works it cites.
MaPLe: Multi-modal Prompt Learning. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023, Vancouver, BC, Canada, June 17-24, 2023 . IEEE, 19113–19122
Muhammad Uzair Khattak, Hanoona Abdul Rasheed, Muhammad Maaz, Salman H. Khan, and Fahad Shahbaz Khan. 2023 · 2023
Later among the works it cites.
Domain specialization as the key to make large language models disruptive: A comprehensive survey
Chen Ling, Xujiang Zhao, Jiaying Lu, Chengyuan Deng, Can Zheng, Junxiang Wang, Tanmoy Chowdhury, Yun Li, Hejie Cui, Tianjiao Zhao, et al · 2023
Later among the works it cites.
UNIFIED-IO: A Unified Model for Vision, Language, and Multi-modal Tasks. In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023 . OpenReview.net
Jiasen Lu, Christopher Clark, Rowan Zellers, Roozbeh Mottaghi, and Aniruddha Kembhavi. 2023 · 2023
Later among the works it cites.
Anomaly Detection and Failure Root Cause Analysis in (Micro) Service-Based Cloud Applications: A Survey
Jacopo Soldani and Antonio Brogi. 2023 · 2023
Later among the works it cites.
Hierarchical graph neural networks for causal discovery and root cause localization
Dongjie Wang, Zhengzhang Chen, Jingchao Ni, Liang Tong, Zheng Wang, Yanjie Fu, and Haifeng Chen. 2023b · 2023
Later among the works it cites.
Interdependent Causal Networks for Root Cause Localization. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2023, Long Beach, CA, USA, August 6-10, 2023 . ACM, 5051–5060
Dongjie Wang, Zhengzhang Chen, Jingchao Ni, Liang Tong, Zheng Wang, Yanjie Fu, and Haifeng Chen. 2023c · 2023
Later among the works it cites.
Root Cause Analysis for Microservice Systems via Hierarchical Reinforcement Learning from Human Feedback. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2023, Long Beach, CA, USA, August 6-10, 2023 . ACM, 5116–5125
Lu Wang, Chaoyun Zhang, Ruomeng Ding, Yong Xu, Qihang Chen, Wentao Zou, Qingjun Chen, Meng Zhang, Xuedong Gao, Hao Fan, Saravan Rajmohan, Qingwei Lin, and Dongmei Zhang. 2023d · 2023
Later among the works it cites.
Nezha: Interpretable Fine-Grained Root Causes Analysis for Microservices on Multi-modal Observability Data
Guangba Yu, Pengfei Chen, Yufeng Li, Hongyang Chen, Xiaoyun Li, and Zibin Zheng. 2023 · 2023
Later among the works it cites.
Fairness-aware Multi-view Clustering. In Proceedings of the 2023 SIAM International Conference on Data Mining, SDM 2023, Minneapolis-St. Paul Twin Cities, MN, USA, April 27-29, 2023 , Shashi Shekhar, Zhi-Hua Zhou, Yao-Yi Chiang, and Gregor Stiglic (Eds.). SIAM, 856–864
Lecheng Zheng, Yada Zhu, and Jingrui He. 2023 · 2023
Later among the works it cites.