{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,30]],"date-time":"2025-08-30T16:57:09Z","timestamp":1756573029502,"version":"3.38.0"},"reference-count":123,"publisher":"Association for Computing Machinery (ACM)","issue":"2","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2024,10]]},"abstract":"<jats:p>Causal inference can quantify cause-effect relationships in domains as varied as medicine, economics and public policy. Production computer systems exhibit a similar level of complexity and a recurring need to diagnose problems quickly. However, systems are only observed imperfectly, often via long, messy, semi-structured logs.<\/jats:p>\n          <jats:p>\n            In this work, we want to accelerate large systems debugging by applying causal inference over logs, enabling engineers to diagnose problems and assess interventions in a principled manner. Our framework achieves this through two human-in-the-loop modules: (1) The\n            <jats:bold>Candidate Cause Ranker<\/jats:bold>\n            , through which one can determine the causes of a variable without running a full causal discovery algorithm; and (2) the\n            <jats:bold>Interactive Causal Graph Refiner<\/jats:bold>\n            , which helps engineers compute an unbiased estimation of their effect of interest without extensive manual causal graph verification. Both modules are powered by the insight that only part of the causal graph of the system is needed to correctly quantify a given effect of interest. We also provide a data preparation pipeline, the\n            <jats:bold>Log Converter<\/jats:bold>\n            , which transforms raw, messy, real-world logs into an appropriate tabular input for causal inference, using methods drawn from data transformation, cleaning, and extraction.\n          <\/jats:p>\n          <jats:p>\n            We evaluate LOGos, a prototype implementation, on both real-world and synthetic logs and find that: (1) The\n            <jats:bold>Candidate Cause Ranker<\/jats:bold>\n            achieved an average precision 1.08\u00d7-18\u00d7 higher than the baselines, in interactive time; (2) The\n            <jats:bold>Interactive Causal Graph Refiner<\/jats:bold>\n            required a number of causal judgments 1.61 \u00d7 - 16.83\u00d7 lower than the baselines; and (3) The latency of\n            <jats:bold>Log Converter<\/jats:bold>\n            scaled linearly with three measures of the complexity of a log: length, distinct templates, and fraction of tokens that are variables.\n          <\/jats:p>","DOI":"10.14778\/3705829.3705836","type":"journal-article","created":{"date-parts":[[2025,2,28]],"date-time":"2025-02-28T23:21:06Z","timestamp":1740784866000},"page":"158-172","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["From Logs to Causal Inference: Diagnosing Large Systems"],"prefix":"10.14778","volume":"18","author":[{"given":"Markos","family":"Markakis","sequence":"first","affiliation":[{"name":"MIT CSAIL, Cambridge, MA"}]},{"given":"Brit","family":"Youngmann","sequence":"additional","affiliation":[{"name":"Technion, Haifa, Israel"}]},{"given":"Trinity","family":"Gao","sequence":"additional","affiliation":[{"name":"MIT CSAIL, Cambridge, MA"}]},{"given":"Ziyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"MIT CSAIL, Cambridge, MA"}]},{"given":"Rana","family":"Shahout","sequence":"additional","affiliation":[{"name":"Harvard University, Cambridge, MA"}]},{"given":"Peter Baile","family":"Chen","sequence":"additional","affiliation":[{"name":"MIT CSAIL, Cambridge, MA"}]},{"given":"Chunwei","family":"Liu","sequence":"additional","affiliation":[{"name":"MIT CSAIL, Cambridge, MA"}]},{"given":"Ibrahim","family":"Sabek","sequence":"additional","affiliation":[{"name":"University of Southern California, Los Angeles, CA"}]},{"given":"Michael","family":"Cafarella","sequence":"additional","affiliation":[{"name":"MIT CSAIL, Cambridge, MA"}]}],"member":"320","published-online":{"date-parts":[[2025,2,28]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Service-Oriented Computing - ICSOC 2020 Workshops. 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Explaining Image Classifiers Using Contrastive Counterfactuals in Generative Latent Spaces. arXiv:2206.05257 [cs.CV] https:\/\/arxiv.org\/abs\/2206.05257"},{"key":"e_1_2_1_4_1","volume-title":"CausalSim: A Causal Framework for Unbiased Trace-Driven Simulation. In 20th USENIX Symposium on Networked Systems Design and Implementation (NSDI 23)","author":"Alomar Abdullah","year":"2023","unstructured":"Abdullah Alomar, Pouya Hamadanian, Arash Nasr-Esfahany, Anish Agarwal, Mohammad Alizadeh, and Devavrat Shah. 2023. CausalSim: A Causal Framework for Unbiased Trace-Driven Simulation. In 20th USENIX Symposium on Networked Systems Design and Implementation (NSDI 23). USENIX Association, Boston, MA, 1115--1147. https:\/\/www.usenix.org\/conference\/nsdi23\/presentation\/alomar"},{"key":"e_1_2_1_5_1","unstructured":"Alessandro Antonucci Gregorio Piqu\u00e9 and Marco Zaffalon. 2023. Zero-shot Causal Graph Extrapolation from Text via LLMs. arXiv:2312.14670 [cs.AI] https:\/\/arxiv.org\/abs\/2312.14670"},{"key":"e_1_2_1_6_1","volume-title":"Proceedings of the 2016 ACM SIGCOMM Conference (SIGCOMM '16)","author":"Arzani Behnaz","year":"2016","unstructured":"Behnaz Arzani, Selim Ciraci, Boon Thau Loo, Assaf Schuster, and Geoff Outhred. 2016. Taking the Blame Game out of Data Centers Operations with NetPoirot. In Proceedings of the 2016 ACM SIGCOMM Conference (SIGCOMM '16). Association for Computing Machinery, New York, NY, USA, 440--453. 10.1145\/2934872.2934884"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","unstructured":"Nicolas Aussel Yohan Petetin and Sophie Chabridon. 2018. Improving Performances of Log Mining for Anomaly Prediction Through NLP-Based Log Parsing. In 2018 IEEE 26th International Symposium on Modeling Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS). IEEE Piscataway NJ USA 237--243. 10.1109\/MASCOTS.2018.00031","DOI":"10.1109\/MASCOTS.2018.00031"},{"key":"e_1_2_1_8_1","unstructured":"Taiyu Ban Lyuzhou Chen Derui Lyu Xiangyu Wang and Huanhuan Chen. 2023. Causal Structure Learning Supervised by Large Language Model. arXiv:2311.11689 [cs.AI] https:\/\/arxiv.org\/abs\/2311.11689"},{"key":"e_1_2_1_9_1","unstructured":"Taiyu Ban Lyvzhou Chen Xiangyu Wang and Huanhuan Chen. 2023. From Query Tools to Causal Architects: Harnessing Large Language Models for Advanced Causal Discovery from Data. arXiv:2306.16902 [cs.AI] https:\/\/arxiv.org\/abs\/2306.16902"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1137\/080716542"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1540-5907.2012.00626.x"},{"key":"e_1_2_1_12_1","first-page":"1","article-title":"DoWhy-GCM: An Extension of DoWhy for Causal Inference in Graphical Causal Models","volume":"25","author":"Bl\u00f6baum Patrick","year":"2024","unstructured":"Patrick Bl\u00f6baum, Peter G\u00f6tz, Kailash Budhathoki, Atalanti A. Mastakouri, and Dominik Janzing. 2024. DoWhy-GCM: An Extension of DoWhy for Causal Inference in Graphical Causal Models. Journal of Machine Learning Research 25, 147 (2024), 1--7. http:\/\/jmlr.org\/papers\/v25\/22-1258.html","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_2_1_13_1","volume-title":"Proceedings of the 39th International Conference on Machine Learning (Proceedings of Machine Learning Research)","author":"Budhathoki Kailash","unstructured":"Kailash Budhathoki, Lenon Minorics, Patrick Bloebaum, and Dominik Janzing. 2022. Causal structure-based root cause analysis of outliers. In Proceedings of the 39th International Conference on Machine Learning (Proceedings of Machine Learning Research), Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesvari, Gang Niu, and Sivan Sabato (Eds.), Vol. 162. PMLR, Cambridge, MA, USA, 2357--2369. https:\/\/proceedings.mlr.press\/v162\/budhathoki22a.html"},{"key":"e_1_2_1_14_1","doi-asserted-by":"crossref","unstructured":"Cristiano Calcagno Dino Distefano Jeremy Dubreil Dominik Gabi Pieter Hooimeijer Martino Luca Peter O'Hearn Irene Papakonstantinou Jim Purbrick and Dulma Rodriguez. 2015. Moving Fast with Software Verification. In NASA Formal Methods Klaus Havelund Gerard Holzmann and Rajeev Joshi (Eds.). Springer International Cham 3--11.","DOI":"10.1007\/978-3-319-17524-9_1"},{"key":"e_1_2_1_15_1","first-page":"507","article-title":"Optimal structure identification with greedy search","author":"Chickering David Maxwell","year":"2002","unstructured":"David Maxwell Chickering. 2002. Optimal structure identification with greedy search. Journal of machine learning research 3, Nov (2002), 507--554.","journal-title":"Journal of machine learning research 3"},{"key":"e_1_2_1_16_1","unstructured":"Tom Claassen Joris Mooij and Tom Heskes. 2013. Learning Sparse Causal Models is not NP-hard. arXiv:1309.6824 [cs.AI] https:\/\/arxiv.org\/abs\/1309.6824"},{"key":"e_1_2_1_17_1","doi-asserted-by":"crossref","first-page":"878","DOI":"10.1111\/joes.12217","article-title":"Causal inference on education policies: A survey of empirical studies using PISA","volume":"32","author":"Cordero Jos\u00e9 M","year":"2018","unstructured":"Jos\u00e9 M Cordero, V\u00edctor Crist\u00f3bal, and Daniel Sant\u00edn. 2018. Causal inference on education policies: A survey of empirical studies using PISA, TIMSS and PIRLS. Journal of Economic Surveys 32, 3 (2018), 878--915.","journal-title":"Journal of Economic Surveys"},{"key":"e_1_2_1_18_1","volume-title":"REPT: Reverse Debugging of Failures in Deployed Software. In 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18)","author":"Cui Weidong","year":"2018","unstructured":"Weidong Cui, Xinyang Ge, Baris Kasikci, Ben Niu, Upamanyu Sharma, Ruoyu Wang, and Insu Yun. 2018. REPT: Reverse Debugging of Failures in Deployed Software. In 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18). USENIX Association, Carlsbad, CA, 17--32. https:\/\/www.usenix.org\/conference\/osdi18\/presentation\/weidong"},{"key":"e_1_2_1_19_1","first-page":"879","article-title":"Logram: Efficient Log Parsing Using n n-Gram Dictionaries","volume":"48","author":"Dai Hetong","year":"2020","unstructured":"Hetong Dai, Heng Li, Che-Shao Chen, Weiyi Shang, and Tse-Hsun Chen. 2020. Logram: Efficient Log Parsing Using n n-Gram Dictionaries. IEEE Transactions on Software Engineering 48, 3 (2020), 879--892.","journal-title":"IEEE Transactions on Software Engineering"},{"key":"e_1_2_1_20_1","volume-title":"IEEE 25th International Symposium on Software Reliability Engineering. IEEE","author":"Daka Ermira","year":"2014","unstructured":"Ermira Daka and Gordon Fraser. 2014. A Survey on Unit Testing Practices and Problems. In IEEE 25th International Symposium on Software Reliability Engineering. IEEE, Piscataway, NJ, USA, 201--211. 10.1109\/ISSRE.2014.11"},{"key":"e_1_2_1_21_1","volume-title":"Retrieved","year":"2022","unstructured":"Datadog. 2022. Automated root cause analysis with Watchdog RCA. Retrieved October 21, 2024 from https:\/\/www.datadoghq.com\/blog\/datadog-watchdog-automated-root-cause-analysis\/"},{"key":"e_1_2_1_22_1","unstructured":"Datadog. 2024. Retrieved October 21 2024 from https:\/\/www.datadoghq.com\/"},{"key":"e_1_2_1_23_1","volume-title":"LogLens: A Real-Time Log Analysis System. In 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS). IEEE","author":"Debnath Biplob","year":"2018","unstructured":"Biplob Debnath, Mohiuddin Solaimani, Muhammad Ali Gulzar Gulzar, Nipun Arora, Cristian Lumezanu, JianWu Xu, Bo Zong, Hui Zhang, Guofei Jiang, and Latifur Khan. 2018. LogLens: A Real-Time Log Analysis System. In 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS). IEEE, Piscataway, NJ, USA, 1052--1062. 10.1109\/ICDCS.2018.00105"},{"key":"e_1_2_1_24_1","volume-title":"Soviet Math. Doklady","author":"Dinic Efim A","unstructured":"Efim A Dinic. 1970. Algorithm for solution of a problem of maximum flow in networks with power estimation. In Soviet Math. Doklady, Vol. 11. American Mathematical Society, Providence, RI, USA, 1277--1280."},{"key":"e_1_2_1_25_1","doi-asserted-by":"crossref","first-page":"1165","DOI":"10.1109\/TCAD.2008.923410","article-title":"A survey of automated techniques for formal software verification","volume":"27","author":"D'silva Vijay","year":"2008","unstructured":"Vijay D'silva, Daniel Kroening, and Georg Weissenbacher. 2008. A survey of automated techniques for formal software verification. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 27, 7 (2008), 1165--1178.","journal-title":"IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems"},{"key":"e_1_2_1_26_1","volume-title":"Spell: Streaming Parsing of System Event Logs. In 2016 IEEE 16th International Conference on Data Mining (ICDM). IEEE","author":"Du Min","year":"2016","unstructured":"Min Du and Feifei Li. 2016. Spell: Streaming Parsing of System Event Logs. In 2016 IEEE 16th International Conference on Data Mining (ICDM). IEEE, Piscataway, NJ, USA, 859--864. 10.1109\/ICDM.2016.0103"},{"key":"e_1_2_1_27_1","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.jss.2012.06.025","article-title":"Failure prediction based on log files using random indexing and support vector machines","volume":"86","author":"Fronza Ilenia","year":"2013","unstructured":"Ilenia Fronza, Alberto Sillitti, Giancarlo Succi, Mikko Terho, and Jelena Vlasenko. 2013. Failure prediction based on log files using random indexing and support vector machines. Journal of Systems and Software 86, 1 (2013), 2--11.","journal-title":"Journal of Systems and Software"},{"key":"e_1_2_1_28_1","volume-title":"Proceedings of the 2022 International Conference on Management of Data","author":"Galhotra Sainyam","year":"2022","unstructured":"Sainyam Galhotra, Amir Gilad, Sudeepa Roy, and Babak Salimi. 2022. HypeR: Hypothetical Reasoning With What-If and How-To Queries Using a Probabilistic Causal Approach. In Proceedings of the 2022 International Conference on Management of Data (Philadelphia, PA, USA) (SIGMOD '22). Association for Computing Machinery, New York, NY, USA, 1598--1611. 10.1145\/3514221.3526149"},{"key":"e_1_2_1_29_1","volume-title":"Proceedings of the 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems","author":"Gan Yu","year":"2021","unstructured":"Yu Gan, Mingyu Liang, Sundar Dev, David Lo, and Christina Delimitrou. 2021. Sage: practical and scalable ML-driven performance debugging in microservices. In Proceedings of the 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems (Virtual, USA) (ASPLOS '21). Association for Computing Machinery, New York, NY, USA, 135--151. 10.1145\/3445814.3446700"},{"key":"e_1_2_1_30_1","volume-title":"Review of causal discovery methods based on graphical models. Frontiers in genetics 10","author":"Glymour Clark","year":"2019","unstructured":"Clark Glymour, Kun Zhang, and Peter Spirtes. 2019. Review of causal discovery methods based on graphical models. Frontiers in genetics 10 (2019), 524."},{"key":"e_1_2_1_31_1","volume-title":"Proceedings of the 2017 ACM International Conference on Management of Data","author":"Gudmundsdottir Helga","year":"2017","unstructured":"Helga Gudmundsdottir, Babak Salimi, Magdalena Balazinska, Dan R.K. Ports, and Dan Suciu. 2017. A Demonstration of Interactive Analysis of Performance Measurements with Viska. In Proceedings of the 2017 ACM International Conference on Management of Data (Chicago, Illinois, USA) (SIGMOD '17). Association for Computing Machinery, New York, NY, USA, 1707--1710. 10.1145\/3035918.3056448"},{"key":"e_1_2_1_32_1","volume-title":"2021 International Joint Conference on Neural Networks (IJCNN). IEEE","author":"Guo Haixuan","year":"2021","unstructured":"Haixuan Guo, Shuhan Yuan, and Xintao Wu. 2021. LogBERT: Log Anomaly Detection via BERT. In 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, Piscataway, NJ, USA, 1--8. 10.1109\/IJCNN52387.2021.9534113"},{"key":"e_1_2_1_33_1","volume-title":"Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis","author":"Gupta Saurabh","year":"2017","unstructured":"Saurabh Gupta, Tirthak Patel, Christian Engelmann, and Devesh Tiwari. 2017. Failures in large scale systems: long-term measurement, analysis, and implications. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (Denver, Colorado) (SC '17). Association for Computing Machinery, New York, NY, USA, Article 44, 12 pages. 10.1145\/3126908.3126937"},{"key":"e_1_2_1_34_1","volume-title":"Proceedings of the ACM SIGCOMM 2023 Conference (New York, NY, USA) (ACM SIGCOMM '23). Association for Computing Machinery","author":"Harsh Vipul","year":"2023","unstructured":"Vipul Harsh, Wenxuan Zhou, Sachin Ashok, Radhika Niranjan Mysore, Brighten Godfrey, and Sujata Banerjee. 2023. Murphy: Performance Diagnosis of Distributed Cloud Applications. In Proceedings of the ACM SIGCOMM 2023 Conference (New York, NY, USA) (ACM SIGCOMM '23). Association for Computing Machinery, New York, NY, USA, 438--451. 10.1145\/3603269.3604877"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1296"},{"key":"e_1_2_1_36_1","volume-title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19","author":"Hassanzadeh Oktie","year":"2019","unstructured":"Oktie Hassanzadeh, Debarun Bhattacharjya, Mark Feblowitz, Kavitha Srinivas, Michael Perrone, Shirin Sohrabi, and Michael Katz. 2019. Answering Binary Causal Questions Through Large-Scale Text Mining: An Evaluation Using Cause-Effect Pairs from Human Experts. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19. International Joint Conferences on Artificial Intelligence Organization, California, USA, 5003--5009. 10.24963\/ijcai.2019\/695"},{"key":"e_1_2_1_37_1","volume-title":"An Evaluation Study on Log Parsing and Its Use in Log Mining. In 2016 46th Annual IEEE\/IFIP International Conference on Dependable Systems and Networks (DSN). IEEE","author":"He Pinjia","year":"2016","unstructured":"Pinjia He, Jieming Zhu, Shilin He, Jian Li, and Michael R. Lyu. 2016. An Evaluation Study on Log Parsing and Its Use in Log Mining. In 2016 46th Annual IEEE\/IFIP International Conference on Dependable Systems and Networks (DSN). IEEE, Piscataway, NJ, USA, 654--661. 10.1109\/DSN.2016.66"},{"key":"e_1_2_1_38_1","first-page":"931","article-title":"Towards automated log parsing for large-scale log data analysis","volume":"15","author":"He Pinjia","year":"2017","unstructured":"Pinjia He, Jieming Zhu, Shilin He, Jian Li, and Michael R Lyu. 2017. Towards automated log parsing for large-scale log data analysis. IEEE Transactions on Dependable and Secure Computing 15, 6 (2017), 931--944.","journal-title":"IEEE Transactions on Dependable and Secure Computing"},{"key":"e_1_2_1_39_1","volume-title":"Drain: An Online Log Parsing Approach with Fixed Depth Tree. In 2017 IEEE International Conference on Web Services (ICWS). IEEE","author":"He Pinjia","year":"2017","unstructured":"Pinjia He, Jieming Zhu, Zibin Zheng, and Michael R. Lyu. 2017. Drain: An Online Log Parsing Approach with Fixed Depth Tree. In 2017 IEEE International Conference on Web Services (ICWS). IEEE, Piscataway, NJ, USA, 33--40. 10.1109\/ICWS.2017.13"},{"key":"e_1_2_1_40_1","volume-title":"Proceedings of the 29th ACM International Conference on Information & Knowledge Management","author":"Heindorf Stefan","year":"2020","unstructured":"Stefan Heindorf, Yan Scholten, Henning Wachsmuth, Axel-Cyrille Ngonga Ngomo, and Martin Potthast. 2020. CauseNet: Towards a Causality Graph Extracted from the Web. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management (Virtual Event, Ireland) (CIKM '20). Association for Computing Machinery, New York, NY, USA, 3023--3030. 10.1145\/3340531.3412763"},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-34878-0_13"},{"key":"e_1_2_1_42_1","volume-title":"NOMS 2020 - 2020 IEEE\/IFIP Network Operations and Management Symposium. IEEE","author":"Huang Shaohan","year":"2020","unstructured":"Shaohan Huang, Yi Liu, Carol Fung, Rong He, Yining Zhao, Hailong Yang, and Zhongzhi Luan. 2020. Paddy: An Event Log Parsing Approach using Dynamic Dictionary. In NOMS 2020 - 2020 IEEE\/IFIP Network Operations and Management Symposium. IEEE, Piscataway, NJ, USA, 1--8. 10.1109\/NOMS47738.2020.9110435"},{"key":"e_1_2_1_43_1","volume-title":"Retrieved","author":"Flowerfire Inc. [n.d.]. Sawmill","year":"2024","unstructured":"Flowerfire Inc. [n.d.]. Sawmill: Universal Log File Analysis and Reporting. Retrieved October 21, 2024 from http:\/\/www.sawmill.net\/"},{"key":"e_1_2_1_44_1","volume-title":"Intel Xeon Gold 6230 CPU","author":"Intel Corporation","year":"2024","unstructured":"Intel Corporation. 2019. Intel Xeon Gold 6230 CPU. Intel Corporation. Retrieved October 21, 2024 from https:\/\/ark.intel.com\/content\/www\/us\/en\/ark\/products\/192437\/intel-xeon-gold-6230-processor-27-5m-cache-2-10-ghz.html"},{"key":"e_1_2_1_45_1","volume-title":"Proceedings of the 2019 International Conference on Management of Data","author":"Jeyakumar Vimalkumar","year":"2019","unstructured":"Vimalkumar Jeyakumar, Omid Madani, Ali Parandeh, Ashutosh Kulshreshtha, Weifei Zeng, and Navindra Yadav. 2019. ExplainIt! - A Declarative Root-cause Analysis Engine for Time Series Data. In Proceedings of the 2019 International Conference on Management of Data (Amsterdam, Netherlands) (SIGMOD '19). Association for Computing Machinery, New York, NY, USA, 333--348. 10.1145\/3299869.3314048"},{"key":"e_1_2_1_46_1","volume-title":"Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering","author":"Jiang Jiajun","year":"2020","unstructured":"Jiajun Jiang, Weihai Lu, Junjie Chen, Qingwei Lin, Pu Zhao, Yu Kang, Hongyu Zhang, Yingfei Xiong, Feng Gao, Zhangwei Xu, Yingnong Dang, and Dongmei Zhang. 2020. How to mitigate the incident? an effective troubleshooting guide recommendation technique for online service systems. In Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (Virtual Event, USA) (ESEC\/FSE 2020). Association for Computing Machinery, New York, NY, USA, 1410--1420. 10.1145\/3368089.3417054"},{"key":"e_1_2_1_47_1","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1109\/MCS.2016.2602089","article-title":"Simulation-based approaches for verification of embedded control systems: An overview of traditional and advanced modeling, testing, and verification techniques","volume":"36","author":"Kapinski James","year":"2016","unstructured":"James Kapinski, Jyotirmoy V Deshmukh, Xiaoqing Jin, Hisahiro Ito, and Ken Butts. 2016. Simulation-based approaches for verification of embedded control systems: An overview of traditional and advanced modeling, testing, and verification techniques. IEEE Control Systems Magazine 36, 6 (2016), 45--64.","journal-title":"IEEE Control Systems Magazine"},{"key":"e_1_2_1_48_1","volume-title":"Proceedings of the 25th Symposium on Operating Systems Principles (SOSP '15)","author":"Kasikci Baris","year":"2015","unstructured":"Baris Kasikci, Benjamin Schubert, Cristiano Pereira, Gilles Pokam, and George Candea. 2015. Failure sketching: a technique for automated root cause diagnosis of in-production failures. In Proceedings of the 25th Symposium on Operating Systems Principles (SOSP '15). Association for Computing Machinery, New York, NY, USA, 344--360. 10.1145\/2815400.2815412"},{"key":"e_1_2_1_49_1","volume-title":"David Hall, Percy Liang, Christopher Potts, and Matei Zaharia.","author":"Khattab Omar","year":"2023","unstructured":"Omar Khattab, Keshav Santhanam, Xiang Lisa Li, David Hall, Percy Liang, Christopher Potts, and Matei Zaharia. 2023. Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP. arXiv:2212.14024 [cs.CL] https:\/\/arxiv.org\/abs\/2212.14024"},{"key":"e_1_2_1_50_1","unstructured":"Omar Khattab Arnav Singhvi Paridhi Maheshwari Zhiyuan Zhang Keshav Santhanam Sri Vardhamanan Saiful Haq Ashutosh Sharma Thomas T. Joshi Hanna Moazam Heather Miller Matei Zaharia and Christopher Potts. 2023. DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines. arXiv:2310.03714 [cs.CL] https:\/\/arxiv.org\/abs\/2310.03714"},{"key":"e_1_2_1_51_1","unstructured":"Emre K\u0131c\u0131man Robert Ness Amit Sharma and Chenhao Tan. 2023. Causal Reasoning and Large Language Models: Opening a New Frontier for Causality. arXiv:2305.00050 [cs.AI] https:\/\/arxiv.org\/abs\/2305.00050"},{"key":"e_1_2_1_52_1","volume-title":"Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence (Proceedings of Machine Learning Research), James Cussens and Kun Zhang (Eds.)","volume":"180","author":"Lam Wai-Yin","year":"2022","unstructured":"Wai-Yin Lam, Bryan Andrews, and Joseph Ramsey. 2022. Greedy relaxations of the sparsest permutation algorithm. In Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence (Proceedings of Machine Learning Research), James Cussens and Kun Zhang (Eds.), Vol. 180. PMLR, Cambridge, MA, USA, 1052--1062. https:\/\/proceedings.mlr.press\/v180\/lam22a.html"},{"key":"e_1_2_1_53_1","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1002\/stvr.299","article-title":"Applying simulation and design of experiments to the embedded software testing process","volume":"14","author":"Lazi\u0107 Lj","year":"2004","unstructured":"Lj Lazi\u0107 and D Vela\u0161evi\u0107. 2004. Applying simulation and design of experiments to the embedded software testing process. Software Testing, Verification and Reliability 14, 4 (2004), 257--282.","journal-title":"Software Testing, Verification and Reliability"},{"key":"e_1_2_1_54_1","volume-title":"Zui Chen, Michael Franklin, Tim Kraska, Samuel Madden, Rana Shahout, et al.","author":"Liu Chunwei","year":"2025","unstructured":"Chunwei Liu, Matthew Russo, Michael Cafarella, Lei Cao, Peter Baile Chen, Zui Chen, Michael Franklin, Tim Kraska, Samuel Madden, Rana Shahout, et al. 2025. Palimpzest: Optimizing AI-Powered Analytics with Declarative Query Processing. In CIDR 2025."},{"key":"e_1_2_1_55_1","volume-title":"Zui Chen, Michael Franklin, Tim Kraska, Samuel Madden, and Gerardo Vitagliano.","author":"Liu Chunwei","year":"2024","unstructured":"Chunwei Liu, Matthew Russo, Michael Cafarella, Lei Cao, Peter Baille Chen, Zui Chen, Michael Franklin, Tim Kraska, Samuel Madden, and Gerardo Vitagliano. 2024. A Declarative System for Optimizing AI Workloads. arXiv:2405.14696 [cs.CL] https:\/\/arxiv.org\/abs\/2405.14696"},{"key":"e_1_2_1_56_1","unstructured":"Xiaoyu Liu Paiheng Xu Junda Wu Jiaxin Yuan Yifan Yang Yuhang Zhou Fuxiao Liu Tianrui Guan Haoliang Wang Tong Yu Julian McAuley Wei Ai and Furong Huang. 2024. Large Language Models and Causal Inference in Collaboration: A Comprehensive Survey. arXiv:2403.09606 [cs.CL] https:\/\/arxiv.org\/abs\/2403.09606"},{"key":"e_1_2_1_57_1","volume-title":"Proceedings of the ACM Web Conference 2022 (Virtual Event","author":"Liu Yudong","year":"2022","unstructured":"Yudong Liu, Xu Zhang, Shilin He, Hongyu Zhang, Liqun Li, Yu Kang, Yong Xu, Minghua Ma, Qingwei Lin, Yingnong Dang, Saravan Rajmohan, and Dongmei Zhang. 2022. UniParser: A Unified Log Parser for Heterogeneous Log Data. In Proceedings of the ACM Web Conference 2022 (Virtual Event, Lyon, France) (WWW '22). Association for Computing Machinery, New York, NY, USA, 1893--1901. 10.1145\/3485447.3511993"},{"key":"e_1_2_1_58_1","unstructured":"Stephanie Long Tibor Schuster and Alexandre Pich\u00e9. 2024. Can large language models build causal graphs? arXiv:2303.05279 [cs.CL] https:\/\/arxiv.org\/abs\/2303.05279"},{"key":"e_1_2_1_59_1","first-page":"1","article-title":"XInsight: eXplainable Data Analysis Through The Lens of Causality","volume":"1","author":"Ma Pingchuan","year":"2023","unstructured":"Pingchuan Ma, Rui Ding, Shuai Wang, Shi Han, and Dongmei Zhang. 2023. XInsight: eXplainable Data Analysis Through The Lens of Causality. Proceedings of the ACM on Management of Data 1, 2 (2023), 1--27.","journal-title":"Proceedings of the ACM on Management of Data"},{"key":"e_1_2_1_60_1","volume-title":"Retrieved","author":"Markakis Markos","year":"2024","unstructured":"Markos Markakis, An Bo Chen, Trinity Gao, and Peter Baile Chen. 2024. LOGos Code. Retrieved October 21, 2024 from https:\/\/github.com\/mitdbg\/logos"},{"key":"e_1_2_1_61_1","volume-title":"Companion of the 2024 International Conference on Management of Data (Santiago AA, Chile) (SIGMOD\/PODS '24)","author":"Markakis Markos","year":"2024","unstructured":"Markos Markakis, An Bo Chen, Brit Youngmann, Trinity Gao, Ziyu Zhang, Rana Shahout, Peter Baile Chen, Chunwei Liu, Ibrahim Sabek, and Michael Cafarella. 2024. Sawmill: From Logs to Causal Diagnosis of Large Systems. In Companion of the 2024 International Conference on Management of Data (Santiago AA, Chile) (SIGMOD\/PODS '24). Association for Computing Machinery, New York, NY, USA, 444--447. 10.1145\/3626246.3654731"},{"key":"e_1_2_1_62_1","volume-title":"Proceedings of the Conference on Governance, Understanding and Integration of Data for Effective and Responsible AI (Santiago, AA, Chile) (GUIDE-AI '24)","author":"Markakis Markos","year":"2024","unstructured":"Markos Markakis, Ziyu Zhang, Rana Shahout, Trinity Gao, Chunwei Liu, Ibrahim Sabek, and Michael Cafarella. 2024. Press ECCS to Doubt (Your Causal Graph). In Proceedings of the Conference on Governance, Understanding and Integration of Data for Effective and Responsible AI (Santiago, AA, Chile) (GUIDE-AI '24). Association for Computing Machinery, New York, NY, USA, 6--15. 10.1145\/3665601.3669842"},{"key":"e_1_2_1_63_1","volume-title":"PerpLE: Improving the Speed and Effectiveness of Memory Consistency Testing. In 53rd Annual IEEE\/ACM International Symposium on Microarchitecture (MICRO). IEEE","author":"Melissaris Themis","year":"2020","unstructured":"Themis Melissaris, Markos Markakis, Kelly Shaw, and Margaret Martonosi. 2020. PerpLE: Improving the Speed and Effectiveness of Memory Consistency Testing. In 53rd Annual IEEE\/ACM International Symposium on Microarchitecture (MICRO). IEEE, Piscataway, NJ, USA, 329--341. 10.1109\/MICRO50266.2020.00037"},{"key":"e_1_2_1_64_1","volume-title":"2020 29th International Conference on Computer Communications and Networks (ICCCN). IEEE","author":"Meng Weibin","year":"2020","unstructured":"Weibin Meng, Ying Liu, Federico Zaiter, Shenglin Zhang, Yihao Chen, Yuzhe Zhang, Yichen Zhu, En Wang, Ruizhi Zhang, Shimin Tao, Dian Yang, Rong Zhou, and Dan Pei. 2020. LogParse: Making Log Parsing Adaptive through Word Classification. In 2020 29th International Conference on Computer Communications and Networks (ICCCN). IEEE, Piscataway, NJ, USA, 1--9. 10.1109\/ICCCN49398.2020.9209681"},{"key":"e_1_2_1_65_1","volume-title":"The Art of Software Testing","author":"Myers Glenford J","unstructured":"Glenford J Myers, Corey Sandler, and Tom Badgett. 2011. The Art of Software Testing. Wiley, Hoboken, NJ, USA."},{"key":"e_1_2_1_66_1","volume-title":"Brian Piening, and Kevin Matlock.","author":"Naik Narmada","year":"2023","unstructured":"Narmada Naik, Ayush Khandelwal, Mohit Joshi, Madhusudan Atre, Hollis Wright, Kavya Kannan, Scott Hill, Giridhar Mamidipudi, Ganapati Srinivasa, Carlo Bifulco, Brian Piening, and Kevin Matlock. 2023. Applying Large Language Models for Causal Structure Learning in Non Small Cell Lung Cancer. arXiv:2311.07191 [cs.AI] https:\/\/arxiv.org\/abs\/2311.07191"},{"key":"e_1_2_1_67_1","volume-title":"Machine Learning and Knowledge Discovery in Databases: Applied Data Science Track, Yuxiao Dong, Dunja Mladeni\u0107","author":"Nedelkoski Sasho","unstructured":"Sasho Nedelkoski, Jasmin Bogatinovski, Alexander Acker, Jorge Cardoso, and Odej Kao. 2021. Self-supervised Log Parsing. In Machine Learning and Knowledge Discovery in Databases: Applied Data Science Track, Yuxiao Dong, Dunja Mladeni\u0107, and Craig Saunders (Eds.). Springer International, Cham, 122--138."},{"key":"e_1_2_1_68_1","volume-title":"Falcon: A Practical Log-Based Analysis Tool for Distributed Systems. In 48th Annual IEEE\/IFIP International Conference on Dependable Systems and Networks (DSN). IEEE","author":"Neves Francisco","year":"2018","unstructured":"Francisco Neves, Nuno Machado, and Jos\u00c3\u00a9 Pereira. 2018. Falcon: A Practical Log-Based Analysis Tool for Distributed Systems. In 48th Annual IEEE\/IFIP International Conference on Dependable Systems and Networks (DSN). IEEE, Piscataway, NJ, USA, 534--541. 10.1109\/DSN.2018.00061"},{"key":"e_1_2_1_69_1","volume-title":"Horus: Non-Intrusive Causal Analysis of Distributed Systems Logs. In 51st Annual IEEE\/IFIP International Conference on Dependable Systems and Networks (DSN). IEEE","author":"Neves Francisco","year":"2021","unstructured":"Francisco Neves, Nuno Machado, Ricardo Vila\u00e7a, and Jos\u00e9 Pereira. 2021. Horus: Non-Intrusive Causal Analysis of Distributed Systems Logs. In 51st Annual IEEE\/IFIP International Conference on Dependable Systems and Networks (DSN). IEEE, Piscataway, NJ, USA, 212--223. 10.1109\/DSN48987.2021.00035"},{"key":"e_1_2_1_70_1","doi-asserted-by":"crossref","first-page":"548","DOI":"10.1097\/EDE.0000000000000659","article-title":"Repair of partly misspecified causal diagrams","volume":"28","author":"Oates Chris J","year":"2017","unstructured":"Chris J Oates, Jessica Kasza, Julie A Simpson, and Andrew B Forbes. 2017. Repair of partly misspecified causal diagrams. Epidemiology 28, 4 (2017), 548--552.","journal-title":"Epidemiology"},{"key":"e_1_2_1_72_1","unstructured":"OpenAI. 2024. Retrieved October 21 2024 from https:\/\/openai.com\/index\/gpt-4o-mini-advancing-cost-efficient-intelligence\/"},{"key":"e_1_2_1_73_1","volume-title":"Retrieved","author":"AI.","year":"2024","unstructured":"OpenAI. 2024. Models. Retrieved October 21, 2024 from https:\/\/platform.openai.com\/docs\/models\/gpt-3-5-turbo"},{"key":"e_1_2_1_74_1","volume-title":"Proceedings of the 7th conference of the Cognitive Science Society. Cognitive Science Society","author":"Pearl Judea","year":"1985","unstructured":"Judea Pearl. 1985. Bayesian networks: A model of self-activated memory for evidential reasoning. In Proceedings of the 7th conference of the Cognitive Science Society. Cognitive Science Society, Seattle, WA, USA, 15--17."},{"key":"e_1_2_1_75_1","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9780511803161","volume-title":"Causality: Models, Reasoning and Inference","author":"Pearl Judea","year":"2009","unstructured":"Judea Pearl. 2009. Causality: Models, Reasoning and Inference. Cambridge University Press, Cambridge, UK."},{"key":"e_1_2_1_76_1","volume-title":"The Book of Why: The New Science of Cause and Effect","author":"Pearl Judea","unstructured":"Judea Pearl and Dana Mackenzie. 2018. The Book of Why: The New Science of Cause and Effect. Basic Books, New York, NY, USA."},{"key":"e_1_2_1_77_1","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa F.","year":"2011","unstructured":"F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. 2011. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12 (2011), 2825--2830.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_2_1_78_1","volume-title":"Proceedings of the 2002 ACM SIGMOD International Conference on Management of Data","author":"Poess Meikel","year":"2002","unstructured":"Meikel Poess, Bryan Smith, Lubor Kollar, and Paul Larson. 2002. TPC-DS, taking decision support benchmarking to the next level. In Proceedings of the 2002 ACM SIGMOD International Conference on Management of Data (Madison, Wisconsin) (SIGMOD '02). Association for Computing Machinery, New York, NY, USA, 582--587. 10.1145\/564691.564759"},{"key":"e_1_2_1_79_1","volume-title":"Miguel Angel Hernan, and Babette Brumback","author":"Robins James M","year":"2000","unstructured":"James M Robins, Miguel Angel Hernan, and Babette Brumback. 2000. Marginal structural models and causal inference in epidemiology."},{"key":"e_1_2_1_80_1","doi-asserted-by":"crossref","first-page":"591","DOI":"10.2307\/2287653","article-title":"Discussion of 'Randomization Analysis of Experimental Data in the Fisher Randomization Test'by Basu","volume":"75","author":"Rubin Donald B.","year":"1980","unstructured":"Donald B. Rubin. 1980. Discussion of 'Randomization Analysis of Experimental Data in the Fisher Randomization Test'by Basu. J. Amer. Statist. Assoc. 75, 371 (1980), 591--93.","journal-title":"J. Amer. Statist. Assoc."},{"key":"e_1_2_1_81_1","volume-title":"A survey of unit testing practices","author":"Runeson Per","year":"2006","unstructured":"Per Runeson. 2006. A survey of unit testing practices. IEEE software 23, 4 (2006), 22--29."},{"key":"e_1_2_1_82_1","volume-title":"Proceedings of the 5th Workshop on Evaluation and Usability of Programming Languages and Tools","author":"Sadowski Caitlin","year":"2014","unstructured":"Caitlin Sadowski and Jaeheon Yi. 2014. How Developers Use Data Race Detection Tools. In Proceedings of the 5th Workshop on Evaluation and Usability of Programming Languages and Tools (Portland, Oregon, USA) (PLATEAU '14). Association for Computing Machinery, New York, NY, USA, 43--51. 10.1145\/2688204.2688205"},{"key":"e_1_2_1_83_1","first-page":"4","article-title":"Error Log Processing for Accurate Failure Prediction","volume":"8","author":"Salfner Felix","year":"2008","unstructured":"Felix Salfner and Steffen Tschirpke. 2008. Error Log Processing for Accurate Failure Prediction. WASL 8 (2008), 4.","journal-title":"WASL"},{"key":"e_1_2_1_84_1","volume-title":"Proceedings of the 2018 International Conference on Management of Data","author":"Salimi Babak","year":"2018","unstructured":"Babak Salimi, Johannes Gehrke, and Dan Suciu. 2018. Bias in OLAP Queries: Detection, Explanation, and Removal. In Proceedings of the 2018 International Conference on Management of Data (Houston, TX, USA) (SIGMOD '18). Association for Computing Machinery, New York, NY, USA, 1021--1035. 10.1145\/3183713.3196914"},{"key":"e_1_2_1_85_1","volume-title":"A large-scale study of failures in high-performance computing systems","author":"Schroeder Bianca","year":"2009","unstructured":"Bianca Schroeder and Garth A Gibson. 2009. A large-scale study of failures in high-performance computing systems. IEEE transactions on Dependable and Secure Computing 7, 4 (2009), 337--350."},{"key":"e_1_2_1_86_1","volume-title":"Retrieved","author":"Services Amazon Web","year":"2024","unstructured":"Amazon Web Services. [n.d.]. Cloud Compute Capacity - Amazon EC2 - AWS. Retrieved October 21, 2024 from https:\/\/aws.amazon.com\/ec2\/"},{"key":"e_1_2_1_87_1","unstructured":"Amit Sharma and Emre Kiciman. 2020. DoWhy: An End-to-End Library for Causal Inference. arXiv:2011.04216 [stat.ME] https:\/\/arxiv.org\/abs\/2011.04216"},{"key":"e_1_2_1_88_1","volume-title":"Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering","author":"Shetty Manish","year":"2022","unstructured":"Manish Shetty, Chetan Bansal, Sai Pramod Upadhyayula, Arjun Radhakrishna, and Anurag Gupta. 2022. AutoTSG: learning and synthesis for incident troubleshooting. In Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (Singapore, Singapore) (ESEC\/FSE 2022). Association for Computing Machinery, New York, NY, USA, 1477--1488. 10.1145\/3540250.3558958"},{"key":"e_1_2_1_89_1","doi-asserted-by":"crossref","first-page":"e1581","DOI":"10.1002\/wics.1581","article-title":"Data integration in causal inference","volume":"15","author":"Shi Xu","year":"2023","unstructured":"Xu Shi, Ziyang Pan, and Wang Miao. 2023. Data integration in causal inference. Wiley Interdisciplinary Reviews: Computational Statistics 15, 1 (2023), e1581.","journal-title":"Wiley Interdisciplinary Reviews: Computational Statistics"},{"key":"e_1_2_1_90_1","first-page":"2003","article-title":"A linear non-Gaussian acyclic model for causal discovery","volume":"7","author":"Shimizu Shohei","year":"2006","unstructured":"Shohei Shimizu, Patrik O Hoyer, Aapo Hyv\u00e4rinen, Antti Kerminen, and Michael Jordan. 2006. A linear non-Gaussian acyclic model for causal discovery. Journal of Machine Learning Research 7, 72 (2006), 2003--2030. http:\/\/jmlr.org\/papers\/v7\/shimizu06a.html","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_2_1_91_1","volume-title":"Cause and correlation in biology: a user's guide to path analysis, structural equations and causal inference with R","author":"Shipley Bill","unstructured":"Bill Shipley. 2016. Cause and correlation in biology: a user's guide to path analysis, structural equations and causal inference with R. Cambridge University Press, Cambridge, UK."},{"key":"e_1_2_1_92_1","volume-title":"Proceedings of the Twenty-Second Conference on Uncertainty in Artificial Intelligence","author":"Silander Tomi","year":"2006","unstructured":"Tomi Silander and Petri Myllym\u00e4ki. 2006. A simple approach for finding the globally optimal Bayesian network structure. In Proceedings of the Twenty-Second Conference on Uncertainty in Artificial Intelligence (Cambridge, MA, USA) (UAI'06). AUAI Press, Arlington, Virginia, USA, 445--452."},{"key":"e_1_2_1_93_1","volume-title":"An algorithm for fast recovery of sparse causal graphs. Social science computer review 9, 1","author":"Spirtes Peter","year":"1991","unstructured":"Peter Spirtes and Clark Glymour. 1991. An algorithm for fast recovery of sparse causal graphs. Social science computer review 9, 1 (1991), 62--72."},{"key":"e_1_2_1_94_1","volume-title":"prediction, and search","author":"Spirtes Peter","unstructured":"Peter Spirtes, Clark N Glymour, and Richard Scheines. 2000. Causation, prediction, and search. MIT press, Cambridge, MA, USA."},{"key":"e_1_2_1_95_1","unstructured":"Splunk. 2024. Retrieved October 21 2024 from https:\/\/www.splunk.com\/"},{"key":"e_1_2_1_96_1","volume-title":"Retrieved","author":"Starosta Abraham","year":"2021","unstructured":"Abraham Starosta. 2021. How Splunk Is Parsing Machine Logs With Machine Learning On NVIDIA's Triton and Morpheus. Retrieved October 21, 2024 from https:\/\/www.splunk.com\/en_us\/blog\/it\/how-splunk-is-parsing-machine-logs-with-machine-learning-on-nvidia-s-triton-and-morpheus.html"},{"key":"e_1_2_1_97_1","volume-title":"Finding Minimal D-separators. Citeseer","author":"Tian Jin","unstructured":"Jin Tian, Azaria Paz, and Judea Pearl. 1998. Finding Minimal D-separators. Citeseer, University Park, PA, USA."},{"key":"e_1_2_1_98_1","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression Shrinkage and Selection via the Lasso","volume":"58","author":"Tibshirani Robert","year":"1996","unstructured":"Robert Tibshirani. 1996. Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B (Methodological) 58, 1 (1996), 267--288.","journal-title":"Journal of the Royal Statistical Society: Series B (Methodological)"},{"key":"e_1_2_1_99_1","unstructured":"Ruibo Tu Chao Ma and Cheng Zhang. 2023. Causal-Discovery Performance of ChatGPT in the context of Neuropathic Pain Diagnosis. arXiv:2301.13819 [cs.CL] https:\/\/arxiv.org\/abs\/2301.13819"},{"key":"e_1_2_1_100_1","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1145\/1323293.1294275","article-title":"Triage: diagnosing production run failures at the user's site","volume":"41","author":"Tucek Joseph","year":"2007","unstructured":"Joseph Tucek, Shan Lu, Chengdu Huang, Spiros Xanthos, and Yuanyuan Zhou. 2007. Triage: diagnosing production run failures at the user's site. ACM SIGOPS Operating Systems Review 41, 6 (2007), 131--144.","journal-title":"ACM SIGOPS Operating Systems Review"},{"key":"e_1_2_1_101_1","doi-asserted-by":"crossref","first-page":"7310","DOI":"10.1073\/pnas.1510479113","article-title":"Causal inference in economics and marketing","volume":"113","author":"Varian Hal R","year":"2016","unstructured":"Hal R Varian. 2016. Causal inference in economics and marketing. Proceedings of the National Academy of Sciences 113, 27 (2016), 7310--7315.","journal-title":"Proceedings of the National Academy of Sciences"},{"key":"e_1_2_1_102_1","volume-title":"Abhinav Kumar, Saketh Bachu, Vineeth N Balasubramanian, and Amit Sharma.","author":"Vashishtha Aniket","year":"2023","unstructured":"Aniket Vashishtha, Abbavaram Gowtham Reddy, Abhinav Kumar, Saketh Bachu, Vineeth N Balasubramanian, and Amit Sharma. 2023. Causal Inference Using LLM-Guided Discovery. arXiv:2310.15117 [cs.AI] https:\/\/arxiv.org\/abs\/2310.15117"},{"key":"e_1_2_1_103_1","volume-title":"Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence (UAI '90)","author":"Verma Thomas","year":"1990","unstructured":"Thomas Verma and Judea Pearl. 1990. Equivalence and Synthesis of Causal Models. In Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence (UAI '90). Elsevier Science Inc., USA, 255--270."},{"key":"e_1_2_1_104_1","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1109\/52.28119","article-title":"Software verification and validation: an overview","volume":"6","author":"Wallace Dolores R","year":"1989","unstructured":"Dolores R Wallace and Roger U Fujii. 1989. Software verification and validation: an overview. Ieee Software 6, 3 (1989), 10--17.","journal-title":"Ieee Software"},{"key":"e_1_2_1_105_1","volume-title":"18th USENIX Symposium on Networked Systems Design and Implementation (NSDI 21)","author":"Xia Yiting","year":"2021","unstructured":"Yiting Xia, Ying Zhang, Zhizhen Zhong, Guanqing Yan, Chiun Lin Lim, Satyajeet Singh Ahuja, Soshant Bali, Alexander Nikolaidis, Kimia Ghobadi, and Manya Ghobadi. 2021. A Social Network Under Social Distancing: Risk-Driven Backbone Management During COVID-19 and Beyond. In 18th USENIX Symposium on Networked Systems Design and Implementation (NSDI 21). USENIX Association, Berkeley, CA, USA, 217--231."},{"key":"e_1_2_1_106_1","volume-title":"LPV: A Log Parser Based on Vectorization for Offline and Online Log Parsing. In 2020 IEEE International Conference on Data Mining (ICDM). IEEE","author":"Xiao Tong","year":"2020","unstructured":"Tong Xiao, Zhe Quan, Zhi-Jie Wang, Kaiqi Zhao, and Xiangke Liao. 2020. LPV: A Log Parser Based on Vectorization for Offline and Online Log Parsing. In 2020 IEEE International Conference on Data Mining (ICDM). IEEE, Piscataway, NJ, USA, 1346--1351. 10.1109\/ICDM50108.2020.00175"},{"key":"e_1_2_1_107_1","volume-title":"Generalized independent noise condition for estimating latent variable causal graphs. Advances in neural information processing systems 33","author":"Xie Feng","year":"2020","unstructured":"Feng Xie, Ruichu Cai, Biwei Huang, Clark Glymour, Zhifeng Hao, and Kun Zhang. 2020. Generalized independent noise condition for estimating latent variable causal graphs. Advances in neural information processing systems 33 (2020), 14891--14902."},{"key":"e_1_2_1_108_1","volume-title":"Proceedings of the ACM SIGOPS 22nd Symposium on Operating Systems Principles","author":"Xu Wei","unstructured":"Wei Xu, Ling Huang, Armando Fox, David Patterson, and Michael I. Jordan. 2009. Detecting large-scale system problems by mining console logs. In Proceedings of the ACM SIGOPS 22nd Symposium on Operating Systems Principles (Big Sky, Montana, USA) (SOSP '09). Association for Computing Machinery, New York, NY, USA, 117--132. 10.1145\/1629575.1629587"},{"key":"e_1_2_1_109_1","doi-asserted-by":"publisher","DOI":"10.14778\/3603581.3603602"},{"key":"e_1_2_1_110_1","doi-asserted-by":"crossref","first-page":"2659","DOI":"10.14778\/3603581.3603602","article-title":"Causal Data Integration","volume":"16","author":"Youngmann Brit","year":"2023","unstructured":"Brit Youngmann, Michael J. Cafarella, Babak Salimi, and Anna Zeng. 2023. Causal Data Integration. Proc. VLDB Endow. 16, 10 (2023), 2659--2665.","journal-title":"Proc. VLDB Endow."},{"key":"e_1_2_1_111_1","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1613\/jair.4039","article-title":"Learning optimal Bayesian networks: A shortest path perspective","volume":"48","author":"Yuan Changhe","year":"2013","unstructured":"Changhe Yuan and Brandon Malone. 2013. Learning optimal Bayesian networks: A shortest path perspective. Journal of Artificial Intelligence Research 48 (2013), 23--65.","journal-title":"Journal of Artificial Intelligence Research"},{"key":"e_1_2_1_112_1","doi-asserted-by":"publisher","DOI":"10.1145\/1735970.1736038"},{"key":"e_1_2_1_113_1","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1016\/j.camwa.2011.07.040","article-title":"Job failures in high performance computing systems: A large-scale empirical study","volume":"63","author":"Yuan Yulai","year":"2012","unstructured":"Yulai Yuan, Yongwei Wu, Qiuping Wang, Guangwen Yang, and Weimin Zheng. 2012. Job failures in high performance computing systems: A large-scale empirical study. Computers & Mathematics with Applications 63, 2 (2012), 365--377.","journal-title":"Computers & Mathematics with Applications"},{"key":"e_1_2_1_114_1","doi-asserted-by":"crossref","first-page":"1014","DOI":"10.1038\/s41467-022-28546-8","article-title":"Uncovering interpretable potential confounders in electronic medical records","volume":"13","author":"Zeng Jiaming","year":"2022","unstructured":"Jiaming Zeng, Michael F Gensheimer, Daniel L Rubin, Susan Athey, and Ross D Shachter. 2022. Uncovering interpretable potential confounders in electronic medical records. 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System log pre-processing to improve failure prediction. In 2009 IEEE\/IFIP International Conference on Dependable Systems & Networks. IEEE, Piscataway, NJ, USA, 572--577. 10.1109\/DSN.2009.5270289"},{"key":"e_1_2_1_119_1","unstructured":"Jiongli Zhu Sainyam Galhotra Nazanin Sabri and Babak Salimi. 2023. Consistent Range Approximation for Fair Predictive Modeling. arXiv:2212.10839 [cs.LG] https:\/\/arxiv.org\/abs\/2212.10839"},{"key":"e_1_2_1_120_1","volume-title":"Lyu","author":"Zhu Jieming","year":"2023","unstructured":"Jieming Zhu, Shilin He, Pinjia He, Jinyang Liu, and Michael R. Lyu. 2023. Loghub: A Large Collection of System Log Datasets for AI-driven Log Analytics. arXiv:2008.06448 [cs.SE] https:\/\/arxiv.org\/abs\/2008.06448"},{"key":"e_1_2_1_121_1","volume-title":"Proceedings of the 41st International Conference on Software Engineering: Software Engineering in Practice (Montreal","author":"Zhu Jieming","year":"2019","unstructured":"Jieming Zhu, Shilin He, Jinyang Liu, Pinjia He, Qi Xie, Zibin Zheng, and Michael R. Lyu. 2019. Tools and benchmarks for automated log parsing. In Proceedings of the 41st International Conference on Software Engineering: Software Engineering in Practice (Montreal, Quebec, Canada) (ICSE-SEIP '19). IEEE, Piscataway, NJ, USA, 121--130. 10.1109\/ICSE-SEIP.2019.00021"},{"key":"e_1_2_1_122_1","unstructured":"Shengyu Zhu Ignavier Ng and Zhitang Chen. 2020. Causal Discovery with Reinforcement Learning. arXiv:1906.04477 [cs.LG] https:\/\/arxiv.org\/abs\/1906.04477"},{"key":"e_1_2_1_123_1","first-page":"1","article-title":"Runtime Variation in Big Data Analytics","volume":"1","author":"Zhu Yiwen","year":"2023","unstructured":"Yiwen Zhu, Rathijit Sen, Robert Horton, and John Mark Agosta. 2023. Runtime Variation in Big Data Analytics. Proceedings of the ACM on Management of Data 1, 1 (2023), 1--20.","journal-title":"Proceedings of the ACM on Management of Data"},{"key":"e_1_2_1_124_1","volume-title":"Proceedings of the Conference of the ACM Special Interest Group on Data Communication","author":"Zhuo Danyang","year":"2017","unstructured":"Danyang Zhuo, Monia Ghobadi, Ratul Mahajan, Klaus-Tycho F\u00f6rster, Arvind Krishnamurthy, and Thomas Anderson. 2017. Understanding and Mitigating Packet Corruption in Data Center Networks. In Proceedings of the Conference of the ACM Special Interest Group on Data Communication (Los Angeles, CA, USA) (SIGCOMM '17). Association for Computing Machinery, New York, NY, USA, 362--375. 10.1145\/3098822.3098849"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3705829.3705836","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,28]],"date-time":"2025-02-28T23:28:22Z","timestamp":1740785302000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3705829.3705836"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10]]},"references-count":123,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,10]]}},"alternative-id":["10.14778\/3705829.3705836"],"URL":"https:\/\/doi.org\/10.14778\/3705829.3705836","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2024,10]]},"assertion":[{"value":"2025-02-28","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}