Thomas Feltin

@inproceedings{nokey,

title = {Understanding Semantics in Feature Selection for Fault Diagnosis in Network Telemetry Data},

author = {Thomas Feltin and Juan Antonio Cordero and Thomas Clausen and Frank Brockners},

url = {https://www.epizeuxis.net/wp-content/uploads/2023/06/2023009866.pdf},

year  = {2023},

date = {2023-05-08},

urldate = {2023-05-08},

booktitle = {Proc. IEEE/IFIP Network Operations and Management Symposium 2023},

publisher = {IEEE},

abstract = {Expert systems for fault diagnosis are computationally expensive to build and maintain, and lack scalability and inherent adaptability to unknown events or modifications in the topology of the monitored system. While data-driven feature se- lection mechanisms can facilitate diagnosis without the hardship of developing and maintaining expert systems, purely data-driven mechanisms lack understanding of semantic importance within a feature set, and would benefit from additional domain knowledge. Part of this additional knowledge can be extracted from meta-data. The proposed approach combines data-driven metrics and semantic information contained in the feature names to produce selections of features which best represent an underlying event. This study extends a cross entropy based optimization method to join semantic importance with data behavior. A benchmarking architecture is introduced to evaluate the benefits of semantic analysis, and demonstrate the performance and robustness of semantic feature selection on different types of faults in network telemetry datasets, modeled with the YANG data modeling language. The results illustrate the interest of such a complementary meta-data analysis for data-driven fault diagnosis, and highlight the robustness of the studied approach against variations in the input feature set.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Feltin2020,

title = {Semantic feature selection for network telemetry event description},

author = {Thomas Feltin and Parisa Foroughi and Wenqin Shao and Frank Brockners and Thomas Clausen},

url = {https://www.thomasclausen.net/wp-content/uploads/2020/08/AnNet20201-1.pdf},

doi = {10.1109/NOMS47738.2020.9110382},

isbn = {2374-9709},

year  = {2020},

date = {2020-04-20},

booktitle = {NOMS 2020 - 2020 IEEE/IFIP Network Operations and Management Symposium},

pages = {1-6},

abstract = {Model driven telemetry (MDT) enables the real-time collection of hundreds of thousands of counters on large-scale networks, with contextual information to each counter provided in the telemetry data structure definition. Explaining network events in such datasets implies substantial analysis by a domain expert. This paper presents an semantic feature selection method, to find the most important counters which describe a given event in a telemetry dataset, and facilitate the explanation process. This paper proposes a metric for estimating the importance of features in a dataset with descriptive feature names, to find those that are most meaningful to a human. With this estimation, this paper presents a cross-entropy based metric describing the quality of a selection of counters, which is combined with the data behavior to define an optimization goal. The computation of optimal selections distills intelligible and precise selections of counters with adjustable verbosity, and describes events with a few selected counters outlining the root cause of network events.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}