Record link: 
http://hdl.handle.net/20.500.12708/192231
-
Title: 
dSalmon: High-Speed Anomaly Detection for Evolving Multivariate Data Streams
en
Citation: 
Hartl, A., Iglesias Vazquez, F., & Zseby, T. (2024). dSalmon: High-Speed Anomaly Detection for Evolving Multivariate Data Streams. In E. Kalyvianaki & M. Paolieri (Eds.), Performance Evaluation Methodologies and Tools: 16th EAI International Conference, VALUETOOLS 2023, Crete, Greece, September 6–7, 2023, Proceedings (pp. 153–169). Springer Cham. https://doi.org/10.1007/978-3-031-48885-6_10
-
Publisher DOI: 
10.1007/978-3-031-48885-6_10
-
Publication Type: 
Inproceedings - Full-Paper Contribution
en
Language: 
English
-
Authors: 
Hartl, Alexander 
Iglesias Vazquez, Felix 
Zseby, Tanja 
-
Organisational Unit: 
E389-01 - Forschungsbereich Networks 
-
Published in: 
Performance Evaluation Methodologies and Tools: 16th EAI International Conference, VALUETOOLS 2023, Crete, Greece, September 6–7, 2023, Proceedings 
-
ISBN: 
978-3-031-48885-6
-
Volume: 
539
-
DOI of the book: 
10.1007/978-3-031-48885-6
-
Date (published): 
3-Jan-2024
-
Event name: 
16th EAI International Conference, VALUETOOLS 2023 
en
Event date: 
6-Sep-2023 - 7-Sep-2023
-
Event place: 
Crete, Greece
-
Number of Pages: 
17
-
Publisher: 
Springer Cham
-
Peer reviewed: 
Yes
-
Keywords: 
outlier detection; data streams; unsupervised learning; python; c++
en
Abstract: 
We introduce dSalmon, a highly efficient framework for outlier detection on streaming data. dSalmon can be used with both Python and C++, meeting the requirements of modern data science research. It provides an intuitive interface and has almost no package dependencies. dSalmon implements main stream outlier detection approaches from literature. By using pure C++ in its core and making the most of available parallelism, data is analyzed with superior processing speed.
en
Project title: 
MALware cOmmunication in cRitical Infrastructures: 873511 (FFG - Österr. Forschungsförderungs- gesellschaft mbH) 
-
Research Areas: 
Logic and Computation: 30%
Mathematical and Algorithmic Foundations: 40%
Information Systems Engineering: 30%
-
Science Branch: 
1020 - Informatik: 60%
2020 - Elektrotechnik, Elektronik, Informationstechnik: 20%
1010 - Mathematik: 20%
-
Appears in Collections:
Conference Paper

Show full item record

Page view(s)

52
checked on Jan 18, 2024

Download(s)

2
checked on Jan 18, 2024

Google ScholarTM

Check