<div class="csl-bib-body">
<div class="csl-entry">Hartl, A., Iglesias Vazquez, F., & Zseby, T. (2024). dSalmon: High-Speed Anomaly Detection for Evolving Multivariate Data Streams. In E. Kalyvianaki & M. Paolieri (Eds.), <i>Performance Evaluation Methodologies and Tools: 16th EAI International Conference, VALUETOOLS 2023, Crete, Greece, September 6–7, 2023, Proceedings</i> (pp. 153–169). Springer Cham. https://doi.org/10.1007/978-3-031-48885-6_10</div>
</div>
-
dc.identifier.uri
http://hdl.handle.net/20.500.12708/192231
-
dc.description.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
dc.description.sponsorship
FFG - Österr. Forschungsförderungs- gesellschaft mbH
-
dc.language.iso
en
-
dc.relation.ispartofseries
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
-
dc.subject
outlier detection
en
dc.subject
data streams
en
dc.subject
unsupervised learning
en
dc.subject
python
en
dc.subject
c++
en
dc.title
dSalmon: High-Speed Anomaly Detection for Evolving Multivariate Data Streams
en
dc.type
Inproceedings
en
dc.type
Konferenzbeitrag
de
dc.contributor.editoraffiliation
The Alan Turing Institute, United Kingdom of Great Britain and Northern Ireland (the)
-
dc.contributor.editoraffiliation
University of Southern California, United States of America (the)
-
dc.relation.isbn
978-3-031-48885-6
-
dc.relation.doi
10.1007/978-3-031-48885-6
-
dc.relation.issn
1867-8211
-
dc.description.startpage
153
-
dc.description.endpage
169
-
dc.relation.grantno
873511
-
dc.type.category
Full-Paper Contribution
-
dc.relation.eissn
1867-822X
-
tuw.booktitle
Performance Evaluation Methodologies and Tools: 16th EAI International Conference, VALUETOOLS 2023, Crete, Greece, September 6–7, 2023, Proceedings
-
tuw.container.volume
539
-
tuw.peerreviewed
true
-
tuw.relation.publisher
Springer Cham
-
tuw.project.title
MALware cOmmunication in cRitical Infrastructures
-
tuw.researchTopic.id
I1
-
tuw.researchTopic.id
C4
-
tuw.researchTopic.id
I4
-
tuw.researchTopic.name
Logic and Computation
-
tuw.researchTopic.name
Mathematical and Algorithmic Foundations
-
tuw.researchTopic.name
Information Systems Engineering
-
tuw.researchTopic.value
30
-
tuw.researchTopic.value
40
-
tuw.researchTopic.value
30
-
tuw.publication.orgunit
E389-01 - Forschungsbereich Networks
-
tuw.publisher.doi
10.1007/978-3-031-48885-6_10
-
dc.description.numberOfPages
17
-
tuw.author.orcid
0000-0003-4376-9605
-
tuw.author.orcid
0000-0001-6081-969X
-
tuw.author.orcid
0000-0002-5391-467X
-
tuw.editor.orcid
0000-0003-0753-1261
-
tuw.editor.orcid
0000-0001-5110-203X
-
tuw.event.name
16th EAI International Conference, VALUETOOLS 2023
en
tuw.event.startdate
06-09-2023
-
tuw.event.enddate
07-09-2023
-
tuw.event.online
On Site
-
tuw.event.type
Event for scientific audience
-
tuw.event.place
Crete
-
tuw.event.country
GR
-
tuw.event.presenter
Hartl, Alexander
-
tuw.event.track
Single Track
-
wb.sciencebranch
Informatik
-
wb.sciencebranch
Elektrotechnik, Elektronik, Informationstechnik
-
wb.sciencebranch
Mathematik
-
wb.sciencebranch.oefos
1020
-
wb.sciencebranch.oefos
2020
-
wb.sciencebranch.oefos
1010
-
wb.sciencebranch.value
60
-
wb.sciencebranch.value
20
-
wb.sciencebranch.value
20
-
item.languageiso639-1
en
-
item.cerifentitytype
Publications
-
item.openairecristype
http://purl.org/coar/resource_type/c_5794
-
item.openairetype
conference paper
-
item.grantfulltext
restricted
-
item.fulltext
no Fulltext
-
crisitem.project.funder
FFG - Österr. Forschungsförderungs- gesellschaft mbH
