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| Fältnamn | Indikatorer | Metadata |
|---|---|---|
| 000 | 03297naa a2200433 4500 | |
| 001 | oai:DiVA.org:bth-18026 | |
| 003 | SwePub | |
| 008 | 190614s2020 | |||||||||||000 ||eng| | |
| 024 | 7 | a https://urn.kb.se/resolve?urn=urn:nbn:se:bth-180262 URI |
| 024 | 7 | a https://doi.org/10.1007/s10115-019-01365-y2 DOI |
| 040 | a (SwePub)bth | |
| 041 | a engb eng | |
| 042 | 9 SwePub | |
| 072 | 7 | a ref2 swepub-contenttype |
| 072 | 7 | a art2 swepub-publicationtype |
| 100 | 1 | a Boldt, Martinu Blekinge Tekniska Högskola,Institutionen för datavetenskap4 aut0 (Swepub:bth)mbo |
| 245 | 1 0 | a Anomaly detection of event sequences using multiple temporal resolutions and Markov chains |
| 264 | c 2019-05-15 | |
| 264 | 1 | b Springer London,c 2020 |
| 338 | a electronic2 rdacarrier | |
| 500 | a open access | |
| 520 | a Streaming data services, such as video-on-demand, are getting increasingly more popular, and they are expected to account for more than 80% of all Internet traffic in 2020. In this context, it is important for streaming service providers to detect deviations in service requests due to issues or changing end-user behaviors in order to ensure that end-users experience high quality in the provided service. Therefore, in this study we investigate to what extent sequence-based Markov models can be used for anomaly detection by means of the end-users’ control sequences in the video streams, i.e., event sequences such as play, pause, resume and stop. This anomaly detection approach is further investigated over three different temporal resolutions in the data, more specifically: 1 h, 1 day and 3 days. The proposed anomaly detection approach supports anomaly detection in ongoing streaming sessions as it recalculates the probability for a specific session to be anomalous for each new streaming control event that is received. Two experiments are used for measuring the potential of the approach, which gives promising results in terms of precision, recall, F 1 -score and Jaccard index when compared to k-means clustering of the sessions. © 2019, The Author(s). | |
| 650 | 7 | a NATURVETENSKAPx Data- och informationsvetenskapx Datavetenskap0 (SwePub)102012 hsv//swe |
| 650 | 7 | a NATURAL SCIENCESx Computer and Information Sciencesx Computer Sciences0 (SwePub)102012 hsv//eng |
| 653 | a Anomaly detection | |
| 653 | a Event sequences | |
| 653 | a Markov Chains | |
| 653 | a Multiple temporal resolutions | |
| 653 | a Video-on-demand | |
| 700 | 1 | a Borg, Antonu Blekinge Tekniska Högskola,Institutionen för datavetenskap4 aut0 (Swepub:bth)atb |
| 700 | 1 | a Ickin, Selimu Ericsson Research, SWE4 aut |
| 700 | 1 | a Gustafsson, Jörgenu Ericsson Research, SWE4 aut |
| 710 | 2 | a Blekinge Tekniska Högskolab Institutionen för datavetenskap4 org |
| 773 | 0 | t Knowledge and Information Systemsd : Springer Londong 62, s. 669-686q 62<669-686x 0219-1377x 0219-3116 |
| 856 | 4 | u https://doi.org/10.1007/s10115-019-01365-yy Fulltext |
| 856 | 4 | u https://bth.diva-portal.org/smash/get/diva2:1324926/FULLTEXT01.pdfx primaryx Raw objecty fulltext:print |
| 856 | 4 | u https://link.springer.com/content/pdf/10.1007/s10115-019-01365-y.pdf |
| 856 | 4 8 | u https://urn.kb.se/resolve?urn=urn:nbn:se:bth-18026 |
| 856 | 4 8 | u https://doi.org/10.1007/s10115-019-01365-y |
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