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Johansson, R., Hammar, P., Karlsson, A. & Rydström, S. (2023). Avskanning av dataanalys och AI – Rapport år 2023. Stockholm: Totalförsvarets forskningsinstitut (FOI)
Open this publication in new window or tab >>Avskanning av dataanalys och AI – Rapport år 2023
2023 (Swedish)Report (Other academic)
Alternative title[en]
Horizon Scanning of Data Analysis and AI – Report in 2023
Abstract [sv]

Dataanalys och det närbesläktade området artificiell intelligens utvecklas snabbt och är av stort intresse för ledningsområdet då de kan erbjuda åtråvärda egenskaper som automatiserad hantering av stora datamängder och modeller för att tolka ny data. Den här rapporten omfattar en avskanning av fyra utvalda delområden: preskriptiv analys, osäkerhetshantering, förklarbarhet (XAI) och systemperspektiv. Rapporten innehåller dessutom en uppskattning av den framtida utvecklingen av delområdena och en jämförelse med avseende på deras mognadsgrad och relevans.

Abstract [en]

Data analysis and the closely related field of artificial intelligence are developing rapidly and are of great interest to the command and control field as they can offer desirable features such as automated handling of large data sets and models for interpreting new data. This report includes a scan of four selected sub-areas: prescriptive analysis, uncertainty management, explainability (XAI) and systems perspective. The report also contains an estimate of the future development of the sub-areas and a comparison with respect to their degree of maturity and relevance.

Place, publisher, year, edition, pages
Stockholm: Totalförsvarets forskningsinstitut (FOI), 2023. p. 60
Series
Totalförsvarets forskningsinstitut-rapport (FOI-R), ISSN 1650-1942 ; FOI-R--5483--SE
Keywords
artificiell intelligens (AI), dataanalys, avskanning, preskriptiv dataanalys, osäkerhetshantering, förklarbarhet (XAI), artificial intelligence (AI), data analysis, horizon scanning, prescriptive data analysis, uncertainty management, explainable AI (XAI)
National Category
Other Computer and Information Science
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-23324 (URN)
Note

Ronnie Johansson (red.), Peter Hammar, Alexander Karlsson, Sidney Rydström

Available from: 2023-10-23 Created: 2023-10-23 Last updated: 2023-11-13Bibliographically approved
Vellenga, K., Karlsson, A., Steinhauer, H. J., Falkman, G. & Sjögren, A. (2023). Surrogate Deep Learning to Estimate Uncertainties for Driver Intention Recognition. In: ICMLC 2023: Proceedings of 2023 15th International Conference on Machine Learning and Computing, Zhuhai, China, February 17-20, 2023. Paper presented at 15th International Conference on Machine Learning and Computing, Zhuhai, China, February 17-20, 2023 (pp. 252-258). New York, NY, USA: Association for Computing Machinery (ACM)
Open this publication in new window or tab >>Surrogate Deep Learning to Estimate Uncertainties for Driver Intention Recognition
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2023 (English)In: ICMLC 2023: Proceedings of 2023 15th International Conference on Machine Learning and Computing, Zhuhai, China, February 17-20, 2023, New York, NY, USA: Association for Computing Machinery (ACM), 2023, p. 252-258Conference paper, Published paper (Refereed)
Abstract [en]

Real-world applications of artificial intelligence that can potentially harm human beings should be able to express uncertainty about the made predictions. Probabilistic deep learning (DL) methods (e.g., variational inference [VI], VI last layer [VI-LL], Monte-Carlo [MC] dropout, stochastic weight averaging - Gaussian [SWA-G], and deep ensembles) can produce a predictive uncertainty but require expensive MC sampling techniques. Therefore, we evaluated if the probabilistic DL methods are uncertain when making incorrect predictions for an open-source driver intention recognition dataset and if a surrogate DL model can reproduce the uncertainty estimates. We found that all probabilistic DL methods are significantly more uncertain when making incorrect predictions at test time, but there are still instances where the models are very certain but completely incorrect. The surrogate DL models trained on the MC dropout and VI uncertainty estimates were capable of reproducing a significantly higher uncertainty estimate when making incorrect predictions.

Place, publisher, year, edition, pages
New York, NY, USA: Association for Computing Machinery (ACM), 2023
Keywords
Driver intention recognition, probabilistic deep learning, surrogate modeling, uncertainty quantification
National Category
Information Systems Other Computer and Information Science
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-22851 (URN)10.1145/3587716.3587758 (DOI)2-s2.0-85173817744 (Scopus ID)978-1-4503-9841-1 (ISBN)
Conference
15th International Conference on Machine Learning and Computing, Zhuhai, China, February 17-20, 2023
Projects
Intention recognition for real-time automotive 3D situation awareness
Funder
Vinnova, 2018-05012
Note

CC BY-NC-SA 4.0

CORRESPONDING AUTHOR: K. VELLENGA (e-mail: koen.vellenga@volvocars.com)

This work was supported by the Intention Recognition in Real Time for Automotive 3D Situation Awareness (IRRA) Project (https://www.vinnova.se/p/intention-recognition-i-realtid-for-automotive-3d-situation-awareness-irra/).

Available from: 2023-06-27 Created: 2023-06-27 Last updated: 2023-10-26Bibliographically approved
Steinhauer, H. J., Helldin, T., Mathiason, G. & Karlsson, A. (2023). Topic modeling for anomaly detection in telecommunication networks. Journal of Ambient Intelligence and Humanized Computing, 14(11), 15085-15096
Open this publication in new window or tab >>Topic modeling for anomaly detection in telecommunication networks
2023 (English)In: Journal of Ambient Intelligence and Humanized Computing, ISSN 1868-5137, E-ISSN 1868-5145, Vol. 14, no 11, p. 15085-15096Article in journal (Refereed) Published
Abstract [en]

To ensure reliable network performance, anomaly detection is an important part of the telecommunication operators’ work. This includes that operators need to timely intervene with the network, should they encounter indications of network performance degradation. In this paper, we describe the results of an initial experiment for anomaly detection with regard to network performance, using topic modeling on base station run-time variable data collected from live Radio Access Networks (RANs). The results show that topic modeling clusters semantically related data in the same way as human experts would and that the anomalies in our test cases could be identified in latent Dirichlet allocation (LDA) topic models. Our experiment further reveals which information provided by the topic model is particularly usable to support human anomaly detection in this application domain.

Place, publisher, year, edition, pages
Springer, 2023
Keywords
Telecommunication anomaly detection, Topic modeling, Decision-making
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-17527 (URN)10.1007/s12652-019-01372-5 (DOI)2-s2.0-85182305640 (Scopus ID)
Projects
bison
Funder
University of SkövdeKnowledge Foundation
Note

CC BY 4.0

Received: 31 January 2019 / Accepted: 18 June 2019 / Published online: 2 August 2019

H. Joe Steinhauer joe.steinhauer@his.se

Open access funding provided by University of Skövde. This work was supported by the Swedish Knowledge Foundation under grant BISON—Big Data Fusion—in cooperation with Huawei Technologies Sweden AB. We would like to thank Anders Åhlén for sharing his knowledge throughout our work. The topic modeling was performed using the package topicmodels (Grün and Hornik 2011) in R (R Core Team 2017), and the LDAvis visualization was enabled by Sievert and Shirley (2014).

Available from: 2019-08-13 Created: 2019-08-13 Last updated: 2024-01-26Bibliographically approved
Vellenga, K., Steinhauer, H. J., Karlsson, A., Falkman, G., Rhodin, A. & Koppisetty, A. C. (2022). Driver intention recognition: state-of-the-art review. IEEE Open Journal of Intelligent Transportation Systems, 3, 602-616
Open this publication in new window or tab >>Driver intention recognition: state-of-the-art review
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2022 (English)In: IEEE Open Journal of Intelligent Transportation Systems, E-ISSN 2687-7813, Vol. 3, p. 602-616Article, review/survey (Refereed) Published
Abstract [en]

Every year worldwide more than one million people die and a further 50 million people are injured in traffic accidents. Therefore, the development of car safety features that actively support the driver in preventing accidents, is of utmost importance to reduce the number of injuries and fatalities. However, to establish this support it is necessary that the advanced driver assistance system (ADAS) understands the driver’s intended behavior in advance. The growing variety of sensors available for vehicles together with improved computer vision techniques, hence led to increased research directed towards inferring the driver’s intentions. This article reviews 64 driver intention recognition studies with regard to the maneuvers considered, the driving features included, the AI methods utilized, the achieved performance within the presented experiments, and the open challenges identified by the respected researchers. The article provides a high level analysis of the current technology readiness level of driver intention recognition technology to address the challenges to enable reliable driver intention recognition, such as the system architecture, implementation, and the purpose of the technology.

Place, publisher, year, edition, pages
IEEE, 2022
Keywords
Driver intentions, intention recognition, driver behavior, driving maneuvers
National Category
Computer Systems
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-21812 (URN)10.1109/ojits.2022.3197296 (DOI)000853832800001 ()2-s2.0-85147393634 (Scopus ID)
Projects
Intention recognition for real-time automotive 3D situation awareness
Funder
Vinnova, 2018-05012
Note

CC BY-NC-ND 4.0

CORRESPONDING AUTHOR: K. VELLENGA (e-mail: koen.vellenga@volvocars.com)

This work was supported by the Intention Recognition in Real Time for Automotive 3D Situation Awareness (IRRA) Project (https://www.vinnova.se/p/intention-recognition-i-realtid-for-automotive-3d-situation-awareness-irra/).

Available from: 2022-09-12 Created: 2022-09-12 Last updated: 2023-02-16Bibliographically approved
Johansson, R., Karlsson, A., Andler, S. F., Brohede, M., van Laere, J., Klingegård (Nilsson), M. & Ziemke, T. (2022). On the Definition and Scope of Information Fusion as a Field of Research. ISIF Perspectives on Information Fusion, 5(1), 3-12
Open this publication in new window or tab >>On the Definition and Scope of Information Fusion as a Field of Research
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2022 (English)In: ISIF Perspectives on Information Fusion, ISSN 2831-4824, Vol. 5, no 1, p. 3-12Article in journal (Refereed) Published
Abstract [en]

A definition of information fusion (IF) as a field of research can benefit researchers within the field, who may use such a definition when motivating their own work and evaluating the contributions of others. Moreover, it can enable researchers and practitioners outside the field to more easily relate their own work to the field and more easily understand the scope of IF techniques and methods. Based on strengths and weaknesses of existing definitions, a definition is proposed that is argued to effectively fulfill the requirements that can be put on a definition of IF as a field of research. Although the proposed definition aims to be precise, it does not fully capture the richness and versatility of the IF field. To address that limitation, we highlight some topics to explore the scope of IF, covering the systems perspective of IF and its relation to ma-chine learning, optimization, robot behavior, opinion aggregation, and databases.

Place, publisher, year, edition, pages
International Society of Information Fusion (ISIF), 2022
National Category
Computer Sciences Robotics Information Systems
Research subject
Skövde Artificial Intelligence Lab (SAIL); Distributed Real-Time Systems; Information Systems
Identifiers
urn:nbn:se:his:diva-21740 (URN)
Note

Abstracting is permitted with credit to the source. For all other copying, reprint, or republication permissions, contact the Administrative Editor. Copyright© 2022 ISIF, Inc.

Available from: 2022-08-30 Created: 2022-08-30 Last updated: 2023-09-25Bibliographically approved
Rajashekarappa, M., Lené, J., Bekar, E. T., Skoogh, A. & Karlsson, A. (2021). A Data-Driven Approach to Air Leakage Detection in Pneumatic Systems. In: Wei Guo; Steven Li (Ed.), 2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing): October 15-17, 2021 in Nanjing, China. Paper presented at 12th IEEE Global Reliability and Prognostics and Health Management, PHM-Nanjing 2021, Nanjing 15 October 2021 through 17 October 2021, Code 174772. IEEE
Open this publication in new window or tab >>A Data-Driven Approach to Air Leakage Detection in Pneumatic Systems
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2021 (English)In: 2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing): October 15-17, 2021 in Nanjing, China / [ed] Wei Guo; Steven Li, IEEE, 2021Conference paper, Published paper (Refereed)
Abstract [en]

During the transition phase of traditional manufacturing companies towards smart factories, they are likely to experience challenges like lack of prehistoric data recordings or events on which the machine learning models need to be trained. This paper introduces a novel approach of artificially induced anomalies for data labelling. Moreover, for newly installed systems or a setup, which has not seen any kind of malfunction yet, the combination of artificially induced anomalies by experiments and machine learning model help to proactively prepare for any kind of future hindrance of the production systems. Two experiments were performed for detection of air leakage. The first one was designed to identify 'sensitive feature' and understand the behaviour of the sensor readings with respect to different state of the machine. The second one was performed to capture more data points pertaining to leaking state of machine on a normal production day since the first one was conducted on a maintenance break). RUSBoosted bagged trees model was built as a supervised machine-learning model, which was yielded 98.73% accuracy, 99.40% precision, recall of 99.21%, and F1 score of 99.30% on test data for detecting pneumatic leakage. As a conclusion, previously unknown hidden patterns and insights regarding temperature feature along with a standardized and systematic methodology are the important deliverables of this study. 

Place, publisher, year, edition, pages
IEEE, 2021
Keywords
Artificial anomalies, Data labelling, Data-driven decision-making, Machine learning, Pneumatic leakage, Predictive maintenance, Pneumatics, Supervised learning, Trees (mathematics), Air leakage, Artificial anomaly, Data driven decision, Data-driven approach, Decisions makings, Machine learning models, Decision making
National Category
Computer Sciences Other Computer and Information Science Robotics
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-20891 (URN)10.1109/PHM-Nanjing52125.2021.9612973 (DOI)2-s2.0-85123450051 (Scopus ID)978-1-6654-0131-9 (ISBN)978-1-6654-0130-2 (ISBN)978-1-6654-2979-5 (ISBN)
Conference
12th IEEE Global Reliability and Prognostics and Health Management, PHM-Nanjing 2021, Nanjing 15 October 2021 through 17 October 2021, Code 174772
Funder
Vinnova, 201900789
Note

© 2021 IEEE.

This paper has been produced from the Master thesis study of the first and second authors at Chalmers University of Technology. The authors would like to thank the Production 2030 Strategic Innovation Program funded by VINNOVA for their funding of the research project PACA-Predictive Maintenance using Advanced Cluster Analysis (Grant No. 201900789), which this study has been conducted. Thanks also to Thomas Sundqvist, Jonas Vallström, and Robert Andersson Jarl for their guidance and support with the real-time data from a real-world manufacturing system. This study has been conducted within Production Area of Advance at the Chalmers University of Technology.

Available from: 2022-02-03 Created: 2022-02-03 Last updated: 2022-04-22Bibliographically approved
Ulfenborg, B., Karlsson, A., Riveiro, M., Andersson, C. X., Sartipy, P. & Synnergren, J. (2021). Multi-Assignment Clustering: Machine learning from a biological perspective. Journal of Biotechnology, 326, 1-10
Open this publication in new window or tab >>Multi-Assignment Clustering: Machine learning from a biological perspective
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2021 (English)In: Journal of Biotechnology, ISSN 0168-1656, E-ISSN 1873-4863, Vol. 326, p. 1-10Article in journal (Refereed) Published
Abstract [en]

A common approach for analyzing large-scale molecular data is to cluster objects sharing similar characteristics. This assumes that genes with highly similar expression profiles are likely participating in a common molecular process. Biological systems are extremely complex and challenging to understand, with proteins having multiple functions that sometimes need to be activated or expressed in a time-dependent manner. Thus, the strategies applied for clustering of these molecules into groups are of key importance for translation of data to biologically interpretable findings. Here we implemented a multi-assignment clustering (MAsC) approach that allows molecules to be assigned to multiple clusters, rather than single ones as in commonly used clustering techniques. When applied to high-throughput transcriptomics data, MAsC increased power of the downstream pathway analysis and allowed identification of pathways with high biological relevance to the experimental setting and the biological systems studied. Multi-assignment clustering also reduced noise in the clustering partition by excluding genes with a low correlation to all of the resulting clusters. Together, these findings suggest that our methodology facilitates translation of large-scale molecular data into biological knowledge. The method is made available as an R package on GitLab (https://gitlab.com/wolftower/masc).

Place, publisher, year, edition, pages
Elsevier, 2021
Keywords
Clustering, K-means, annotation enrichment, multiple cluster assignment, pathways, transcriptomics
National Category
Bioinformatics and Systems Biology
Research subject
Bioinformatics; Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-19329 (URN)10.1016/j.jbiotec.2020.12.002 (DOI)000616124700001 ()33285150 (PubMedID)2-s2.0-85097644109 (Scopus ID)
Note

CC BY 4.0

Available from: 2020-12-16 Created: 2020-12-16 Last updated: 2021-03-11Bibliographically approved
Karlsson, A., Bekar, E. T. & Skoogh, A. (2021). Multi-Machine Gaussian Topic Modeling for Predictive Maintenance. IEEE Access, 9, 100063-100080
Open this publication in new window or tab >>Multi-Machine Gaussian Topic Modeling for Predictive Maintenance
2021 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 9, p. 100063-100080Article in journal (Refereed) Published
Abstract [en]

In this paper, we propose a coherent framework for multi-machine analysis, using a group clustering model, which can be utilized for predictive maintenance (PdM). The framework benefits from the repetitive structure posed by multiple machines and enables for assessment of health condition, degradation modeling and comparison of machines. It is based on a hierarchical probabilistic model, denoted Gaussian topic model (GTM), where cluster patterns are shared over machines and therefore it allows one to directly obtain proportions of patterns over the machines. This is then used as a basis for cross comparison between machines where identified similarities and differences can lead to important insights about their degradation behaviors. The framework is based on aggregation of data over multiple streams by a predefined set of features extracted over a time window. Moreover, the framework contains a clustering schema which takes uncertainty of cluster assignments into account and where one can specify a desirable degree of reliability of the assignments. By using a multi-machine simulation example, we highlight how the framework can be utilized in order to obtain cluster patterns and inherent variations of such patterns over machines. Furthermore, a comparative study with the commonly used Gaussian mixture model (GMM) demonstrates that GTM is able to identify inherent patterns in the data while the GMM fails. Such result is a consequence of the group level being modeled by the GTM while being absent in the GMM. Hence, the GTM are trained with a view on the data that is not available to the GMM with the consequence that the GMM can miss important, possibly even key cluster patterns. Therefore, we argue that more advanced cluster models, like the GTM, can be key for interpreting and understanding degradation behavior across machines and ultimately for obtaining more efficient and reliable PdM systems.

Place, publisher, year, edition, pages
IEEE, 2021
Keywords
exploratory data analysis, cluster analysis, Gaussian topic modeling, hierarchical modeling, multi-machine analysis, multiple data streams, predictive maintenance
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-19969 (URN)10.1109/ACCESS.2021.3096387 (DOI)000675190900001 ()2-s2.0-85110894784 (Scopus ID)
Projects
Predictive Maintenance using Advanced Cluster Analysis (PACA)
Funder
Vinnova, 2019-00789
Note

CC BY 4.0

Corresponding author: Alexander Karlsson (alexander.karlsson@his.se)

This work was supported by grant 2019-00789 at Vinnova, Project: Predictive Maintenance using Advanced Cluster Analysis (PACA).

Available from: 2021-06-24 Created: 2021-06-24 Last updated: 2021-10-29Bibliographically approved
Ståhl, N., Falkman, G., Karlsson, A. & Mathiason, G. (2020). Evaluation of Uncertainty Quantification in Deep Learning. In: Marie-Jeanne Lesot, Susana Vieira, Marek Z. Reformat, João Paulo Carvalho, Anna Wilbik, Bernadette Bouchon-Meunier, Ronald R. Yager (Ed.), Marie-Jeanne Lesot, Susana Vieira, Marek Z. Reformat, João Paulo Carvalho, Anna Wilbik, Bernadette Bouchon-Meunier, Ronald R. Yager (Ed.), Information Processing and Management of Uncertainty in Knowledge-Based Systems: 18th International Conference, IPMU 2020, Lisbon, Portugal, June 15–19, 2020, Proceedings, Part I. Paper presented at 18th International Conference, IPMU 2020, Lisbon, Portugal, June 15–19, 2020 (pp. 556-568). Cham: Springer
Open this publication in new window or tab >>Evaluation of Uncertainty Quantification in Deep Learning
2020 (English)In: Information Processing and Management of Uncertainty in Knowledge-Based Systems: 18th International Conference, IPMU 2020, Lisbon, Portugal, June 15–19, 2020, Proceedings, Part I / [ed] Marie-Jeanne Lesot, Susana Vieira, Marek Z. Reformat, João Paulo Carvalho, Anna Wilbik, Bernadette Bouchon-Meunier, Ronald R. Yager, Cham: Springer, 2020, p. 556-568Conference paper, Published paper (Refereed)
Abstract [en]

Artificial intelligence (AI) is nowadays included into an increasing number of critical systems. Inclusion of AI in such systems may, however, pose a risk, since it is, still, infeasible to build AI systems that know how to function well in situations that differ greatly from what the AI has seen before. Therefore, it is crucial that future AI systems have the ability to not only function well in known domains, but also understand and show when they are uncertain when facing something unknown. In this paper, we evaluate four different methods that have been proposed to correctly quantifying uncertainty when the AI model is faced with new samples. We investigate the behaviour of these models when they are applied to samples far from what these models have seen before, and if they correctly attribute those samples with high uncertainty. We also examine if incorrectly classified samples are attributed with an higher uncertainty than correctly classified samples. The major finding from this simple experiment is, surprisingly, that the evaluated methods capture the uncertainty differently and the correlation between the quantified uncertainty of the models is low. This inconsistency is something that needs to be further understood and solved before AI can be used in critical applications in a trustworthy and safe manner. © 2020, Springer Nature Switzerland AG.

Place, publisher, year, edition, pages
Cham: Springer, 2020
Series
Communications in Computer and Information Science, ISSN 1865-0929, E-ISSN 1865-0937 ; 1237
Keywords
Function evaluation, Information management, Knowledge based systems, Technology transfer, Uncertainty analysis, AI systems, Critical applications, Critical systems, Uncertainty quantifications, Deep learning
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-18555 (URN)10.1007/978-3-030-50146-4_41 (DOI)2-s2.0-85086272108 (Scopus ID)978-3-030-50145-7 (ISBN)978-3-030-50146-4 (ISBN)
Conference
18th International Conference, IPMU 2020, Lisbon, Portugal, June 15–19, 2020
Available from: 2020-06-18 Created: 2020-06-18 Last updated: 2021-04-19Bibliographically approved
Steinhauer, H. J., Åhlén, A., Helldin, T., Karlsson, A. & Mathiason, G. (2020). Increased Network Monitoring Support through Topic Modeling. International Journal of Information, Communication Technology and Applications, 6(1)
Open this publication in new window or tab >>Increased Network Monitoring Support through Topic Modeling
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2020 (English)In: International Journal of Information, Communication Technology and Applications, E-ISSN 2205-0930, Vol. 6, no 1Article in journal (Refereed) Published
Abstract [en]

To ensure that a wireless telecommunication system is reliably functioning at all times, root-causes of potential network failures need to be identified and remedied, ideally before a noticeable network performance degradation occurs. Network operators are today observing a multitude of key performance indicators (KPIs) and are notified of possible network problems through alarms issued by different parts of the network. However, the number of cascading alarms together with the number of observable KPIs are easily overwhelming the operator’s cognitive capacity. In this paper we show how exploratory data analysis and machine learning, in particular topic modelling, can assist the operator when monitoring network performance and identifying anomalous network behaviour as well as supporting the operator’s analysis of the anomaly and identification of its root-cause. 

Place, publisher, year, edition, pages
Australasian Association for Information and Communication Technology, 2020
Keywords
topic modelling, exploratory data analysis, anomaly detection, root cause identification, telecommunication networks, network performance monitoring
National Category
Computer and Information Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-19532 (URN)
Funder
Knowledge Foundation
Note

CC BY-NC-ND 4.0

Copyright © Australasian Association for Information and Communication Technology General permission to republish, but not for profit, all or part of this material is granted, under the Creative Commons Australian Attribution-NonCommercial-NoDerivs 4.0 Licence, provided that the copyright notice is given and that reference is made to the publication, to its date of issue, and to the fact that reprinting privileges were granted by permission of the Copyright holder.

Available from: 2021-03-12 Created: 2021-03-12 Last updated: 2021-04-26Bibliographically approved
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Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-2973-3112

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