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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
Koloseni, D., Helldin, T. & Torra, V. (2020). AHP-Like Matrices and Structures: Absolute and Relative Preferences. Mathematics, 8(5), Article ID 813.
Open this publication in new window or tab >>AHP-Like Matrices and Structures: Absolute and Relative Preferences
2020 (English)In: Mathematics, E-ISSN 2227-7390, Vol. 8, no 5, article id 813Article in journal (Refereed) Published
Abstract [en]

Aggregation functions are extensively used in decision making processes to combine available information. Arithmetic mean and weighted mean are some of the most used ones. In order to use a weighted mean, we need to define its weights. The Analytical Hierarchy Process (AHP) is a well known technique used to obtain weights based on interviews with experts. From the interviews we define a matrix of pairwise comparisons of the importance of the weights. We call these AHP-like matrices absolute preferences of weights. We propose another type of matrix that we call a relative preference matrix. We define this matrix with the same goal—to find the weights for weighted aggregators. We discuss how it can be used for eliciting the weights for the weighted mean and define a similar approach for the Choquet integral.

Place, publisher, year, edition, pages
MDPI, 2020
Keywords
aggregation functions, weight selection, fuzzy measures, AHP (Analytical Hierarchy Process)
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-18466 (URN)10.3390/math8050813 (DOI)000542738100193 ()2-s2.0-85086099761 (Scopus ID)
Available from: 2020-05-29 Created: 2020-05-29 Last updated: 2020-08-27Bibliographically 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
Bae, J., Helldin, T., Riveiro, M., Nowaczyk, S., Bouguelia, M.-R. & Falkman, G. (2020). Interactive clustering: A comprehensive review. ACM Computing Surveys, 53(1), Article ID 1.
Open this publication in new window or tab >>Interactive clustering: A comprehensive review
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2020 (English)In: ACM Computing Surveys, ISSN 0360-0300, E-ISSN 1557-7341, Vol. 53, no 1, article id 1Article in journal (Refereed) Published
Abstract [en]

In this survey, 105 papers related to interactive clustering were reviewed according to seven perspectives: (1) on what level is the interaction happening, (2) which interactive operations are involved, (3) how user feedback is incorporated, (4) how interactive clustering is evaluated, (5) which data and (6) which clustering methods have been used, and (7) what outlined challenges there are. This article serves as a comprehensive overview of the field and outlines the state of the art within the area as well as identifies challenges and future research needs.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2020
Keywords
Clustering, Evaluation, Feedback, Interaction, Interactive, User, Surveys, Computer science
National Category
Computer Sciences Human Computer Interaction
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-18266 (URN)10.1145/3340960 (DOI)000582585800001 ()2-s2.0-85079573488 (Scopus ID)
Note

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). © 2020 Copyright held by the owner/author(s).

Available from: 2020-02-28 Created: 2020-02-28 Last updated: 2020-11-17Bibliographically approved
Helldin, T., Bae, J. & Alklind Taylor, A.-S. (2019). Intelligent User Interfaces: Trends and application areas. Skövde: University of Skövde
Open this publication in new window or tab >>Intelligent User Interfaces: Trends and application areas
2019 (English)Report (Other academic)
Abstract [en]

This report outlines trends and application areas within the research field of intelligent user interfaces(IUIs) from 2010-2018. The purpose of the report is to give an overview of the IUI research area andpoint out particular subfields that have been given attention in the recent years, indicating possible trendsfor future research. Our report indicates that the field of IUIs is very broad, resulting in rather diverseresearch trends within the area. However, general trends could be identified, such as an increasing interest inbetter human-machine decision-making, where strategies for explaining the automatic reasoning are beinginvestigated together with ways of improving the trustworthiness of the systems and their possible adaptationsto individuals’ needs. The report also outlines research on multimodal interactions, adaptivity and humanrobotcollaboration, addressing challenges such as increased human workload, unobtrusiveness, privacy andmultiparty communication.

Place, publisher, year, edition, pages
Skövde: University of Skövde, 2019. p. 16
Series
IIT Technical Reports ; HS-IIT-TR-20-001
Keywords
Intelligent user interfaces, AI, HCI
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); Interaction Lab (ILAB)
Identifiers
urn:nbn:se:his:diva-18303 (URN)
Available from: 2020-03-12 Created: 2020-03-12 Last updated: 2020-06-18Bibliographically approved
Bae, J., Falkman, G., Helldin, T. & Riveiro, M. (2019). Visual Data Analysis. In: Alan Said, Vicenç Torra (Ed.), Data science in Practice: (pp. 133-155). Springer
Open this publication in new window or tab >>Visual Data Analysis
2019 (English)In: Data science in Practice / [ed] Alan Said, Vicenç Torra, Springer, 2019, p. 133-155Chapter in book (Refereed)
Abstract [en]

Data Science offers a set of powerful approaches for making new discoveries from large and complex data sets. It combines aspects of mathematics, statistics, machine learning, etc. to turn vast amounts of data into new insights and knowledge. However, the sole use of automatic data science techniques for large amounts of complex data limits the human user’s possibilities in the discovery process, since the user is estranged from the process of data exploration. This chapter describes the importance of Information Visualization (InfoVis) and visual analytics (VA) within data science and how interactive visualization can be used to support analysis and decision-making, empowering and complementing data science methods. Moreover, we review perceptual and cognitive aspects, together with design and evaluation methodologies for InfoVis and VA.

Place, publisher, year, edition, pages
Springer, 2019
Series
Studies in Big Data, ISSN 2197-6503, E-ISSN 2197-6511 ; 46
National Category
Computer and Information Sciences Computer Sciences Other Computer and Information Science
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-16810 (URN)10.1007/978-3-319-97556-6_8 (DOI)000464719500009 ()978-3-319-97556-6 (ISBN)978-3-319-97555-9 (ISBN)
Available from: 2019-04-24 Created: 2019-04-24 Last updated: 2020-06-18Bibliographically approved
Koloseni, D., Helldin, T. & Torra, V. (2018). Absolute and relative preferences in AHP-like matrices. In: Jun Liu, Jie Lu, Yang Xu, Luis Martinez, Etienne E Kerre (Ed.), Data Science and Knowledge Engineering for Sensing Decision Support: Proceedings of the 13th International FLINS Conference (FLINS 2018). Paper presented at Conference on Data Science and Knowledge Engineering for Sensing Decision Support (FLINS 2018), Belfast, United Kingdom, August 21-24, 2018 (pp. 260-267). SINGAPORE: World Scientific Publishing Co. Pte. Ltd., 11
Open this publication in new window or tab >>Absolute and relative preferences in AHP-like matrices
2018 (English)In: Data Science and Knowledge Engineering for Sensing Decision Support: Proceedings of the 13th International FLINS Conference (FLINS 2018) / [ed] Jun Liu, Jie Lu, Yang Xu, Luis Martinez, Etienne E Kerre, SINGAPORE: World Scientific Publishing Co. Pte. Ltd. , 2018, Vol. 11, p. 260-267Conference paper, Published paper (Refereed)
Abstract [en]

The Analytical Hierarchy Process (AHP) has been extensively used to interview experts in order to find the weights of the criteria. We call AHP-like matrices relative preferences of weights. In this paper we propose another type of matrix that we call a absolute preference matrix. They are also used to find weights, and we propose that they can be applied to find the weights of weighted means and also of the Choquet integral.

Place, publisher, year, edition, pages
SINGAPORE: World Scientific Publishing Co. Pte. Ltd., 2018
Series
World Scientific Proceedings Series on Computer Engineering and Information Science, ISSN 1793-7868 ; 11
National Category
Computer Sciences
Research subject
INF301 Data Science; Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-16409 (URN)10.1142/9789813273238_0035 (DOI)000468160600035 ()978-981-3273-22-1 (ISBN)978-981-3273-24-5 (ISBN)
Conference
Conference on Data Science and Knowledge Engineering for Sensing Decision Support (FLINS 2018), Belfast, United Kingdom, August 21-24, 2018
Available from: 2018-11-19 Created: 2018-11-19 Last updated: 2019-06-07Bibliographically approved
Steinhauer, H. J., Helldin, T., Mathiason, G. & Karlsson, A. (2018). Anomaly Detection in Telecommunication Networks using Topic Models. In: : . Paper presented at Modeling Decisions for Artificial Intelligence, 15th International Conference, MDAI 2018, Mallorca, Spain, October 15–18, 2018.
Open this publication in new window or tab >>Anomaly Detection in Telecommunication Networks using Topic Models
2018 (English)Conference paper, Published paper (Refereed)
National Category
Computer and Information Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
urn:nbn:se:his:diva-16491 (URN)
Conference
Modeling Decisions for Artificial Intelligence, 15th International Conference, MDAI 2018, Mallorca, Spain, October 15–18, 2018
Available from: 2018-12-12 Created: 2018-12-12 Last updated: 2019-06-13Bibliographically approved
Helldin, T., Steinhauer, H. J., Karlsson, A. & Mathiason, G. (2018). Situation Awareness in Telecommunication Networks Using Topic Modeling. In: 2018 21st International Conference on Information Fusion, FUSION 2018: . Paper presented at FUSION 2018 21st International Conference on Information Fusion, 10-13 July 2018, Cambridge, United Kingdom (pp. 549-556). IEEE
Open this publication in new window or tab >>Situation Awareness in Telecommunication Networks Using Topic Modeling
2018 (English)In: 2018 21st International Conference on Information Fusion, FUSION 2018, IEEE, 2018, p. 549-556Conference paper, Published paper (Refereed)
Abstract [en]

For an operator of wireless telecommunication networks to make timely interventions in the network before minor faults escalate into issues that can lead to substandard system performance, good situation awareness is of high importance. Due to the increasing complexity of such networks, as well as the explosion of traffic load, it has become necessary to aid human operators to reach a good level of situation awareness through the use of exploratory data analysis and information fusion techniques. However, to understand the results of such techniques is often cognitively challenging and time consuming. In this paper, we present how telecommunication operators can be aided in their data analysis and sense-making processes through the usage and visualization of topic modeling results. We present how topic modeling can be used to extract knowledge from base station counter readings and make design suggestions for how to visualize the analysis results to a telecommunication operator.

Place, publisher, year, edition, pages
IEEE, 2018
Keywords
Data handling, Data visualization, Information fusion, Design suggestions, Exploratory data analysis, Human operator, Information fusion techniques, Situation awareness, Telecommunication operators, Topic Modeling, Wireless telecommunications, Data mining
National Category
Computer and Information Sciences Computer Sciences Human Computer Interaction
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
urn:nbn:se:his:diva-16297 (URN)10.23919/ICIF.2018.8455529 (DOI)000495071900076 ()2-s2.0-85054066646 (Scopus ID)978-0-9964527-6-2 (ISBN)978-0-9964527-7-9 (ISBN)978-1-5386-4330-3 (ISBN)
Conference
FUSION 2018 21st International Conference on Information Fusion, 10-13 July 2018, Cambridge, United Kingdom
Available from: 2018-10-15 Created: 2018-10-15 Last updated: 2019-12-06Bibliographically approved
Steinhauer, H. J., Helldin, T. & Mathiason, G. (2018). Spatio-Temporal Awareness for Wireless Telecommunication Networks. In: Working Papers and Documents of the IJCAI-ECAI-2018 Workshop on Learning and Reasoning: Principles & Applications to Everyday Spatial and Temporal Knowledge. Paper presented at International Journal Conference for Artificial Intelligence (IJCAI), IJCAI-ECAI-2018 Workshop on Learning and Reasoning, July 13 - 14, 2018 Stockholm (Sweden) (pp. 49-50).
Open this publication in new window or tab >>Spatio-Temporal Awareness for Wireless Telecommunication Networks
2018 (English)In: Working Papers and Documents of the IJCAI-ECAI-2018 Workshop on Learning and Reasoning: Principles & Applications to Everyday Spatial and Temporal Knowledge, 2018, p. 49-50Conference paper, Published paper (Refereed)
National Category
Computer and Information Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
urn:nbn:se:his:diva-16490 (URN)
Conference
International Journal Conference for Artificial Intelligence (IJCAI), IJCAI-ECAI-2018 Workshop on Learning and Reasoning, July 13 - 14, 2018 Stockholm (Sweden)
Note

http://www.iiia.csic.es/LR2018/talks_proceedings

Available from: 2018-12-12 Created: 2018-12-12 Last updated: 2019-02-14Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-6245-5850

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