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Publications (10 of 46) Show all publications
Holst, A., Bouguelia, M.-R. -., Görnerup, O., Pashami, S., Al-Shishtawy, A., Falkman, G., . . . Soliman, A. (2019). Eliciting structure in data. In: CEUR Workshop Proceedings: . Paper presented at 2019 Joint ACM IUI Workshops, ACMIUI-WS 2019, 20 March 2019.
Open this publication in new window or tab >>Eliciting structure in data
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2019 (English)In: CEUR Workshop Proceedings, 2019Conference paper, Published paper (Refereed)
Abstract [en]

This paper demonstrates how to explore and visualize different types of structure in data, including clusters, anomalies, causal relations, and higher order relations. The methods are developed with the goal of being as automatic as possible and applicable to massive, streaming, and distributed data. Finally, a decentralized learning scheme is discussed, enabling finding structure in the data without collecting the data centrally. © 2019 Copyright held for the individual papers by the papers’ authors. Copying permitted for private and academic purposes. This volume is published and copyrighted by its editors.

Keywords
Anomaly detection, Causal inference, Clustering, Distributed analytics, Higher-order structure, Information visualization, Information systems, User interfaces, Causal inferences, Data acquisition
National Category
Natural Sciences
Identifiers
urn:nbn:se:ri:diva-38261 (URN)2-s2.0-85063227224 (Scopus ID)
Conference
2019 Joint ACM IUI Workshops, ACMIUI-WS 2019, 20 March 2019
Available from: 2019-04-02 Created: 2019-04-02 Last updated: 2019-04-02Bibliographically approved
Holst, A., Pashami, S. & Bae, J. (2019). Incremental causal discovery and visualization. In: Proceedings of the Workshop on Interactive Data Mining, WIDM 2019: . Paper presented at 1st Workshop on Interactive Data Mining, WIDM 2019, co-located with 12th ACM International Conference on Web Search and Data Mining, WSDM 2019, 15 February 2019. Association for Computing Machinery, Inc
Open this publication in new window or tab >>Incremental causal discovery and visualization
2019 (English)In: Proceedings of the Workshop on Interactive Data Mining, WIDM 2019, Association for Computing Machinery, Inc , 2019Conference paper, Published paper (Refereed)
Abstract [en]

Discovering causal relations from limited amounts of data can be useful for many applications. However, all causal discovery algorithms need huge amounts of data to estimate the underlying causal graph. To alleviate this gap, this paper proposes a novel visualization tool which incrementally discovers causal relations as more data becomes available. That is, we assume that stronger causal links will be detected quickly and weaker links revealed when enough data is available. In addition to causal links, the correlation between variables and the uncertainty of the strength of causal links are visualized in the same graph. The tool is illustrated on three example causal graphs, and results show that incremental discovery works and that the causal structure converges as more data becomes available. © 2019 Copyright held by the owner/author(s).

Place, publisher, year, edition, pages
Association for Computing Machinery, Inc, 2019
Keywords
Causal Discovery, Correlation, Incremental Visualization, Correlation methods, Data mining, Visualization, Causal graph, Causal relations, Discovery algorithm, Incremental discoveries, Novel visualizations, Data visualization
National Category
Natural Sciences
Identifiers
urn:nbn:se:ri:diva-39672 (URN)10.1145/3304079.3310287 (DOI)2-s2.0-85069768142 (Scopus ID)9781450362962 (ISBN)
Conference
1st Workshop on Interactive Data Mining, WIDM 2019, co-located with 12th ACM International Conference on Web Search and Data Mining, WSDM 2019, 15 February 2019
Note

Funding text 1: This research has been conducted within the “A Big Data Analytics Framework for a Smart Society" (BIDAF) project supported by the Swedish Knowledge Foundation.

Available from: 2019-08-07 Created: 2019-08-07 Last updated: 2019-08-07Bibliographically approved
Holst, A., Karlsson, A., Bae, J. & Bouguelia, M.-R. (2019). Interactive clustering for exploring multiple data streams at different time scales and granularity. In: Proceedings of the Workshop on Interactive Data Mining, WIDM 2019: . Paper presented at 1st Workshop on Interactive Data Mining, WIDM 2019, co-located with 12th ACM International Conference on Web Search and Data Mining, WSDM 2019, 15 February 2019. Association for Computing Machinery, Inc
Open this publication in new window or tab >>Interactive clustering for exploring multiple data streams at different time scales and granularity
2019 (English)In: Proceedings of the Workshop on Interactive Data Mining, WIDM 2019, Association for Computing Machinery, Inc , 2019Conference paper, Published paper (Refereed)
Abstract [en]

We approach the problem of identifying and interpreting clusters over different time scales and granularity in multivariate time series data. We extract statistical features over a sliding window of each time series, and then use a Gaussian mixture model to identify clusters which are then projected back on the data streams. The human analyst can then further analyze this projection and adjust the size of the sliding window and the number of clusters in order to capture the different types of clusters over different time scales. We demonstrate the effectiveness of our approach in two different application scenarios: (1) fleet management and (2) district heating, wherein each scenario, several different types of meaningful clusters can be identified when varying over these dimensions. © 2019 Copyright held by the owner/author(s).

Place, publisher, year, edition, pages
Association for Computing Machinery, Inc, 2019
Keywords
Clustering, Interaction, Time scales, Time series, Fleet operations, Gaussian distribution, Time measurement, Application scenario, Different time scale, Gaussian Mixture Model, Multiple data streams, Multivariate time series, Time-scales, Data mining
National Category
Natural Sciences
Identifiers
urn:nbn:se:ri:diva-39673 (URN)10.1145/3304079.3310286 (DOI)2-s2.0-85069762696 (Scopus ID)9781450362962 (ISBN)
Conference
1st Workshop on Interactive Data Mining, WIDM 2019, co-located with 12th ACM International Conference on Web Search and Data Mining, WSDM 2019, 15 February 2019
Available from: 2019-08-07 Created: 2019-08-07 Last updated: 2019-08-07Bibliographically approved
Bouguelia, M.-R., Karlsson, A., Pashami, S., Nowaczyk, S. & Holst, A. (2018). Mode tracking using multiple data streams. Information Fusion, 43, 33-46
Open this publication in new window or tab >>Mode tracking using multiple data streams
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2018 (English)In: Information Fusion, ISSN 1566-2535, E-ISSN 1872-6305, Vol. 43, p. 33-46Article in journal (Refereed) Published
Abstract [en]

Most existing work in information fusion focuses on combining information with well-defined meaning towards a concrete, pre-specified goal. In contradistinction, we instead aim for autonomous discovery of high-level knowledge from ubiquitous data streams. This paper introduces a method for recognition and tracking of hidden conceptual modes, which are essential to fully understand the operation of complex environments, and an important step towards building truly intelligent aware systems. We consider a scenario of analyzing usage of a fleet of city buses, where the objective is to automatically discover and track modes such as highway route, heavy traffic, or aggressive driver, based on available on-board signals. The method we propose is based on aggregating the data over time, since the high-level modes are only apparent in the longer perspective. We search through different features and subsets of the data, and identify those that lead to good clusterings, interpreting those clusters as initial, rough models of the prospective modes. We utilize Bayesian tracking in order to continuously improve the parameters of those models, based on the new data, while at the same time following how the modes evolve over time. Experiments with artificial data of varying degrees of complexity, as well as on real-world datasets, prove the effectiveness of the proposed method in accurately discovering the modes and in identifying which one best explains the current observations from multiple data streams.

Keywords
Clustering, Data streams, Knowledge discovery, Mode tracking, Time series, Data mining, Information fusion, Software engineering, Complex environments, Data stream, Degrees of complexity, High level knowledge, Multiple data streams, Real-world datasets, Fleet operations
National Category
Natural Sciences
Identifiers
urn:nbn:se:ri:diva-33228 (URN)10.1016/j.inffus.2017.11.011 (DOI)2-s2.0-85037072003 (Scopus ID)
Available from: 2018-02-12 Created: 2018-02-12 Last updated: 2018-08-16Bibliographically approved
Olsson, T. & Holst, A. (2015). A Probabilistic Approach to Aggregating Anomalies for Unsupervised Anomaly Detection with Industrial Applications (7ed.). In: Proceedings of the 28th International Florida Artificial Intelligence Research Society Conference (FLAIRS 2015): . Paper presented at 28th International Florida Artificial Intelligence Research Society Conference (FLAIRS 2015), May 18-20, 2015, Hollywood, US (pp. 434-439).
Open this publication in new window or tab >>A Probabilistic Approach to Aggregating Anomalies for Unsupervised Anomaly Detection with Industrial Applications
2015 (English)In: Proceedings of the 28th International Florida Artificial Intelligence Research Society Conference (FLAIRS 2015), 2015, 7, p. 434-439Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a novel, unsupervised approach to detecting anomalies at the collective level. The method probabilistically aggregates the contribution of the individual anomalies in order to detect significantly anomalous groups of cases. The approach is unsupervised in that as only input, it uses a list of cases ranked according to its individual anomaly score. Thus, any anomaly detection algorithm can be used for scoring individual anomalies, both supervised and unsupervised approaches. The applicability of the proposed approach is shown by applying it to an artificial data set and to two industrial data sets — detecting anomalously moving cranes (model-based detection) and anomalous fuel consumption (neighbour-based detection).

National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:ri:diva-24430 (URN)2-s2.0-84958181138 (Scopus ID)9781577357308 (ISBN)
Conference
28th International Florida Artificial Intelligence Research Society Conference (FLAIRS 2015), May 18-20, 2015, Hollywood, US
Projects
STREAM
Available from: 2016-10-31 Created: 2016-10-31 Last updated: 2019-08-08Bibliographically approved
Bohlin, M., Holst, A., Ekman, J., Sellin, O., Lindström, B. & Larsen, S. (2012). Statistical Anomaly Detection for Train Fleets (9ed.). In: : . Paper presented at Proceedings of the 21st Innovative Applications of Artificial Intelligence Conference. Toronto, Canada
Open this publication in new window or tab >>Statistical Anomaly Detection for Train Fleets
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2012 (English)Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Toronto, Canada: , 2012 Edition: 9
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:ri:diva-24031 (URN)
Conference
Proceedings of the 21st Innovative Applications of Artificial Intelligence Conference
Projects
DUST
Note

Accepted for publication.

Available from: 2016-10-31 Created: 2016-10-31 Last updated: 2018-08-20Bibliographically approved
Holst, A. (2011). Analys och prediktion av slitage på hjulprofiler och bromsbelägg på tåg (6ed.). Kista, Sweden: Swedish Institute of Computer Science
Open this publication in new window or tab >>Analys och prediktion av slitage på hjulprofiler och bromsbelägg på tåg
2011 (Swedish)Report (Other academic)
Abstract [sv]

Vi har som en del av det Vinnova-finansierade projektet DUST undersökt hur Bayesiansk statistisk modellering och avvikelsedetektion kan användas för att analysera slitage på hjulprofiler och bromsbelägg på tåg. Vi visar hur man med denna analys kan filtrera data, upptäcka onormalt slitage, och förutsäga när det är dags för underhåll. Resultaten visar att de föreslagna metoderna fungerar mycket bra för analys av den typ av tidsseriedata med trender som det handlar om här, och att det går att få ut ganska mycket trots att data är relativt få och brusiga.

Place, publisher, year, edition, pages
Kista, Sweden: Swedish Institute of Computer Science, 2011 Edition: 6
Series
SICS Technical Report, ISSN 1100-3154 ; 2011:14
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:ri:diva-23916 (URN)
Projects
DUST
Available from: 2016-10-31 Created: 2016-10-31 Last updated: 2018-08-16Bibliographically approved
Nelson, D. W., Thornquist, B., MacCallum, R. M., Holst, A., Nyström, H., Rudehill, A., . . . Bellander, B.-M. (2011). Analysis of cerebral microdialysis in patients with traumatic brain injury; relations to intracranial pressure, cerebral perfusion pressure and catheter placement. (8ed.). BMC Medicine, 9
Open this publication in new window or tab >>Analysis of cerebral microdialysis in patients with traumatic brain injury; relations to intracranial pressure, cerebral perfusion pressure and catheter placement.
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2011 (English)In: BMC Medicine, Vol. 9Article in journal (Refereed) Published
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:ri:diva-23991 (URN)
Available from: 2016-10-31 Created: 2016-10-31 Last updated: 2018-08-16Bibliographically approved
Ekman, J., Holst, A. & Jonasson, A. (2011). Condition based maintenance of trains doors (7ed.). Kista, Sweden: Swedish Institute of Computer Science
Open this publication in new window or tab >>Condition based maintenance of trains doors
2011 (English)Report (Other academic)
Abstract [en]

As part of the project DUST financed by Vinnova, we have investigated whether event data generated on trains can be used for finding evidence of wear on train doors. We have compared the event data and maintenance reports relating to doors of Regina trains. Although some interesting relations were found, the overall result is that the information in event data about wear of doors is very limited.

Place, publisher, year, edition, pages
Kista, Sweden: Swedish Institute of Computer Science, 2011 Edition: 7
Series
SICS Technical Report, ISSN 1100-3154 ; 2011:15
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:ri:diva-24012 (URN)
Projects
DUST
Available from: 2016-10-31 Created: 2016-10-31 Last updated: 2018-08-20Bibliographically approved
Holst, A. & Ekman, J. (2011). Incremental stream clustering for anomaly detection and classification. (7ed.). In: : . Paper presented at Eleventh Scandinavian Conference on Artificial Intelligence, SCAI 2011 (pp. 100-107).
Open this publication in new window or tab >>Incremental stream clustering for anomaly detection and classification.
2011 (English)Conference paper, Published paper (Refereed)
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:ri:diva-23998 (URN)
Conference
Eleventh Scandinavian Conference on Artificial Intelligence, SCAI 2011
Projects
Anomaly detection
Note

In Kofod-Petersen A., Heintz F. and Langseth H. (eds): Eleventh Scandinavian Conference on Artificial Intelligence, SCAI 2011. IOS Press, 2011.

Available from: 2016-10-31 Created: 2016-10-31 Last updated: 2018-08-20Bibliographically approved
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-8577-6745

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