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Bae, J., Karlsson, A., Mellin, J., Ståhl, N. & Torra, V. (2019). Complex Data Analysis. In: Alan Said, Vicenç Torra (Ed.), Data science in Practice: (pp. 157-169). Springer
Open this publication in new window or tab >>Complex Data Analysis
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2019 (English)In: Data science in Practice / [ed] Alan Said, Vicenç Torra, Springer, 2019, p. 157-169Chapter in book (Refereed)
Abstract [en]

Data science applications often need to deal with data that does not fit into the standard entity-attribute-value model. In this chapter we discuss three of these other types of data. We discuss texts, images and graphs. The importance of social media is one of the reason for the interest on graphs as they are a way to represent social networks and, in general, any type of interaction between people. In this chapter we present examples of tools that can be used to extract information and, thus, analyze these three types of data. In particular, we discuss topic modeling using a hierarchical statistical model as a way to extract relevant topics from texts, image analysis using convolutional neural networks, and measures and visual methods to summarize information from graphs.

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); Distributed Real-Time Systems
Identifiers
urn:nbn:se:his:diva-16811 (URN)10.1007/978-3-319-97556-6_9 (DOI)000464719500010 ()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
Mellin, J. (2016). Systematic Generation of Risk Evaluation Systems basedon Temporal Motivational Theory. In: Vicenc Torra (Ed.), USB Proceedings of the 13th Workshop on Modeling Decisions for Artifical Intelligence  (MDAI2016): . Paper presented at Modeling Decisions for Artificial Intelligence (pp. 122-132).
Open this publication in new window or tab >>Systematic Generation of Risk Evaluation Systems basedon Temporal Motivational Theory
2016 (English)In: USB Proceedings of the 13th Workshop on Modeling Decisions for Artifical Intelligence  (MDAI2016) / [ed] Vicenc Torra, 2016, p. 122-132Conference paper, Published paper (Refereed)
Abstract [en]

This paper provides a schematic, systematic and structured approach todeveloping Bayesian belief networks to assess risks in contexts dened by activities.The method ameliorates elicitation, specication and validation of expert knowledgeby reusing a schematic structures based on reasoning of risks based on the temporal motivationaltheory. The method is based on earlier work that took a rst signicant steptowards reducing the complexity of development of Bayesian belief networks by clusteringand classifying variables in Bayesian belief networks as well as associating the processwith human deciions making. It may be possible to reduce the role of a facilitiatoror even remove the facilitator altogether by using this method. The method is partiallyvalidated and further work is required on this topic.

Keywords
Bayesian belief networks, elicitation, specication, validation, temporal motivational theory
National Category
Computer Sciences
Research subject
Technology; Distributed Real-Time Systems
Identifiers
urn:nbn:se:his:diva-12941 (URN)978-99920-3-099-8 (ISBN)
Conference
Modeling Decisions for Artificial Intelligence
Available from: 2016-09-21 Created: 2016-09-21 Last updated: 2019-01-22Bibliographically approved
Steinhauer, H. J. & Mellin, J. (2015). Automatic Early Risk Detection of Possible Medical Conditions for Usage Within an AMI-System. In: Amr Mohamed, Paulo Novais, António Pereira, Gabriel Villarrubia González, Antonio Fernández-Caballero (Ed.), Ambient Intelligence - Software and Applications: . Paper presented at 6th International Symposium on Ambient Intelligence (ISAmI 2015) (pp. 13-21). Springer Berlin/Heidelberg
Open this publication in new window or tab >>Automatic Early Risk Detection of Possible Medical Conditions for Usage Within an AMI-System
2015 (English)In: Ambient Intelligence - Software and Applications / [ed] Amr Mohamed, Paulo Novais, António Pereira, Gabriel Villarrubia González, Antonio Fernández-Caballero, Springer Berlin/Heidelberg, 2015, p. 13-21Conference paper, Published paper (Refereed)
Abstract [en]

Using hyperglycemia as an example, we present how Bayesian networks can be utilized for automatic early detection of a person’s possible medical risks based on information provided by un obtrusive sensors in their living environments. The network’s outcome can be used as a basis on which an automated AMI-system decides whether to interact with the person, their caregiver, or any other appropriate party. The networks’ design is established through expert elicitation and validated using a half-automated validation process that allows the medical expert to specify validation rules. To interpret the networks’ results we use an output dictionary which is automatically generated for each individual network and translates the output probability into the different risk classes (e.g.,no risk, risk).

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2015
Series
Advances in Intelligent Systems and Computing, ISSN 2194-5357 ; 376
Keywords
Ambient Assisted Living, Bayesian networks, Automated Diagnosis
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); Distributed Real-Time Systems
Identifiers
urn:nbn:se:his:diva-11171 (URN)10.1007/978-3-319-19695-4_2 (DOI)2-s2.0-84937501569 (Scopus ID)9783319196947 (ISBN)
Conference
6th International Symposium on Ambient Intelligence (ISAmI 2015)
Projects
Helicopter
Available from: 2015-08-06 Created: 2015-06-18 Last updated: 2018-03-28Bibliographically approved
Mellin, J. & Andler, S. F. (2015). The effect of optimizing engine control on fuel consumption and roll amplitude in ocean-going vessels: An experimental study. Skövde
Open this publication in new window or tab >>The effect of optimizing engine control on fuel consumption and roll amplitude in ocean-going vessels: An experimental study
2015 (English)Report (Other academic)
Abstract [en]

We use data-generated models based on data from experiments of an ocean-going vessel to study the effect of optimizing fuel consumption. The optimization is an add-on module to the existing diesel-engine fuel-injection control built by Q-TAGG R&D AB. The work is mainly a validation of knowledge-based models based on a priori knowledge from physics. The results from a simulation-based analysis of the predictive models built on data agree with the results based on knowledge-based models in a companion study. This indicates that the optimization algorithm saves fuel. We also address specific problems of adapting data to existing machine learning methods. It turns out that we can simplify the problem by ignoring the auto-correlative effects in the time series by employing low-pass filters and resampling techniques. Thereby we can use mature and robust classification techniques with less requirements on the data to demonstrate that fuel is saved compared to the full-fledged time series analysis techniques which are harder to use. The trade-off is the accuracy of the result, that is, it is hard to tell exactly how much fuel is saved. In essence, however, this process can be automated due to its simplicity. 

Place, publisher, year, edition, pages
Skövde: , 2015. p. 74
National Category
Signal Processing Embedded Systems Computer Sciences Probability Theory and Statistics
Research subject
Technology; Distributed Real-Time Systems
Identifiers
urn:nbn:se:his:diva-10942 (URN)
Projects
“System för bränslebesparing på stora fartyg”, 2013-00301, Vinnova Forska & Väx 2013
Funder
Vinnova, 2013-00301
Available from: 2015-05-18 Created: 2015-05-18 Last updated: 2019-11-12Bibliographically approved
Pozzer, C., Amorim, J. A., Gustavsson, P. M., Mellin, J., Heldal, I. & Azevedo, A. T. (2014). Imprecise Computation as an Enabler for Complex and Time Critical HLA Simulation Networks. In: Proceedings of Simulation Interoperability Workshop: . Paper presented at Fall Simulation Interoperability Workshop, 2014 Fall SIW; Orlando; United States; 8 September 2014 through 12 September 2014 (pp. 171-179).
Open this publication in new window or tab >>Imprecise Computation as an Enabler for Complex and Time Critical HLA Simulation Networks
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2014 (English)In: Proceedings of Simulation Interoperability Workshop, 2014, p. 171-179Conference paper, Published paper (Refereed)
Abstract [en]

A trend over the past years is that simulation systems for training are being connected in simulation networks, allowing the interaction of teams spread in distributed sites. By combining interconnected simulation systems the simulation complexity increases and may affect time-critical simulation tasks in a negative way. As a consequence, the training simulation objectives may not be met. The same problem may occur when performing, for example, mission rehearsal on site, since available computation resources are usually very limited in this scenario, or for a joint fires scenario, where the large and complex functional chain (including intelligence, C2, forward observer, pilots, etc.) may overload existing resources. In this work, the technique of imprecise computation in real-time systems (ICRS) to preserve time-critical simulation tasks is presented. The ICRS technique allows time-critical tasks to produce quicker solutions for approximate results and saves computational resources. This paper discusses the main advantages of theICRS technique by a review of the commonly used optimization concepts built upon imprecise computation field. Thepaper ends with presenting a work-in-progress: an architectural solution for aligning ICRS with the High Level Architecture (HLA), standardized as the IEEE 1516-series.

Keywords
scheduling for imprecise computation, HLA, simulation, joint fires, real-time systems, integration
National Category
Computer Systems
Research subject
Technology; Distributed Real-Time Systems; Interaction Lab (ILAB)
Identifiers
urn:nbn:se:his:diva-9891 (URN)2-s2.0-84910115603 (Scopus ID)9781634393898 (ISBN)
Conference
Fall Simulation Interoperability Workshop, 2014 Fall SIW; Orlando; United States; 8 September 2014 through 12 September 2014
Projects
Agent-orient large-scale complex virtual environments
Available from: 2014-09-04 Created: 2014-09-04 Last updated: 2023-01-03Bibliographically approved
Mellin, J., Pozzer, C., Heldal, I. & Gustavsson, P. M. (2014). Using Imprecise Computation for Virtual and Constructive Simulations. In: A. Tolk, S. Y. Diallo, I. O. Ryzhov, L. Yilmaz, S. Buckley, & J. A. Miller (Ed.), Proceedings of the 2014 Winter Simulation Conference: . Paper presented at 2014 Winter Simulation Conference (WSC), December 7-10, 2014, Savannah, USA (pp. 4043-4044). IEEE Press
Open this publication in new window or tab >>Using Imprecise Computation for Virtual and Constructive Simulations
2014 (English)In: Proceedings of the 2014 Winter Simulation Conference / [ed] A. Tolk, S. Y. Diallo, I. O. Ryzhov, L. Yilmaz, S. Buckley, & J. A. Miller, IEEE Press, 2014, p. 4043-4044Conference paper, Poster (with or without abstract) (Refereed)
Abstract [en]

In this work, we raise three critical questions that must be investigated to ameliorate composability ofvirtual simulation models and to enable adoption of systematic and stringent real-time techniques toenable more scalable simulation models for virtual and constructive simulation. The real-time techniquesin question enable us to separate between policies and mechanisms and, thus, the simulation engine candecide dynamically how to run the simulation given the existing resources (e.g., processor) and the goalsof the simulation (e.g., sufficient fidelity in terms of timing and accuracy). The three critical questionsare: (i) how to design efficient and effective algorithms for making dynamic simulation model designdecisions during simulation; (ii) how to map simulation entities (e.g., agents) into (real-time) tasks; and(iii) how to enable a divide and conquer approach to validating simulation models.

Place, publisher, year, edition, pages
IEEE Press, 2014
Keywords
virtual simulation, constructive simulation, scalability, agent-oriented, resource management
National Category
Computer Sciences
Research subject
Natural sciences; Distributed Real-Time Systems; Interaction Lab (ILAB)
Identifiers
urn:nbn:se:his:diva-10470 (URN)978-1-4799-7486-3 (ISBN)
Conference
2014 Winter Simulation Conference (WSC), December 7-10, 2014, Savannah, USA
Available from: 2014-12-21 Created: 2014-12-21 Last updated: 2023-01-03Bibliographically approved
Berndtsson, M. & Mellin, J. (2009). Active Database, Active Database (Management) System. In: Ling Liu, M. Tamer Özsu (Ed.), Encyclopedia of Database Systems: (pp. 27-28). Springer Science+Business Media B.V.
Open this publication in new window or tab >>Active Database, Active Database (Management) System
2009 (English)In: Encyclopedia of Database Systems / [ed] Ling Liu, M. Tamer Özsu, Springer Science+Business Media B.V., 2009, p. 27-28Chapter in book (Other academic)
Place, publisher, year, edition, pages
Springer Science+Business Media B.V., 2009
National Category
Computer and Information Sciences
Research subject
Technology
Identifiers
urn:nbn:se:his:diva-7035 (URN)10.1007/978-0-387-39940-9_502 (DOI)978-0-387-49616-0 (ISBN)978-0-387-35544-3 (ISBN)978-0-387-39940-9 (ISBN)
Available from: 2013-02-06 Created: 2013-01-23 Last updated: 2023-06-16Bibliographically approved
Berndtsson, M. & Mellin, J. (2009). Active Database Coupling Modes. In: Ling Liu, M. Tamer Özsu (Ed.), Encyclopedia of Database Systems: (pp. 33-35). Springer Science+Business Media B.V.
Open this publication in new window or tab >>Active Database Coupling Modes
2009 (English)In: Encyclopedia of Database Systems / [ed] Ling Liu, M. Tamer Özsu, Springer Science+Business Media B.V., 2009, p. 33-35Chapter in book (Other academic)
Place, publisher, year, edition, pages
Springer Science+Business Media B.V., 2009
National Category
Computer and Information Sciences
Research subject
Technology
Identifiers
urn:nbn:se:his:diva-7037 (URN)10.1007/978-0-387-39940-9_503 (DOI)978-0-387-49616-0 (ISBN)978-0-387-35544-3 (ISBN)978-0-387-39940-9 (ISBN)
Available from: 2013-02-06 Created: 2013-01-23 Last updated: 2023-06-16Bibliographically approved
Berndtsson, M. & Mellin, J. (2009). Active Database Execution Model. In: Ling Liu, M. Tamer Özsu (Ed.), Encyclopediia of Database Systems: (pp. 35-36). Springer Science+Business Media B.V.
Open this publication in new window or tab >>Active Database Execution Model
2009 (English)In: Encyclopediia of Database Systems / [ed] Ling Liu, M. Tamer Özsu, Springer Science+Business Media B.V., 2009, p. 35-36Chapter in book (Other academic)
Place, publisher, year, edition, pages
Springer Science+Business Media B.V., 2009
National Category
Computer and Information Sciences
Research subject
Technology
Identifiers
urn:nbn:se:his:diva-7044 (URN)10.1007/978-0-387-39940-9_509 (DOI)978-0-387-49616-0 (ISBN)978-0-387-35544-3 (ISBN)978-0-387-39940-9 (ISBN)
Available from: 2013-02-06 Created: 2013-01-24 Last updated: 2023-06-16Bibliographically approved
Berndtsson, M. & Mellin, J. (2009). Active Database Knowledge Model. In: Ling Liu, M. Tamer Özsu (Ed.), Encyclopedia of Database Systems: (pp. 36-37). Springer Science+Business Media B.V.
Open this publication in new window or tab >>Active Database Knowledge Model
2009 (English)In: Encyclopedia of Database Systems / [ed] Ling Liu, M. Tamer Özsu, Springer Science+Business Media B.V., 2009, p. 36-37Chapter in book (Other academic)
Place, publisher, year, edition, pages
Springer Science+Business Media B.V., 2009
National Category
Computer and Information Sciences
Research subject
Technology
Identifiers
urn:nbn:se:his:diva-7045 (URN)10.1007/978-0-387-39940-9_508 (DOI)978-0-387-49616-0 (ISBN)978-0-387-35544-3 (ISBN)978-0-387-39940-9 (ISBN)
Available from: 2013-02-06 Created: 2013-01-24 Last updated: 2023-06-16Bibliographically approved
Projects
HELICOPTER-HS [2013-03116_Vinnova]; University of Skövde
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-5223-4381

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