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  1. Article ; Online: Human-in-the-Loop: Visual Analytics for Building Models Recognising Behavioural Patterns in Time Series.

    Andrienko, Natalia / Andrienko, Gennady / Artikis, Alexander / Mantenoglou, Periklis / Rinzivillo, Salvatore

    IEEE computer graphics and applications

    2024  Volume PP

    Abstract: Results of automated detection of complex patterns in temporal data, such as trajectories of moving objects, may be not good enough due to the use of strict pattern specifications derived from imprecise domain concepts. To address this challenge, we ... ...

    Abstract Results of automated detection of complex patterns in temporal data, such as trajectories of moving objects, may be not good enough due to the use of strict pattern specifications derived from imprecise domain concepts. To address this challenge, we propose a novel visual analytics approach that combines expert knowledge and automated pattern detection results to construct features that effectively distinguish patterns of interest from other types of behaviour. These features are then used to create interactive visualisations enabling a human analyst to generate labelled examples for building a feature-based pattern classifier. We evaluate our approach through a case study focused on detecting trawling activities in fishing vessel trajectories, demonstrating significant improvements in pattern recognition by leveraging domain knowledge and incorporating human reasoning and feedback. Our contribution is a novel framework that integrates human expertise and analytical reasoning with ML or AI techniques, advancing the field of data analytics.
    Language English
    Publishing date 2024-03-20
    Publishing country United States
    Document type Journal Article
    ISSN 1558-1756
    ISSN (online) 1558-1756
    DOI 10.1109/MCG.2024.3379851
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Visual Analytics for Human-Centered Machine Learning

    Andrienko, Natalia / Andrienko, Gennady / Adilova, Linara / Wrobel, Stefan

    2022  

    Abstract: 123 ... 133 ... We introduce a new research area in visual analytics (VA) aiming to bridge existing gaps between methods of interactive machine learning (ML) and eXplainable Artificial Intelligence (XAI), on one side, and human minds, on the other side. The ... ...

    Abstract 123

    133

    We introduce a new research area in visual analytics (VA) aiming to bridge existing gaps between methods of interactive machine learning (ML) and eXplainable Artificial Intelligence (XAI), on one side, and human minds, on the other side. The gaps are, first, a conceptual mismatch between ML/XAI outputs and human mental models and ways of reasoning, and second, a mismatch between the information quantity and level of detail and human capabilities to perceive and understand. A grand challenge is to adapt ML and XAI to human goals, concepts, values, and ways of thinking. Complementing the current efforts in XAI towards solving this challenge, VA can contribute by exploiting the potential of visualization as an effective way of communicating information to humans and a strong trigger of human abstractive perception and thinking. We propose a cross-disciplinary research framework and formulate research directions for VA.

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    Keywords Computer science ; Human intelligence ; Machine learning ; Visual analytics
    Subject code 401
    Language English
    Publishing date 2022-01-25
    Publishing country de
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: Co-Bridges: Pair-wise Visual Connection and Comparison for Multi-item Data Streams.

    Chen, Siming / Andrienko, Natalia / Andrienko, Gennady / Li, Jie / Yuan, Xiaoru

    IEEE transactions on visualization and computer graphics

    2021  Volume 27, Issue 2, Page(s) 1612–1622

    Abstract: In various domains, there are abundant streams or sequences of multi-item data of various kinds, e.g. streams of news and social media texts, sequences of genes and sports events, etc. Comparison is an important and general task in data analysis. For ... ...

    Abstract In various domains, there are abundant streams or sequences of multi-item data of various kinds, e.g. streams of news and social media texts, sequences of genes and sports events, etc. Comparison is an important and general task in data analysis. For comparing data streams involving multiple items (e.g., words in texts, actors or action types in action sequences, visited places in itineraries, etc.), we propose Co-Bridges, a visual design involving connection and comparison techniques that reveal similarities and differences between two streams. Co-Bridges use river and bridge metaphors, where two sides of a river represent data streams, and bridges connect temporally or sequentially aligned segments of streams. Commonalities and differences between these segments in terms of involvement of various items are shown on the bridges. Interactive query tools support the selection of particular stream subsets for focused exploration. The visualization supports both qualitative (common and distinct items) and quantitative (stream volume, amount of item involvement) comparisons. We further propose Comparison-of-Comparisons, in which two or more Co-Bridges corresponding to different selections are juxtaposed. We test the applicability of the Co-Bridges in different domains, including social media text streams and sports event sequences. We perform an evaluation of the users' capability to understand and use Co-Bridges. The results confirm that Co-Bridges is effective for supporting pair-wise visual comparisons in a wide range of applications.
    Language English
    Publishing date 2021-01-28
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 1941-0506
    ISSN (online) 1941-0506
    DOI 10.1109/TVCG.2020.3030411
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Visual Analytics for Human-Centered Machine Learning.

    Andrienko, Natalia / Andrienko, Gennady / Adilova, Linara / Wrobel, Stefan / Rhyne, Theresa-Marie

    IEEE computer graphics and applications

    2022  Volume 42, Issue 1, Page(s) 123–133

    Abstract: We introduce a new research area in visual analytics (VA) aiming to bridge existing gaps between methods of interactive machine learning (ML) and eXplainable Artificial Intelligence (XAI), on one side, and human minds, on the other side. The gaps are, ... ...

    Abstract We introduce a new research area in visual analytics (VA) aiming to bridge existing gaps between methods of interactive machine learning (ML) and eXplainable Artificial Intelligence (XAI), on one side, and human minds, on the other side. The gaps are, first, a conceptual mismatch between ML/XAI outputs and human mental models and ways of reasoning, and second, a mismatch between the information quantity and level of detail and human capabilities to perceive and understand. A grand challenge is to adapt ML and XAI to human goals, concepts, values, and ways of thinking. Complementing the current efforts in XAI towards solving this challenge, VA can contribute by exploiting the potential of visualization as an effective way of communicating information to humans and a strong trigger of human abstractive perception and thinking. We propose a cross-disciplinary research framework and formulate research directions for VA.
    MeSH term(s) Artificial Intelligence ; Humans ; Machine Learning
    Language English
    Publishing date 2022-03-14
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 1558-1756
    ISSN (online) 1558-1756
    DOI 10.1109/MCG.2021.3130314
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Supporting Visual Exploration of Iterative Job Scheduling.

    Andrienko, Gennady / Andrienko, Natalia / Garcia, Jose Manuel Cordero / Hecker, Dirk / Vouros, George A

    IEEE computer graphics and applications

    2022  Volume 42, Issue 3, Page(s) 74–86

    Abstract: We consider the general problem known as job shop scheduling, in which multiple jobs consist of sequential operations that need to be executed or served by appropriate machines having limited capacities. For example, train journeys (jobs) consist of ... ...

    Abstract We consider the general problem known as job shop scheduling, in which multiple jobs consist of sequential operations that need to be executed or served by appropriate machines having limited capacities. For example, train journeys (jobs) consist of moves and stops (operations) to be served by rail tracks and stations (machines). A schedule is an assignment of the job operations to machines and times where and when they will be executed. The developers of computational methods for job scheduling need tools enabling them to explore how their methods work. At a high level of generality, we define the system of pertinent exploration tasks and a combination of visualizations capable of supporting the tasks. We provide general descriptions of the purposes, contents, visual encoding, properties, and interactive facilities of the visualizations and illustrate them with images from an example implementation in air traffic management. We justify the design of the visualizations based on the tasks, principles of creating visualizations for pattern discovery, and scalability requirements. The outcomes of our research are sufficiently general to be of use in a variety of applications.
    MeSH term(s) Algorithms ; Personnel Staffing and Scheduling ; Workload
    Language English
    Publishing date 2022-06-07
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 1558-1756
    ISSN (online) 1558-1756
    DOI 10.1109/MCG.2022.3163437
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Book ; Conference proceedings: Geospatial visual analytics: focus on time

    Andrienko, Gennady

    special issue of the ICA Commission on GeoVisualization ; [... originates from a workshop organized by the International Cartographic Association Commission on GeoVisualization at the AGILE 2010 conference]

    (International journal of geographical information science ; 24.2010,10)

    2010  

    Event/congress AGILE conference (2010.05.10-11, Guimarães) ; Workshop. International Cartographic Association, Commission on GeoVisualization (2010.05.10-11, Guimarães)
    Author's details guest ed.: Gennady Andrienko
    Series title International journal of geographical information science ; 24.2010,10
    Language English
    Publisher Taylor & Francis
    Publishing place Abingdon
    Document type Book ; Conference proceedings
    Database Library catalogue of the German National Library of Science and Technology (TIB), Hannover

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  7. Book ; Conference proceedings: GeoVA(t) - geospatial visual analytics

    Andrienko, Gennady

    focus on time ; special issue of the ICA Commission on GeoVisualisation ; [... Workshop on GeoVA(t) organised in May 2010 at the AGILE conference]

    (Journal of location based services ; 4.2010,3/4)

    2010  

    Event/congress AGILE Conference (2010.05) ; Workshop on GeoVA(t) (2010.05)
    Author's details guest ed.: Gennady Andrienko
    Series title Journal of location based services ; 4.2010,3/4
    Language English
    Size S. 141 - 252, graph. Darst.
    Publisher Taylor & Francis
    Publishing place Abingdon
    Document type Book ; Conference proceedings
    Database Library catalogue of the German National Library of Science and Technology (TIB), Hannover

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  8. Book ; Conference proceedings: Special issue of the ICA Commission on Geovisualization on Geovisualization of Dynamics, Movement and Change

    Andrienko, Gennady

    [... meeting in Girona, Catalunya, which was held on 5 May 2008 ...]

    (Information visualization ; 7.2008,3/4)

    2008  

    Event/congress Meeting (2008.05.05, Girona)
    Author's details guest ed. Gennady Andrienko
    Series title Information visualization ; 7.2008,3/4
    Language English
    Size S. 1473 - 8716, graph. Darst
    Publisher Palgrave Macmillan
    Publishing place Basingstoke
    Document type Book ; Conference proceedings
    Database Library catalogue of the German National Library of Science and Technology (TIB), Hannover

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  9. Book ; Conference proceedings ; Online: Fifth International Conference on Coordinated and Multiple Views in Exploratory Visualization

    Andrienko, Gennady

    CMV '07 : 2 - 2 July 2007, Zürich, Switzerland ; associated and co-located with International Conference on Information Visualization (IV07)

    2007  

    Title variant Proceedings // International Conference on Coordinated & Multiple Views in Exploratory Visualization 2007
    Event/congress CMV 2007 (5, 2007.07.02, Zürich) ; IV 07 (5, 2007.07.02, Zürich) ; International Conference on Coordinated & Multiple Views in Exploratory Visualization (5, 2007.07.02, Zürich) ; International Conference on Information Visualisation (5, 2007.07.02, Zürich)
    Author's details ed. by Gennady Andrienko
    Keywords Computer graphics ; Computer vision ; Image processing ; Information display systems ; Information visualization
    Language English
    Size Online-Ressource
    Publisher IEEE Computer Society
    Publishing place Los Alamitos, Calif
    Document type Book ; Conference proceedings ; Online
    Note Includes bibliographical references and author index
    ISBN 0769529038 ; 9780769529035
    Database Library catalogue of the German National Library of Science and Technology (TIB), Hannover

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  10. Article ; Online: Explaining deep reinforcement learning decisions in complex multiagent settings: towards enabling automation in air traffic flow management.

    Kravaris, Theocharis / Lentzos, Konstantinos / Santipantakis, Georgios / Vouros, George A / Andrienko, Gennady / Andrienko, Natalia / Crook, Ian / Garcia, Jose Manuel Cordero / Martinez, Enrique Iglesias

    Applied intelligence (Dordrecht, Netherlands)

    2022  Volume 53, Issue 4, Page(s) 4063–4098

    Abstract: With the objective to enhance human performance and maximize engagement during the performance of tasks, we aim to advance automation for decision making in complex and large-scale multi-agent settings. Towards these goals, this paper presents a deep ... ...

    Abstract With the objective to enhance human performance and maximize engagement during the performance of tasks, we aim to advance automation for decision making in complex and large-scale multi-agent settings. Towards these goals, this paper presents a deep multi agent reinforcement learning method for resolving demand - capacity imbalances in real-world Air Traffic Management settings with thousands of agents. Agents comprising the system are able to jointly decide on the measures to be applied to resolve imbalances, while they provide explanations on their decisions: This information is rendered and explored via appropriate visual analytics tools. The paper presents how major challenges of scalability and complexity are addressed, and provides results from evaluation tests that show the abilities of models to provide high-quality solutions and high-fidelity explanations.
    Language English
    Publishing date 2022-06-06
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 1479519-X
    ISSN 1573-7497 ; 0924-669X
    ISSN (online) 1573-7497
    ISSN 0924-669X
    DOI 10.1007/s10489-022-03605-1
    Database MEDical Literature Analysis and Retrieval System OnLINE

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