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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
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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. 42 1
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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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
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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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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
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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
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Book ; Article ; Online: Give more data, awareness and control to individual citizens, and they will help COVID-19 containment

Nanni, Mirco / Andrienko, Gennady / Barabási, Albert-László

2020

Abstract: The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the phase 2 of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in ... ...

Abstract	The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the phase 2 of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach, where data sensed by the app are all sent to a nation-wide server, raises concerns about citizens' privacy and needlessly strong digital surveillance, thus alerting us to the need to minimize personal data collection and avoiding location tracking. We advocate the conceptual advantage of a decentralized approach, where both contact and location data are collected exclusively in individual citizens' "personal data stores", to be shared separately and selectively, voluntarily, only when the citizen has tested positive for COVID-19, and with a privacy preserving level of granularity. This approach better protects the personal sphere of citizens and affords multiple benefits: it allows for detailed information gathering for infected people in a privacy-preserving fashion; and, in turn this enables both contact tracing, and, the early detection of outbreak hotspots on more finely-granulated geographic scale. Our recommendation is two-fold. First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allow the user to share spatio-temporal aggregates - if and when they want, for specific aims - with health authorities, for instance. Second, we favour a longer-term pursuit of realizing a Personal Data Store vision, giving users the opportunity to contribute to collective good in the measure they want, enhancing self-awareness, and cultivating collective efforts for rebuilding society.
Keywords	Corona ; 005 ; 006 ; 629
Subject code	303
Language	English
Publishing country	de
Document type	Book ; Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A theoretical model for pattern discovery in visual analytics

Andrienko, Natalia / Andrienko, Gennady / Miksch, Silvia / Schumann, Heidrun / Wrobel, Stefan

2021

Abstract: 23 ... 42 ... The word 'pattern' frequently appears in the visualisation and visual analytics literature, but what do we mean when we talk about patterns? We propose a practicable definition of the concept of a pattern in a data distribution as a combination ...

Abstract	23 42 The word 'pattern' frequently appears in the visualisation and visual analytics literature, but what do we mean when we talk about patterns? We propose a practicable definition of the concept of a pattern in a data distribution as a combination of multiple interrelated elements of two or more data components that can be represented and treated as a unified whole. Our theoretical model describes how patterns are made by relationships existing between data elements. Knowing the types of these relationships, it is possible to predict what kinds of patterns may exist. We demonstrate how our model underpins and refines the established fundamental principles of visualisation. The model also suggests a range of interactive analytical operations that can support visual analytics workflows where patterns, once discovered, are explicitly involved in further data analysis. 5 1
Keywords	Visual analytics ; Data distribution ; Pattern ; Abstraction ; Data arrangement ; Data organisation ; Data variation ; Pattern discovery ; DDC::000 Informatik ; Informationswissenschaft ; allgemeine Werke::000 Informatik ; Wissen ; Systeme::005 Computerprogrammierung ; Programme ; Daten ; Systeme::006 Spezielle Computerverfahren ; DDC::600 Technik ; Medizin ; angewandte Wissenschaften::620 Ingenieurwissenschaften::629 Andere Fachrichtungen der Ingenieurwissenschaften
Subject code	006
Language	English
Publishing date	2021-01-21
Publisher	Elsevier B.V.
Publishing country	de
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: The Flow of Trust: A Visualization Framework to Externalize, Explore, and Explain Trust in ML Applications.

Elzen, Stef van den / Andrienko, Gennady / Andrienko, Natalia / Fisher, Brian D / Martins, Rafael M / Peltonen, Jaakko / Telea, Alexandru C / Verleysen, Michel / Rhyne, Theresa-Marie

IEEE computer graphics and applications

2023 Volume 43, Issue 2, Page(s) 78–88

Abstract: We present a conceptual framework for the development of visual interactive techniques to formalize and externalize trust in machine learning (ML) workflows. Currently, trust in ML applications is an implicit process that takes place in the user's mind. ... ...

Abstract	We present a conceptual framework for the development of visual interactive techniques to formalize and externalize trust in machine learning (ML) workflows. Currently, trust in ML applications is an implicit process that takes place in the user's mind. As such, there is no method of feedback or communication of trust that can be acted upon. Our framework will be instrumental in developing interactive visualization approaches that will help users to efficiently and effectively build and communicate trust in ways that fit each of the ML process stages. We formulate several research questions and directions that include: 1) a typology/taxonomy of trust objects, trust issues, and possible reasons for (mis)trust; 2) formalisms to represent trust in machine-readable form; 3) means by which users can express their state of trust by interacting with a computer system (e.g., text, drawing, marking); 4) ways in which a system can facilitate users' expression and communication of the state of trust; and 5) creation of visual interactive techniques for representation and exploration of trust over all stages of an ML pipeline.
Language	English
Publishing date	2023-10-09
Publishing country	United States
Document type	Journal Article
ISSN	1558-1756
ISSN (online)	1558-1756
DOI	10.1109/MCG.2023.3237286
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Article ; Online: Visual Analytics Methodology for Eye Movement Studies.

Andrienko, G / Andrienko, N / Burch, M / Weiskopf, D

IEEE transactions on visualization and computer graphics

2012 Volume 18, Issue 12, Page(s) 2889–2898

Abstract: Eye movement analysis is gaining popularity as a tool for evaluation of visual displays and interfaces. However, the existing methods and tools for analyzing eye movements and scanpaths are limited in terms of the tasks they can support and effectiveness ...

Abstract	Eye movement analysis is gaining popularity as a tool for evaluation of visual displays and interfaces. However, the existing methods and tools for analyzing eye movements and scanpaths are limited in terms of the tasks they can support and effectiveness for large data and data with high variation. We have performed an extensive empirical evaluation of a broad range of visual analytics methods used in analysis of geographic movement data. The methods have been tested for the applicability to eye tracking data and the capability to extract useful knowledge about users' viewing behaviors. This allowed us to select the suitable methods and match them to possible analysis tasks they can support. The paper describes how the methods work in application to eye tracking data and provides guidelines for method selection depending on the analysis tasks.
MeSH term(s)	Algorithms ; Cluster Analysis ; Computer Graphics ; Eye Movements/physiology ; Fixation, Ocular/physiology ; Humans ; Image Processing, Computer-Assisted/methods
Language	English
Publishing date	2012-12
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.2012.276
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