Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS

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  • Publication
    The Flow of Trust: A Visualization Framework to Externalize, Explore, and Explain Trust in ML Applications
    ( 2023-03-01)
    Elzen, Stef van den
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    Andriyenko, Gennadiy
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    Andriyenko, Nathaliya
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    Fisher, Brian D.
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    Martins, Rafael M.
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    Peltonen, Jaakko
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    Telea, Alexandru C.
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    Verleysen, Michel
    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.
  • Publication
    Extracting Movement-based Topics for Analysis of Space Use
    ( 2023)
    Andriyenko, Gennadiy
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    Andriyenko, Nathaliya
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    Hecker, Dirk
    We present a novel approach to analyze spatio-temporal movement patterns using topic modeling. Our approach represents trajectories as sequences of place visits and moves, applies topic modeling separately to each collection of sequences, and synthesizes results. This supports the identification of dominant topics for both place visits and moves, the exploration of spatial and temporal patterns of movement, enabling understanding of space use. The approach is applied to two real-world data sets of car movements in Milan and UK road traffic, demonstrating the ability to uncover meaningful patterns and insights.
  • Publication
    A conceptual framework for developing dashboards for big mobility data
    ( 2023)
    Conrow, Lindsey
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    Fu, Cheng
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    Huang, Haosheng
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    Andriyenko, Nathaliya
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    Andriyenko, Gennadiy
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    Weibel, Robert
    Dashboards are an increasingly popular form of data visualization. Large, complex, and dynamic mobility data present a number of challenges in dashboard design. The overall aim for dashboard design is to improve information communication and decision making, though big mobility data in particular require considering privacy alongside size and complexity. Taking these issues into account, a gap remains between wrangling mobility data and developing meaningful dashboard output. Therefore, there is a need for a framework that bridges this gap to support the mobility dashboard development and design process. In this paper we outline a conceptual framework for mobility data dashboards that provides guidance for the development process while considering mobility data structure, volume, complexity, varied application contexts, and privacy constraints. We illustrate the proposed framework’s components and process using example mobility dashboards with varied inputs, end-users and objectives. Overall, the framework offers a basis for developers to understand how informational displays of big mobility data are determined by end-user needs as well as the types of data selection, transformation, and display available to particular mobility datasets.
  • Publication
    A theoretical model for pattern discovery in visual analytics
    (Elsevier B.V., 2021-01-21)
    Andrienko, Natalia
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    Andrienko, Gennady
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    Miksch, Silvia
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    Schumann, Heidrun
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    Wrobel, Stefan
    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.
  • Publication
    Human migration: The big data perspective
    ( 2021)
    Sîrbu, A.
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    Andrienko, Gennady
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    Andrienko, Natalia
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    Boldrini, C.
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    Conti, Marco
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    Giannotti, Fosca
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    Guidotti, R.
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    Bertoli, S.
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    Kim, J.
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    Muntean, C.I.
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    Pappalardo, L.
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    Passarella, Andrea
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    Pedreschi, Dino
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    Pollacci, L.
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    Pratesi, Francesca
    ;
    Sharma, R.
    How can big data help to understand the migration phenomenon? In this paper, we try to answer this question through an analysis of various phases of migration, comparing traditional and novel data sources and models at each phase. We concentrate on three phases of migration, at each phase describing the state of the art and recent developments and ideas. The first phase includes the journey, and we study migration flows and stocks, providing examples where big data can have an impact. The second phase discusses the stay, i.e. migrant integration in the destination country. We explore various data sets and models that can be used to quantify and understand migrant integration, with the final aim of providing the basis for the construction of a novel multi-level integration index. The last phase is related to the effects of migration on the source countries and the return of migrants.
  • Publication
    Correction to: Human migration: The big data perspective
    ( 2021)
    Sîrbu, A.
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    Andrienko, Gennady
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    Andrienko, Natalia
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    Boldrini, C.
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    Conti, Marco
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    Giannotti, Fosca
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    Guidotti, R.
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    Bertoli, S.
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    Kim, J.
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    Muntean, C.I.
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    Pappalardo, L.
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    Passarella, Andrea
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    Pedreschi, Dino
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    Pollacci, L.
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    Pratesi, Francesca
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    Sharma, R.
    The article "Human migration: the big data perspective", written by Alina Sîrbu, Gennady Andrienko, Natalia Andrienko, Chiara Boldrini, Marco Conti, Fosca Giannotti, Riccardo Guidotti, Simone Bertoli, Jisu Kim, Cristina Ioana Muntean, Luca Pappalardo, Andrea Passarella, Dino Pedreschi, Laura Pollacci, Francesca Pratesi, Rajesh Sharma originally published electronically on the publisher's internet portal (currently SpringerLink) on April 10, 2021 without open access. The copyright of the article changed to © The Author(s) 2021 and the article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The original article has been updated.
  • Publication
    Toward flexible visual analytics augmented through smooth display transitions
    ( 2021)
    Tominski, Christian
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    Andrienko, Gennady
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    Andrienko, Natalia
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    Bleisch, S.
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    Fabrikant, S.I.
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    Mayr, E.
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    Miksch, Silvia
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    Pohl, M.
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    Skupin, A.
    Visualizing big and complex multivariate data is challenging. To address this challenge, we propose flexible visual analytics (FVA) with the aim to mitigate visual complexity and interaction complexity challenges in visual analytics, while maintaining the strengths of multiple perspectives on the studied data. At the heart of our proposed approach are transitions that fluidly transform data between user-relevant views to offer various perspectives and insights into the data. While smooth display transitions have been already proposed, there has not yet been an interdisciplinary discussion to systematically conceptualize and formalize these ideas. As a call to further action, we argue that future research is necessary to develop a conceptual framework for flexible visual analytics. We discuss preliminary ideas for prioritizing multi-aspect visual representations and multi-aspect transitions between them, and consider the display user for whom such depictions are produced and made available for visual analytics. With this contribution we aim to further facilitate visual analytics on complex data sets for varying data exploration tasks and purposes based on different user characteristics and data use contexts.
  • Publication
    Visual Analytics for Characterizing Mobility Aspects of Urban Context
    ( 2021)
    Andrienko, Gennady
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    Andrienko, Natalia
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    Patterson, Fabian
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    Chen, Siming
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    Weibel, Robert
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    Huang, H.
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    Doulkeridis, C.
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    Georgiou, H.
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    Pelekis, N.
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    Theodoridis, Y.
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    Nanni, M.
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    Longhi, L.
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    Koumparos, A.
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    Yasar, A.
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    Kureshi, I.
    Visual analytics science develops principles and methods for efficient humanâcomputer collaboration in solving complex problems. Visual and interactive techniques are used to create conditions in which human analysts can effectively utilize their unique capabilities: the power of seeing, interpreting, linking, and reasoning. Visual analytics research deals with various types of data and analysis tasks from numerous application domains. A prominent research topic is analysis of spatiotemporal data, which may describe events occurring at different spatial locations, changes of attribute values associated with places or spatial objects, or movements of people, vehicles, or other objects. Such kinds of data are abundant in urban applications. Movement data are a quintessential type of spatiotemporal data because they can be considered from multiple perspectives as trajectories, as spatial events, and as changes of space-related attribute values. By example of movement dat a, we demonstrate the utilization of visual analytics techniques and approaches in data exploration and analysis.
  • Publication
    (So) Big Data and the transformation of the city
    ( 2021)
    Andrienko, Gennady
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    Andrienko, Natalia
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    Boldrini, C.
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    Caldarelli, G.
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    Cintia, P.
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    Cresci, S.
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    Facchini, A.
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    Giannotti, Fosca
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    Gionis, A.
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    Guidotti, R.
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    Mathioudakis, M.
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    Muntean, C.I.
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    Pappalardo, L.
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    Pedreschi, Dino
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    Pournaras, E.
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    Pratesi, Francesca
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    Tesconi, M.
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    Trasarti, Roberto
    The exponential increase in the availability of large-scale mobility data has fueled the vision of smart cities that will transform our lives. The truth is that we have just scratched the surface of the research challenges that should be tackled in order to make this vision a reality. Consequently, there is an increasing interest among different research communities (ranging from civil engineering to computer science) and industrial stakeholders in building knowledge discovery pipelines over such data sources. At the same time, this widespread data availability also raises privacy issues that must be considered by both industrial and academic stakeholders. In this paper, we provide a wide perspective on the role that big data have in reshaping cities. The paper covers the main aspects of urban data analytics, focusing on privacy issues, algorithms, applications and services, and georeferenced data from social media. In discussing these aspects, we leverage, as concrete examples and case studies of urban data science tools, the results obtained in the ""City of Citizens"" thematic area of the Horizon 2020 SoBigData initiative, which includes a virtual research environment with mobility datasets and urban analytics methods developed by several institutions around Europe. We conclude the paper outlining the main research challenges that urban data science has yet to address in order to help make the smart city vision a reality.
  • Publication
    Automating and utilising equal-distribution data classification
    ( 2021)
    Andrienko, Gennady
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    Andrienko, Natalia
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    Kureshi, I.
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    Lee, K.
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    Smith, I.
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    Staykova, T.
    Data classification, i.e. organising data items in groups (classes), is a general technique widely used in data visualisation and cartography, in particular, for creation of choropleth maps. Conventionally, data are classified by dividing the data range into intervals and assigning the same symbol or colour to all data falling within an interval. For instance, the intervals may be of the same length or may include the same number of data items. We propose a method for defining intervals so that some quantity represented by values of another attribute is equally distributed among the classes. This kind of classification supports exploratory analysis of relationships between the attribute used for the classification and the distribution of the phenomenon whose quantity is represented by the additional attribute. The approach may be especially useful when the distribution of the phenomenon is very unequal, with many data items having zero or low quantities and quite a few items having larger quantities. With such a distribution, standard statistical analysis of the relationships may be problematic. We demonstrate the potential of the approach by analysing data referring to a set of spatially distributed people (patients) in relationship to characteristics of the areas in which the people live.