Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS

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  • Publication
    Visual Analytics for Data Scientists
    (Springer Nature, 2020)
    Andrienko, Natalia
    ;
    Andrienko, Gennady
    ;
    Fuchs, Georg
    ;
    Slingsby, Aidan
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    Turkay, Cagatay
    ;
    Wrobel, Stefan
  • Publication
    Visual Analytics in the Aviation and Maritime Domains
    ( 2020)
    Andrienko, Gennady
    ;
    Andrienko, Natalia
    ;
    Fuchs, Georg
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    Rüping, Stefan
    ;
    Cordero Garcia, Jose Manuel
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    Scarlatti, David
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    Vouros, George A.
    ;
    Herranz, Ricardo
    ;
    Marcos, Rodrigo
    Visual analytics is a research discipline that is based on acknowledging the power and the necessity of the human vision, understanding, and reasoning in data analysis and problem solving. It develops a methodology of analysis that facilitates human activities by means of interactive visual representations of information. By examples from the domains of aviation and maritime transportation, we demonstrate the essence of the visual analytics methods and their utility for investigating properties of available data and analysing data for understanding real-world phenomena and deriving valuable knowledge. We describe four case studies in which distinct kinds of knowledge have been derived from trajectories of vessels and airplanes and related spatial and temporal data by human analytical reasoning empowered by interactive visual interfaces combined with computational operations.
  • Publication
    The datAcron Ontology for the Specification of Semantic Trajectories. Specification of Semantic Trajectories for Data Transformations Supporting Visual Analytics
    ( 2019)
    Vouros, George A.
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    Santipantakis, Georgios M.
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    Doulkeridis, Christos
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    Vlachou, Akrivi
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    Andrienko, Gennady
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    Andrienko, Natalia
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    Fuchs, Georg
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    Cordero Garcia, Jose Manuel
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    Garcia Martinez, Miguel
    As the number of moving objects increases, the challenges for achieving operational goals w.r.t. the mobility in many domains that are critical to economy and safety emerge dramatically. In domains such as air traffic management, this dictates a shift of operations' paradigm from location based, as it is today, to trajectory based, where trajectories are turned into ""first-class citizens"". Additionally, the increasing amount of data from heterogenous and disparate data sources implies the need for advanced analysis methods that require exploiting spatio-temporal mobility data in appropriate forms and at varying levels of abstraction. All these call for an in-principle way for organising integrated views of mobility data, with trajectories playing the main role. In this paper, we propose an ontology for modelling semantic trajectories, integrating spatio-temporal information regarding mobility of objects, at multiple, interlinked levels of abstraction. Our work builds upon a comprehensive framework that identifies fundamental spatio-temporal data types and specific conversions among these types. We validate the ontological specifications towards satisfying the needs of visual analysis tasks in the complex air traffic management domain, using real-world data.
  • Publication
    Data abstraction for visualizing large time series
    ( 2018)
    Shurkhovetskyy, G.
    ;
    Andrienko, Natalia
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    Andrienko, Gennady
    ;
    Fuchs, Georg
    Numeric time series is a class of data consisting of chronologically ordered observations represented by numeric values. Much of the data in various domains, such as financial, medical and scientific, are represented in the form of time series. To cope with the increasing sizes of datasets, numerous approaches for abstracting large temporal data are developed in the area of data mining. Many of them proved to be useful for time series visualization. However, despite the existence of numerous surveys on time series mining and visualization, there is no comprehensive classification of the existing methods based on the needs of visualization designers. We propose a classification framework that defines essential criteria for selecting an abstraction method with an eye to subsequent visualization and support of users' analysis tasks. We show that approaches developed in the data mining field are capable of creating representations that are useful for visualizing time series data. We evaluate these methods in terms of the defined criteria and provide a summary table that can be easily used for selecting suitable abstraction methods depending on data properties, desirable form of representation, behaviour features to be studied, required accuracy and level of detail, and the necessity of efficient search and querying. We also indicate directions for possible extension of the proposed classification framework.
  • Publication
    Big data analytics for time critical maritime and aerial mobility forecasting
    ( 2018)
    Vouros, G.A.
    ;
    Doulkeridis, C.
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    Santipantakis, G.
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    Vlachou, A.
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    Pelekis, N.
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    Georgiou, H.
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    Theodoridis, Y.
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    Patroumpas, K.
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    Alevizos, Elias
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    Artikis, A.
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    Fuchs, Georg
    ;
    Mock, Michael
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    Andrienko, Gennady
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    Andrienko, Natalia
    ;
    Ray, C.
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    Claramunt, C.
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    Camossi, E.
    ;
    Jousselme, A.-L.
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    Scarlatti, D.
    ;
    Cordero, J.M.
    The correlated exploitation of heterogeneous data sources offering very large archival and streaming data is important to increase the accuracy of computations when analysing and predicting future states of moving entities. Aiming to significantly advance the capacities of systems to improve safety and effectiveness of critical operations involving a large number of moving entities in large geographical areas, this paper describes progress achieved towards time critical big data analytics solutions to user-defined challenges in the air-traffic management and maritime domains. Besides, this paper presents further research challenges concerning data integration and management, predictive analytics for trajectory and events forecasting, and visual analytics.
  • Publication
    Time-aware sub-trajectory clustering in HERMES@PostgreSQL
    ( 2018)
    Tampakis, P.
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    Pelekis, N.
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    Theodoridis, Y.
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    Andrienko, Natalia
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    Andrienko, Gennady
    ;
    Fuchs, Georg
    In this paper, we present an efficient in-DBMS framework for progressive time-aware sub-trajectory cluster analysis. In particular, we address two variants of the problem: (a) spatiotemporal sub-trajectory clustering and (b) index-based time-aware clustering at querying environment. Our approach for (a) relies on a two-phase process: a voting-and-segmentation phase followed by a sampling-and-clustering phase. Regarding (b), we organize data into partitions that correspond to groups of sub-trajectories, which are incrementally maintained in a hierarchical structure. Both approaches have been implemented in Hermes@PostgreSQL, a real Moving Object Database engine built on top of PostgreSQL, enabling users to perform progressive cluster analysis via simple SQL. The framework is also extended with a Visual Analytics (VA) tool to facilitate real world analysis.
  • Publication
    Viewing Visual Analytics as Model Building
    ( 2018)
    Andrienko, Natalia
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    Lammarsch, T.
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    Andrienko, Gennady
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    Fuchs, Georg
    ;
    Keim, Daniel A.
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    Miksch, Silvia
    ;
    Rind, A.
    To complement the currently existing definitions and conceptual frameworks of visual analytics, which focus mainly on activities performed by analysts and types of techniques they use, we attempt to define the expected results of these activities. We argue that the main goal of doing visual analytics is to build a mental and/or formal model of a certain piece of reality reflected in data. The purpose of the model may be to understand, to forecast or to control this piece of reality. Based on this model-building perspective, we propose a detailed conceptual framework in which the visual analytics process is considered as a goal-oriented workflow producing a model as a result. We demonstrate how this framework can be used for performing an analytical survey of the visual analytics research field and identifying the directions and areas where further research is needed.
  • Publication
    Visual analytics of flight trajectories for uncovering decision making strategies
    ( 2018)
    Andrienko, Gennady
    ;
    Andrienko, Natalia
    ;
    Fuchs, Georg
    ;
    Scarlatti, D.
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    Cordero Garcia, J.M.
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    Vouros, G.A.
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    Herranz, R.
    ;
    Marcos, R.
    In air traffic management and control, movement data describing actual and planned flights are used for planning, monitoring and post-operation analysis purposes with the goal of increased efficient utilization of air space capacities (in terms of delay reduction or flight efficiency), without compromising the safety of passengers and cargo, nor timeliness of flights. From flight data, it is possible to extract valuable information concerning preferences and decision making of airlines (e.g. route choice) and air traffic managers and controllers (e.g. flight rerouting or optimizing flight times), features whose understanding is intended as a key driver for bringing operational performance benefits. In this paper, we propose a suite of visual analytics techniques for supporting assessment of flight data quality and data analysis workflows centred on revealing decision making preferences.
  • Publication
    Increasing maritime situation awareness via trajectory detection, enrichment and recognition of events
    ( 2018)
    Vouros, G.A.
    ;
    Vlachou, A.
    ;
    Santipantakis, G.
    ;
    Doulkeridis, C.
    ;
    Pelekis, N.
    ;
    Georgiou, H.
    ;
    Theodoridis, Y.
    ;
    Patroumpas, K.
    ;
    Alevizos, Elias
    ;
    Artikis, A.
    ;
    Fuchs, Georg
    ;
    Mock, Michael
    ;
    Andrienko, Gennady
    ;
    Andrienko, Natalia
    ;
    Claramunt, C.
    ;
    Ray, C.
    ;
    Camossi, E.
    ;
    Jousselme, A.-L.
    The research presented in this paper aims to show the deployment and use of advanced technologies towards processing surveillance data for the detection of events, contributing to maritime situation awareness via trajectories' detection, synopses generation and semantic enrichment of trajectories. We first introduce the context of the maritime domain and then the main principles of the big data architecture developed so far within the European funded H2020 datAcron project. From the integration of large maritime trajectory datasets, to the generation of synopses and the detection of events, the main functions of the datAcron architecture are developed and discussed. The potential for detection and forecasting of complex events at sea is illustrated by preliminary experimental results.
  • Publication
    Clustering trajectories by relevant parts for air traffic analysis
    ( 2018)
    Andrienko, Gennady
    ;
    Andrienko, Natalia
    ;
    Fuchs, Georg
    ;
    Garcia, Jose
    Clustering of trajectories of moving objects by similarity is an important technique in movement analysis. Existing distance functions assess the similarity between trajectories based on properties of the trajectory points or segments. The properties may include the spatial positions, times, and thematic attributes. There may be a need to focus the analysis on certain parts of trajectories, i.e., points and segments that have particular properties. According to the analysis focus, the analyst may need to cluster trajectories by similarity of their relevant parts only. Throughout the analysis process, the focus may change, and different parts of trajectories may become relevant. We propose an analytical workflow in which interactive filtering tools are used to attach relevance flags to elements of trajectories, clustering is done using a distance function that ignores irrelevant elements, and the resulting clusters are summarized for further analysis. We demonstrate how this workflow can be useful for different analysis tasks in three case studies with real data from the domain of air traffic. We propose a suite of generic techniques and visualization guidelines to support movement data analysis by means of relevance-aware trajectory clustering.