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  1. Buch ; Online: Compact Phase Histograms for Guided Exploration of Periodicity

    Franke, Max / Koch, Steffen

    2023  

    Abstract: Periodically occurring accumulations of events or measured values are present in many time-dependent datasets and can be of interest for analyses. The frequency of such periodic behavior is often not known in advance, making it difficult to detect and ... ...

    Abstract Periodically occurring accumulations of events or measured values are present in many time-dependent datasets and can be of interest for analyses. The frequency of such periodic behavior is often not known in advance, making it difficult to detect and tedious to explore. Automated analysis methods exist, but can be too costly for smooth, interactive analysis. We propose a compact visual representation that reveals periodicity by showing a phase histogram for a given period length that can be used standalone or in combination with other linked visualizations. Our approach supports guided, interactive analyses by suggesting other period lengths to explore, which are ranked based on two quality measures. We further describe how the phase can be mapped to visual representations in other views to reveal periodicity there.

    Comment: IEEE VIS 2023 Short Paper
    Schlagwörter Computer Science - Graphics
    Erscheinungsdatum 2023-07-28
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    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  2. Artikel ; Online: Enhancing Single-Frame Supervision for Better Temporal Action Localization.

    Chen, Changjian / Chen, Jiashu / Yang, Weikai / Wang, Haoze / Knittel, Johannes / Zhao, Xibin / Koch, Steffen / Ertl, Thomas / Liu, Shixia

    IEEE transactions on visualization and computer graphics

    2024  Band PP

    Abstract: Temporal action localization aims to identify the boundaries and categories of actions in videos, such as scoring a goal in a football match. Single-frame supervision has emerged as a labor-efficient way to train action localizers as it requires only one ...

    Abstract Temporal action localization aims to identify the boundaries and categories of actions in videos, such as scoring a goal in a football match. Single-frame supervision has emerged as a labor-efficient way to train action localizers as it requires only one annotated frame per action. However, it often suffers from poor performance due to the lack of precise boundary annotations. To address this issue, we propose a visual analysis method that aligns similar actions and then propagates a few user-provided annotations (e.g., boundaries, category labels) to similar actions via the generated alignments. Our method models the alignment between actions as a heaviest path problem and the annotation propagation as a quadratic optimization problem. As the automatically generated alignments may not accurately match the associated actions and could produce inaccurate localization results, we develop a storyline visualization to explain the localization results of actions and their alignments. This visualization facilitates users in correcting wrong localization results and misalignments. The corrections are then used to improve the localization results of other actions. The effectiveness of our method in improving localization performance is demonstrated through quantitative evaluation and a case study.
    Sprache Englisch
    Erscheinungsdatum 2024-04-15
    Erscheinungsland United States
    Dokumenttyp Journal Article
    ISSN 1941-0506
    ISSN (online) 1941-0506
    DOI 10.1109/TVCG.2024.3388521
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  3. Artikel ; Online: PyramidTags: Context-, Time- and Word Order-Aware Tag Maps to Explore Large Document Collections.

    Knittel, Johannes / Koch, Steffen / Ertl, Thomas

    IEEE transactions on visualization and computer graphics

    2021  Band 27, Heft 12, Seite(n) 4455–4468

    Abstract: It is difficult to explore large text collections if no or little information is available on the contained documents. Hence, starting analytic tasks on such corpora is challenging for many stakeholders from various domains. As a remedy, recent ... ...

    Abstract It is difficult to explore large text collections if no or little information is available on the contained documents. Hence, starting analytic tasks on such corpora is challenging for many stakeholders from various domains. As a remedy, recent visualization research suggests to use visual spatializations of representative text documents or tags to explore text collections. With PyramidTags, we introduce a novel approach for summarizing large text collections visually. In contrast to previous work, PyramidTags in particular aims at creating an improved representation that incorporates both temporal evolution and semantic relationship of visualized tags within the summarized document collection. As a result, it equips analysts with a visual starting point for interactive exploration to not only get an overview of the main terms and phrases of the corpus, but also to grasp important ideas and stories. Analysts can hover and select multiple tags to explore relationships and retrieve the most relevant documents. In this work, we apply PyramidTags to hundreds of thousands of web-crawled news reports. Our benchmarks suggest that PyramidTags creates time- and context-aware layouts, while preserving the inherent word order of important pairs.
    Sprache Englisch
    Erscheinungsdatum 2021-10-26
    Erscheinungsland United States
    Dokumenttyp Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 1941-0506
    ISSN (online) 1941-0506
    DOI 10.1109/TVCG.2020.3010095
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  4. Artikel ; Online: Visual Neural Decomposition to Explain Multivariate Data Sets.

    Knittel, Johannes / Lalama, Andres / Koch, Steffen / Ertl, Thomas

    IEEE transactions on visualization and computer graphics

    2021  Band 27, Heft 2, Seite(n) 1374–1384

    Abstract: Investigating relationships between variables in multi-dimensional data sets is a common task for data analysts and engineers. More specifically, it is often valuable to understand which ranges of which input variables lead to particular values of a ... ...

    Abstract Investigating relationships between variables in multi-dimensional data sets is a common task for data analysts and engineers. More specifically, it is often valuable to understand which ranges of which input variables lead to particular values of a given target variable. Unfortunately, with an increasing number of independent variables, this process may become cumbersome and time-consuming due to the many possible combinations that have to be explored. In this paper, we propose a novel approach to visualize correlations between input variables and a target output variable that scales to hundreds of variables. We developed a visual model based on neural networks that can be explored in a guided way to help analysts find and understand such correlations. First, we train a neural network to predict the target from the input variables. Then, we visualize the inner workings of the resulting model to help understand relations within the data set. We further introduce a new regularization term for the backpropagation algorithm that encourages the neural network to learn representations that are easier to interpret visually. We apply our method to artificial and real-world data sets to show its utility.
    Mesh-Begriff(e) Algorithms ; Computer Graphics ; Neural Networks, Computer
    Sprache Englisch
    Erscheinungsdatum 2021-01-28
    Erscheinungsland United States
    Dokumenttyp Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 1941-0506
    ISSN (online) 1941-0506
    DOI 10.1109/TVCG.2020.3030420
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  5. Buch ; Online: ELSKE

    Knittel, Johannes / Koch, Steffen / Ertl, Thomas

    Efficient Large-Scale Keyphrase Extraction

    2021  

    Abstract: Keyphrase extraction methods can provide insights into large collections of documents such as social media posts. Existing methods, however, are less suited for the real-time analysis of streaming data, because they are computationally too expensive or ... ...

    Abstract Keyphrase extraction methods can provide insights into large collections of documents such as social media posts. Existing methods, however, are less suited for the real-time analysis of streaming data, because they are computationally too expensive or require restrictive constraints regarding the structure of keyphrases. We propose an efficient approach to extract keyphrases from large document collections and show that the method also performs competitively on individual documents.
    Schlagwörter Computer Science - Information Retrieval
    Erscheinungsdatum 2021-02-10
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    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  6. Buch ; Online: Efficient Sparse Spherical k-Means for Document Clustering

    Knittel, Johannes / Koch, Steffen / Ertl, Thomas

    2021  

    Abstract: Spherical k-Means is frequently used to cluster document collections because it performs reasonably well in many settings and is computationally efficient. However, the time complexity increases linearly with the number of clusters k, which limits the ... ...

    Abstract Spherical k-Means is frequently used to cluster document collections because it performs reasonably well in many settings and is computationally efficient. However, the time complexity increases linearly with the number of clusters k, which limits the suitability of the algorithm for larger values of k depending on the size of the collection. Optimizations targeted at the Euclidean k-Means algorithm largely do not apply because the cosine distance is not a metric. We therefore propose an efficient indexing structure to improve the scalability of Spherical k-Means with respect to k. Our approach exploits the sparsity of the input vectors and the convergence behavior of k-Means to reduce the number of comparisons on each iteration significantly.

    Comment: ACM DocEng 2021
    Schlagwörter Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Computer Science - Data Structures and Algorithms
    Erscheinungsdatum 2021-07-30
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    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  7. Buch ; Online ; Dissertation / Habilitation: In defense of conceptual engineering

    Koch, Steffen [Verfasser] / Horvath, Joachim [Gutachter] / Grundmann, Thomas [Gutachter]

    2021  

    Verfasserangabe Steffen Koch ; Gutachter: Joachim Horvath, Thomas Grundmann ; Fakultät für Philosophie und Erziehungswissenschaft
    Schlagwörter Philosophie ; Philosophy
    Thema/Rubrik (Code) sg100
    Sprache Englisch
    Verlag Ruhr-Universität Bochum
    Erscheinungsort Bochum
    Dokumenttyp Buch ; Online ; Dissertation / Habilitation
    Datenquelle Digitale Dissertationen im Internet

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  8. Artikel ; Online: Real-Time Visual Analysis of High-Volume Social Media Posts.

    Knittel, Johannes / Koch, Steffen / Tang, Tan / Chen, Wei / Wu, Yingcai / Liu, Shixia / Ertl, Thomas

    IEEE transactions on visualization and computer graphics

    2021  Band 28, Heft 1, Seite(n) 879–889

    Abstract: Breaking news and first-hand reports often trend on social media platforms before traditional news outlets cover them. The real-time analysis of posts on such platforms can reveal valuable and timely insights for journalists, politicians, business ... ...

    Abstract Breaking news and first-hand reports often trend on social media platforms before traditional news outlets cover them. The real-time analysis of posts on such platforms can reveal valuable and timely insights for journalists, politicians, business analysts, and first responders, but the high number and diversity of new posts pose a challenge. In this work, we present an interactive system that enables the visual analysis of streaming social media data on a large scale in real-time. We propose an efficient and explainable dynamic clustering algorithm that powers a continuously updated visualization of the current thematic landscape as well as detailed visual summaries of specific topics of interest. Our parallel clustering strategy provides an adaptive stream with a digestible but diverse selection of recent posts related to relevant topics. We also integrate familiar visual metaphors that are highly interlinked for enabling both explorative and more focused monitoring tasks. Analysts can gradually increase the resolution to dive deeper into particular topics. In contrast to previous work, our system also works with non-geolocated posts and avoids extensive preprocessing such as detecting events. We evaluated our dynamic clustering algorithm and discuss several use cases that show the utility of our system.
    Sprache Englisch
    Erscheinungsdatum 2021-12-24
    Erscheinungsland United States
    Dokumenttyp Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 1941-0506
    ISSN (online) 1941-0506
    DOI 10.1109/TVCG.2021.3114800
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  9. Buch ; Online: Visual Neural Decomposition to Explain Multivariate Data Sets

    Knittel, Johannes / Lalama, Andres / Koch, Steffen / Ertl, Thomas

    2020  

    Abstract: Investigating relationships between variables in multi-dimensional data sets is a common task for data analysts and engineers. More specifically, it is often valuable to understand which ranges of which input variables lead to particular values of a ... ...

    Abstract Investigating relationships between variables in multi-dimensional data sets is a common task for data analysts and engineers. More specifically, it is often valuable to understand which ranges of which input variables lead to particular values of a given target variable. Unfortunately, with an increasing number of independent variables, this process may become cumbersome and time-consuming due to the many possible combinations that have to be explored. In this paper, we propose a novel approach to visualize correlations between input variables and a target output variable that scales to hundreds of variables. We developed a visual model based on neural networks that can be explored in a guided way to help analysts find and understand such correlations. First, we train a neural network to predict the target from the input variables. Then, we visualize the inner workings of the resulting model to help understand relations within the data set. We further introduce a new regularization term for the backpropagation algorithm that encourages the neural network to learn representations that are easier to interpret visually. We apply our method to artificial and real-world data sets to show its utility.

    Comment: To appear in IEEE Transactions on Visualization and Computer Graphics and IEEE VIS 2020 (VAST)
    Schlagwörter Computer Science - Machine Learning ; Computer Science - Human-Computer Interaction
    Thema/Rubrik (Code) 006
    Erscheinungsdatum 2020-09-11
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    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  10. Buch ; Online ; Dissertation / Habilitation: Geoökologische Untersuchungen zum sickerwassergetragenen Nähr- und Schadstofftransport aus urbanen Böden in aquatische Systeme

    Koch, Steffen

    dargestellt am Beispiel der Stadt Halle (Saale)

    2006  

    Abstract: Zsfassung in engl. ... ...

    Verfasserangabe von Steffen Koch
    Abstract Zsfassung in engl. Sprache
    Sprache Deutsch
    Umfang Online-Ressource, Text + Image
    Verlag Universitäts- und Landesbibliothek
    Erscheinungsort Halle, Saale
    Dokumenttyp Buch ; Online ; Dissertation / Habilitation
    Dissertation / Habilitation Univ., FB Geowissenschaften, Diss.--Halle, 2006
    Datenquelle Katalog der Technische Informationsbibliothek Hannover

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