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Abdelaal, Moataz

Moataz Abdelaal

Herr M. Sc.

Wiss. Mitarbeiter
Visualisierungsinstitut der Universität Stuttgart (VISUS)
Arbeitsgruppe Weiskopf

Kontakt

+49 711 685 88616
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Allmandring 19
70569 Stuttgart
Deutschland
Raum: 00.010

Fachgebiet

Visualization, Network Visualization, Empirical User Evaluation, Visualization Tools

Publikationen

2022
1. T. Krake, M. von Scheven, J. Gade, M. Abdelaal, D. Weiskopf, und M. Bischoff, „Efficient Update of Redundancy Matrices for Truss and Frame Structures“, Journal of Theoretical, Computational and Applied Mechanics, 2022, doi: 10.46298/jtcam.9615.
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  @article{jtcam:10398, abstract = {Redundancy matrices provide insights into the load carrying behavior of statically indeterminate structures. This information can be employed for the design and analysis of structures with regard to certain objectives, for example reliability, robustness, or adaptability. In this context, the structure is often iteratively examined with the help of slight adjustments. However, this procedure generally requires a high computational effort for the recalculation of the redundancy matrix due to the necessity of costly matrix operations. This paper addresses this problem by providing generic algebraic formulations for efficiently updating the redundancy matrix (and related matrices). The formulations include various modifications like adding, removing, and exchanging elements and are applicable to truss and frame structures. With several examples, we demonstrate the interaction between the formulas and their mechanical interpretation. Finally, a performance test for a scaleable structure is presented. }, author = {Krake, Tim and von Scheven, Malte and Gade, Jan and Abdelaal, Moataz and Weiskopf, Daniel and Bischoff, Manfred}, doi = {10.46298/jtcam.9615}, journal = {{Journal of Theoretical, Computational and Applied Mechanics}}, title = {{Efficient Update of Redundancy Matrices for Truss and Frame Structures}}, url = {https://jtcam.episciences.org/10398}, year = 2022 }
  Link
  https://jtcam.episciences.org/10398
2. M. Abdelaal u. a., „Visualization for Architecture, Engineering, and Construction: Shaping the Future of Our Built World“, IEEE Computer Graphics and Applications, Bd. 42, Nr. 2, Art. Nr. 2, März 2022, doi: 10.1109/MCG.2022.3149837.
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  @article{9709159, abstract = {Our built world is one of the most important factors for a livable future, accounting for massive impact on resource and energy use, as well as climate change, but also the social and economic aspects that come with population growth. The architecture, engineering, and construction industry is facing the challenge that it needs to substantially increase its productivity, let alone the quality of buildings of the future. In this article, we discuss these challenges in more detail, focusing on how digitization can facilitate this transformation of the industry, and link them to opportunities for visualization and augmented reality research. We illustrate solution strategies for advanced building systems based on wood and fiber.}, author = {Abdelaal, Moataz and Amtsberg, Felix and Becher, Michael and Estrada, Rebeca Duque and Kannenberg, Fabian and Calepso, Aimée Sousa and Wagner, Hans Jakob and Reina, Guido and Sedlmair, Michael and Menges, Achim and Weiskopf, Daniel}, doi = {10.1109/MCG.2022.3149837}, issn = {1558-1756}, journal = {IEEE Computer Graphics and Applications}, month = {03}, number = 2, pages = {10-20}, title = {Visualization for Architecture, Engineering, and Construction: Shaping the Future of Our Built World}, volume = 42, year = 2022 }
3. M. Abdelaal, N. D. Schiele, K. Angerbauer, K. Kurzhals, M. Sedlmair, und D. Weiskopf, „Supplemental Materials for: Comparative Evaluation of Bipartite, Node-Link, and Matrix-Based Network Representations“. DaRUS, 2022. doi: 10.18419/DARUS-3100.
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  @misc{https://doi.org/10.18419/darus-3100, author = {Abdelaal, Moataz and Schiele, Nathan Daniel and Angerbauer, Katrin and Kurzhals, Kuno and Sedlmair, Michael and Weiskopf, Daniel}, doi = {10.18419/DARUS-3100}, publisher = {DaRUS}, title = {Supplemental Materials for: Comparative Evaluation of Bipartite, Node-Link, and Matrix-Based Network Representations}, url = {https://darus.uni-stuttgart.de/citation?persistentId=doi:10.18419/darus-3100}, year = 2022 }
  Link
  https://darus.uni-stuttgart.de/citation?persistentId=doi:10.18419/darus-3100
4. G. Richer, A. Pister, M. Abdelaal, J.-D. Fekete, M. Sedlmair, und D. Weiskopf, „Scalability in Visualization“, IEEE Transactions on Visualization and Computer Graphics, S. 1–15, 2022, doi: 10.1109/TVCG.2022.3231230.
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  @article{10003102, abstract = {We introduce a conceptual model for scalability designed for visualization research. With this model, we systematically analyze over 120 visualization publications from 1990 to 2020 to characterize the different notions of scalability in these works. While many papers have addressed scalability issues, our survey identifies a lack of consistency in the use of the term in the visualization research community. We address this issue by introducing a consistent terminology meant to help visualization researchers better characterize the scalability aspects in their research. It also helps in providing multiple methods for supporting the claim that a work is “scalable.” Our model is centered around an effort function with inputs and outputs. The inputs are the problem size and resources, whereas the outputs are the actual efforts, for instance, in terms of computational run time or visual clutter. We select representative examples to illustrate different approaches and facets of what scalability can mean in visualization literature. Finally, targeting the diverse crowd of visualization researchers without a scalability tradition, we provide a set of recommendations for how scalability can be presented in a clear and consistent way to improve fair comparison between visualization techniques and systems and foster reproducibility.}, author = {Richer, Gaëlle and Pister, Alexis and Abdelaal, Moataz and Fekete, Jean-Daniel and Sedlmair, Michael and Weiskopf, Daniel}, doi = {10.1109/TVCG.2022.3231230}, issn = {1941-0506}, journal = {IEEE Transactions on Visualization and Computer Graphics}, pages = {1-15}, title = {Scalability in Visualization}, year = 2022 }
5. M. Abdelaal, N. D. Schiele, K. Angerbauer, K. Kurzhals, M. Sedlmair, und D. Weiskopf, „Comparative Evaluation of Bipartite, Node-Link, and Matrix-Based Network Representations“, IEEE Transactions on Visualization and Computer Graphics, S. 1–11, 2022, doi: 10.1109/TVCG.2022.3209427.
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  @article{9908291, abstract = {This work investigates and compares the performance of node-link diagrams, adjacency matrices, and bipartite layouts for visualizing networks. In a crowd-sourced user study (n = 150), we measure the task accuracy and completion time of the three representations for different network classes and properties. In contrast to the literature, which covers mostly topology-based tasks (e.g., path finding) in small datasets, we mainly focus on overview tasks for large and directed networks. We consider three overview tasks on networks with 500 nodes: (T1) network class identification, (T2) cluster detection, and (T3) network density estimation, and two detailed tasks: (T4) node in-degree vs. out-degree and (T5) representation mapping, on networks with 50 and 20 nodes, respectively. Our results show that bipartite layouts are beneficial for revealing the overall network structure, while adjacency matrices are most reliable across the different tasks.}, author = {Abdelaal, Moataz and Schiele, Nathan D. and Angerbauer, Katrin and Kurzhals, Kuno and Sedlmair, Michael and Weiskopf, Daniel}, doi = {10.1109/TVCG.2022.3209427}, issn = {1941-0506}, journal = {IEEE Transactions on Visualization and Computer Graphics}, pages = {1-11}, title = {Comparative Evaluation of Bipartite, Node-Link, and Matrix-Based Network Representations}, year = 2022 }
2021
1. D. Hägele, M. Abdelaal, O. S. Oguz, M. Toussaint, und D. Weiskopf, „Visual analytics for nonlinear programming in robot motion planning“, Journal of Visualization, 2021, doi: 10.1007/s12650-021-00786-8.
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  @article{hagele2021visual, abstract = {Nonlinear programming is a complex methodology where a problem is mathematically expressed in terms of optimality while imposing constraints on feasibility. Such problems are formulated by humans and solved by optimization algorithms. We support domain experts in their challenging tasks of understanding and troubleshooting optimization runs of intricate and high-dimensional nonlinear programs through a visual analytics system. The system was designed for our collaborators’ robot motion planning problems, but is domain agnostic in most parts of the visualizations. It allows for an exploration of the iterative solving process of a nonlinear program through several linked views of the computational process. We give insights into this design study, demonstrate our system for selected real-world cases, and discuss the extension of visualization and visual analytics methods for nonlinear programming.}, author = {Hägele, David and Abdelaal, Moataz and Oguz, Ozgur S. and Toussaint, Marc and Weiskopf, Daniel}, doi = {10.1007/s12650-021-00786-8}, issn = {18758975}, journal = {Journal of Visualization}, refid = {Hägele2021}, title = {Visual analytics for nonlinear programming in robot motion planning}, url = {https://doi.org/10.1007/s12650-021-00786-8}, year = 2021 }
  Link
  https://doi.org/10.1007/s12650-021-00786-8
2020
1. D. Hägele, M. Abdelaal, O. S. Oguz, M. Toussaint, und D. Weiskopf, „Visualization of Nonlinear Programming for Robot Motion Planning“, in Proceedings of the 13th International Symposium on Visual Information Communication and Interaction, in Proceedings of the 13th International Symposium on Visual Information Communication and Interaction. Eindhoven, Netherlands: Association for Computing Machinery, 2020. doi: 10.1145/3430036.3430050.
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  @inproceedings{haegele2020nonlinear, abstract = {Nonlinear programming targets nonlinear optimization with constraints, which is a generic yet complex methodology involving humans for problem modeling and algorithms for problem solving. We address the particularly hard challenge of supporting domain experts in handling, understanding, and trouble-shooting high-dimensional optimization with a large number of constraints. Leveraging visual analytics, users are supported in exploring the computation process of nonlinear constraint optimization. Our system was designed for robot motion planning problems and developed in tight collaboration with domain experts in nonlinear programming and robotics. We report on the experiences from this design study, illustrate the usefulness for relevant example cases, and discuss the extension to visual analytics for nonlinear programming in general.}, address = {New York, NY, USA}, articleno = {10}, author = {Hägele, David and Abdelaal, Moataz and Oguz, Ozgur S. and Toussaint, Marc and Weiskopf, Daniel}, booktitle = {Proceedings of the 13th International Symposium on Visual Information Communication and Interaction}, doi = {10.1145/3430036.3430050}, isbn = {9781450387507}, location = {Eindhoven, Netherlands}, numpages = {8}, publisher = {Association for Computing Machinery}, series = {VINCI '20}, title = {Visualization of Nonlinear Programming for Robot Motion Planning}, url = {https://doi.org/10.1145/3430036.3430050}, year = 2020 }
  Link
  https://doi.org/10.1145/3430036.3430050
2. M. Abdelaal, A. Lhuillier, M. Hlawatsch, und D. Weiskopf, „Time-Aligned Edge Plots for Dynamic Graph Visualization“, in 2020 24th International Conference Information Visualisation (IV), in 2020 24th International Conference Information Visualisation (IV). 2020. doi: 10.1109/IV51561.2020.00048.
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  @conference{abdelaal2020timealigned, author = {Abdelaal, Moataz and Lhuillier, Antoine and Hlawatsch, Marcel and Weiskopf, Daniel}, booktitle = {2020 24th International Conference Information Visualisation (IV)}, doi = {10.1109/IV51561.2020.00048}, title = {Time-Aligned Edge Plots for Dynamic Graph Visualization}, year = 2020 }
2018
1. M. Abdelaal, M. Hlawatsch, M. Burch, und D. Weiskopf, „Clustering for Stacked Edge Splatting“, Vision, S. Modeling and Visualization, 2018, doi: 10.2312/VMV.20181262.
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  @article{https://doi.org/10.2312/vmv.20181262, author = {Abdelaal, Moataz and Hlawatsch, Marcel and Burch, Michael and Weiskopf, Daniel}, doi = {10.2312/VMV.20181262}, journal = {Vision}, pages = {Modeling and Visualization}, publisher = {The Eurographics Association}, title = {Clustering for Stacked Edge Splatting}, url = {https://diglib.eg.org/handle/10.2312/vmv20181262}, year = 2018 }
  Link
  https://diglib.eg.org/handle/10.2312/vmv20181262
2017
1. M. Abdelaal, F. Heimerl, und S. Koch, „ColTop: Visual Topic-Based Analysis of Scientific Community Structure“, in 2017 International Symposium on Big Data Visual Analytics (BDVA), in 2017 International Symposium on Big Data Visual Analytics (BDVA). Nov. 2017, S. 1–8. doi: 10.1109/BDVA.2017.8114622.
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  @inproceedings{8114622, abstract = {Personal networks formed within scientific communities and the collaborations they yield are one of the driving forces behind innovation and new discoveries. Luckily, successful collaboration produces analyzable data points in the form of publications that allow us to learn and understand some of the connections and collaborative structures in a scientific community. Co-author information is one important aspect of this, and various solutions to the fundamental visualization problems of co-author graphs exist. In this work, we introduce ColTop, a multi-level, interactive graph visualization system that allows users to effectively analyze publication data. It combines coauthor information with other meta-data and information extracted from textual content to support comprehensive analyses. ColTop includes a novel, heuristics-based approach to create a meaningful abstraction of co-author networks, and enriches them with topic information. To demonstrate the applicability of our approach, we discuss an example analysis scenario based on a practical data set.}, author = {Abdelaal, Moataz and Heimerl, Florian and Koch, Steffen}, booktitle = {2017 International Symposium on Big Data Visual Analytics (BDVA)}, doi = {10.1109/BDVA.2017.8114622}, month = {11}, pages = {1-8}, title = {ColTop: Visual Topic-Based Analysis of Scientific Community Structure}, year = 2017 }
2. M. Abdelaal, „Multi-time scale dynamic graph visualization“. Universität Stuttgart, 2017. doi: 10.18419/OPUS-9560.
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  @misc{https://doi.org/10.18419/opus-9560, author = {Abdelaal, Moataz}, doi = {10.18419/OPUS-9560}, language = {en}, publisher = {Universität Stuttgart}, title = {Multi-time scale dynamic graph visualization}, url = {http://elib.uni-stuttgart.de/handle/11682/9577}, year = 2017 }
  Link
  http://elib.uni-stuttgart.de/handle/11682/9577

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Theoretical and Methodological Foundations of Visual Computing

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Image Synthesis

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Moataz Abdelaal

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