@article{musleh-2023-ugi, title = "Uncertainty guidance in proton therapy planning visualization", author = "Maath Musleh and Ludvig Paul Muren and Laura Toussaint and Anne Vestergaard and Eduard Gr\"{o}ller and Renata Raidou", year = "2023", abstract = "We investigate uncertainty guidance mechanisms to support proton therapy (PT) planning visualization. Uncertainties in the PT workflow pose significant challenges for navigating treatment plan data and selecting the most optimal plan among alternatives. Although guidance techniques have not yet been applied to PT planning scenarios, they have successfully supported sense- and decision-making processes in other contexts. We hypothesize that augmenting PT uncertainty visualization with guidance may influence the intended users' perceived confidence and provide new insights. To this end, we follow an iterative co-design process with domain experts to develop a visualization dashboard enhanced with distinct level-of-detail uncertainty guidance mechanisms. Our approach classifies uncertainty guidance into two dimensions: degree of intrusiveness and detail-orientation. Our dashboard supports the comparison of multiple treatment plans (i.e., nominal plans with their translational variations) while accounting for multiple uncertainty factors. We subsequently evaluate the designed and developed strategies by assessing perceived confidence and effectiveness during a sense- and decision-making process. Our findings indicate that uncertainty guidance in PT planning visualization does not necessarily impact the perceived confidence of the users in the process. Nonetheless, it provides new insights and raises uncertainty awareness during treatment plan selection. This observation was particularly evident for users with longer experience in PT planning.", month = feb, doi = "10.1016/j.cag.2023.02.002", issn = "1873-7684", journal = "Computers and Graphics", volume = "111", publisher = "PERGAMON-ELSEVIER SCIENCE LTD", pages = "166--179", keywords = "Visual analytics, Applied computing, Decision support systems", URL = "https://www.cg.tuwien.ac.at/research/publications/2023/musleh-2023-ugi/", } @article{musleh-2022-mam5, title = "Visual analysis of blow molding machine multivariate time series data", author = "Maath Musleh and Angelos Chatzimparmpas and Ilir Jusufi", year = "2022", abstract = "The recent development in the data analytics field provides a boost in production for modern industries. Small-sized factories intend to take full advantage of the data collected by sensors used in their machinery. The ultimate goal is to minimize cost and maximize quality, resulting in an increase in profit. In collaboration with domain experts, we implemented a data visualization tool to enable decision-makers in a plastic factory to improve their production process. The tool is an interactive dashboard with multiple coordinated views supporting the exploration from both local and global perspectives. In summary, we investigate three different aspects: methods for preprocessing multivariate time series data, clustering approaches for the already refined data, and visualization techniques that aid domain experts in gaining insights into the different stages of the production process. Here we present our ongoing results grounded in a human-centered development process. We adopt a formative evaluation approach to continuously upgrade our dashboard design that eventually meets partners’ requirements and follows the best practices within the field. We also conducted a case study with a domain expert to validate the potential application of the tool in the real-life context. Finally, we assessed the usability and usefulness of the tool with a two-layer summative evaluation that showed encouraging results.", month = jul, doi = "10.1007/s12650-022-00857-4", issn = "1875-8975", journal = "Journal of Visualization", pages = "14", volume = "25", publisher = "Springer", pages = "1329--1342", keywords = "Time series data, Unsupervised machine learning, Visualization", URL = "https://www.cg.tuwien.ac.at/research/publications/2022/musleh-2022-mam5/", } @misc{musleh_maath_2021_mam, title = "Agritology: A Decision Support System for Local Farmers in Malta and Palestine", author = "Francesca Gauci and Maath Musleh", year = "2021", abstract = " In this project, we are utilizing the potential of Semantic Web in organizing shareable knowledge. We constructed an ontology of the farming process that is reusable and interoperable in the domain of Agriculture. The ontology supports the decision making of farmers in Malta and Palestine. The web application uses the ontology to share knowledge based primarily on user input and other external data sources. In addition to English, information is also presented in Maltese and Arabic in aim to preserve domain-specific vocabulary in these languages.", month = sep, journal = "CEUR Workshop Proceedings ", location = "Bolzano, Italy", event = "3rd International Workshop on Semantics for Biodiversity (S4BioDiv)", pages = "5", Conference date = "Poster presented at 3rd International Workshop on Semantics for Biodiversity (S4BioDiv) (2021-09-11--2021-09-18)", note = "1--5", pages = "1 – 5", keywords = "Semantic Web, Urban Agriculture, Ontology", URL = "https://www.cg.tuwien.ac.at/research/publications/2021/musleh_maath_2021_mam/", } @inproceedings{musleh_maath-2021-mam1, title = "Visual Analysis of Industrial Multivariate Time Series", author = "Maath Musleh and Angelos Chatzimparmpas and Ilir Jusufi", year = "2021", abstract = "The recent development in the data analytics field provides a boost in production for modern industries. Small-sized factories intend to take full advantage of the data collected by sensors used in their machinery. The ultimate goal is to minimize cost and maximize quality, resulting in an increase in profit. In collaboration with domain experts, we implemented a data visualization tool to enable decision-makers in a plastic factory to improve their production process. We investigate three different aspects: methods for preprocessing multivariate time series data, clustering approaches for the already refined data, and visualization techniques that aid domain experts in gaining insights into the different stages of the production process. Here we present our ongoing results grounded in a human-centered development process. We adopt a formative evaluation approach to continuously upgrade our dashboard design that eventually meets partners' requirements and follows the best practices within the field.", month = sep, isbn = "9781450386470", series = "VINCI 2021", publisher = "Association for Computing Machinery", note = "Best Short Paper Award", location = "Potsdam, germany", address = "New York, NY, USA", event = "VINCI 2021: The 14th International Symposium on Visual Information Communication and Interaction", editor = "Karsten Klein, Michael Burch, Daniel Limberger, Matthias Trapp", doi = "10.1145/3481549.3481557", booktitle = "The 14th International Symposium on Visual Information Communication and Interaction", pages = "5", pages = "1--5", keywords = "time series data, unsupervised machine learning, visualization", URL = "https://www.cg.tuwien.ac.at/research/publications/2021/musleh_maath-2021-mam1/", } @mastersthesis{musleh_maath-2021-mam2, title = "Visual Analysis of Industrial Multivariate Time-Series Data: Effective Solution to Maximise Insights from Blow Moulding Machine Sensory Data", author = "Maath Musleh", year = "2021", abstract = "Developments in the field of data analytics provides a boost for small-sized factories. These factories are eager to take full advantage of the potential insights in the remotely collected data to minimise cost and maximise quality and profit. This project aims to process, cluster and visualise sensory data of a blow moulding machine in a plastic production factory. In collaboration with Lean Automation, we aim to develop a data visualisation solution to enable decision-makers in a plastic factory to improve their production process. We will investigate three different aspects of the solution: methods for processing multivariate time-series data, clustering approaches for the sensory-data cultivated, and visualisation techniques that maximises production process insights. We use a formative evaluation method to develop a solution that meets partners' requirements and best practices within the field. Through building the MTSI dashboard tool, we hope to answer questions on optimal techniques to represent, cluster and visualise multivariate time series data. ", month = jun, address = "Favoritenstrasse 9-11/E193-02, A-1040 Vienna, Austria", school = "Research Unit of Computer Graphics, Institute of Visual Computing and Human-Centered Technology, Faculty of Informatics, TU Wien", keywords = "multivariate time-series, visual analytics, automated factories", URL = "https://www.cg.tuwien.ac.at/research/publications/2021/musleh_maath-2021-mam2/", } @runphdthesis{Musleh_PhD, title = "Unknown", author = "Maath Musleh", year = "2021", URL = "https://www.cg.tuwien.ac.at/research/publications/2021/Musleh_PhD/", }