Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162143
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dc.contributor.authorWang, Yan Chaoen_US
dc.contributor.authorXing, Yidanen_US
dc.contributor.authorLin, Fengen_US
dc.contributor.authorSeah, Hock Soonen_US
dc.contributor.authorZhang, Jieen_US
dc.date.accessioned2022-10-05T07:08:43Z-
dc.date.available2022-10-05T07:08:43Z-
dc.date.issued2022-
dc.identifier.citationWang, Y. C., Xing, Y., Lin, F., Seah, H. S. & Zhang, J. (2022). OST: a heuristic-based orthogonal partitioning algorithm for dynamic hierarchical data visualization. Journal of Visualization, 25(4), 875-896. https://dx.doi.org/10.1007/s12650-022-00830-1en_US
dc.identifier.issn1343-8875en_US
dc.identifier.urihttps://hdl.handle.net/10356/162143-
dc.description.abstractTools for intuitive visualization of dynamic datasets are highly demanded for capturing information and revealing potential patterns, especially in understanding the trend of data changes. We propose a novel resolution-independent heuristic algorithm, termed Orthogonal Stable Treemap (OST), to implicitly display dynamic hierarchical data value changes. OST adopts a site-based method as the Voronoi treemap (VT), to preserve the layout stability for diversified data values. Meanwhile, OST partitions the whole canvas with horizontal or vertical lines, instead of the lines with arbitrary orientations in VT. Technical innovations are made in three parts: Initialization of site state to speed up the algorithm and preserve the layout; efficient computation of orthogonal rectangular diagram to partition the empty canvas; self-adaption of site state to quickly reach an equilibrium. The performance of OST is quantitatively evaluated in terms of computation complexity, computation time, convergence rate, visibility, and stability. Moreover, qualitative evaluations (use case and user study) are demonstrated on the dynamic work-in-process dataset in the wafer fab. Evaluation results show that OST combines the advantages of layout stability and tidiness, contributing to easier and faster plot understanding.en_US
dc.description.sponsorshipAgency for Science, Technology and Research (A*STAR)en_US
dc.description.sponsorshipMinistry of Education (MOE)en_US
dc.language.isoenen_US
dc.relationA19C1a0018en_US
dc.relationMOE 2017-T1-001-053-04en_US
dc.relation.ispartofJournal of Visualizationen_US
dc.rights© 2022 The Visualization Society of Japan. All rights reserved.en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleOST: a heuristic-based orthogonal partitioning algorithm for dynamic hierarchical data visualizationen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.identifier.doi10.1007/s12650-022-00830-1-
dc.identifier.scopus2-s2.0-85125400824-
dc.identifier.issue4en_US
dc.identifier.volume25en_US
dc.identifier.spage875en_US
dc.identifier.epage896en_US
dc.subject.keywordsOrthogonal Rectanglesen_US
dc.subject.keywordsTreemapen_US
dc.description.acknowledgementThis work was partially supported by the A*STAR Cyber-Physical Production System (CPPS)-Towards Contextual and Intelligent Response Research Program, under the RIE2020 IAF-PP Grant A19C1a0018, and Model Factory @SIMTech. This work is also partially supported by a Grant MOE 2017-T1-001-053-04 from Ministry of Education, Singapore.en_US
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