@article{ScheibelTrappLimbergeretal.2020,
  author    = {Scheibel, Willy and Trapp, Matthias and Limberger, Daniel and D{\"o}llner, J{\"u}rgen Roland Friedrich},
  title     = {A taxonomy of treemap visualization techniques},
  series = {Science and Technology Publications},
  journal   = {Science and Technology Publications},
  publisher = {Springer},
  address   = {Berlin},
  pages     = {8},
  year      = {2020},
  abstract  = {A treemap is a visualization that has been specifically designed to facilitate the exploration of tree-structured data and, more general, hierarchically structured data. The family of visualization techniques that use a visual metaphor for parent-child relationships based "on the property of containment" (Johnson, 1993) is commonly referred to as treemaps. However, as the number of variations of treemaps grows, it becomes increasingly important to distinguish clearly between techniques and their specific characteristics. This paper proposes to discern between Space-filling Treemap TS, Containment Treemap TC, Implicit Edge Representation Tree TIE, and Mapped Tree TMT for classification of hierarchy visualization techniques and highlights their respective properties. This taxonomy is created as a hyponymy, i.e., its classes have an is-a relationship to one another: TS TC TIE TMT. With this proposal, we intend to stimulate a discussion on a more unambiguous classification of treemaps and, furthermore, broaden what is understood by the concept of treemap itself.},
  language  = {en}
}
@misc{ScheibelTrappLimbergeretal.2020,
  author    = {Scheibel, Willy and Trapp, Matthias and Limberger, Daniel and D{\"o}llner, J{\"u}rgen Roland Friedrich},
  title     = {A taxonomy of treemap visualization techniques},
  series = {Postprints der Universit{\"a}t Potsdam : Reihe der Digital Engineering Fakult{\"a}t},
  journal   = {Postprints der Universit{\"a}t Potsdam : Reihe der Digital Engineering Fakult{\"a}t},
  number    = {8},
  doi       = {10.25932/publishup-52469},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-524693},
  pages     = {10},
  year      = {2020},
  abstract  = {A treemap is a visualization that has been specifically designed to facilitate the exploration of tree-structured data and, more general, hierarchically structured data. The family of visualization techniques that use a visual metaphor for parent-child relationships based "on the property of containment" (Johnson, 1993) is commonly referred to as treemaps. However, as the number of variations of treemaps grows, it becomes increasingly important to distinguish clearly between techniques and their specific characteristics. This paper proposes to discern between Space-filling Treemap TS, Containment Treemap TC, Implicit Edge Representation Tree TIE, and Mapped Tree TMT for classification of hierarchy visualization techniques and highlights their respective properties. This taxonomy is created as a hyponymy, i.e., its classes have an is-a relationship to one another: TS TC TIE TMT. With this proposal, we intend to stimulate a discussion on a more unambiguous classification of treemaps and, furthermore, broaden what is understood by the concept of treemap itself.},
  language  = {en}
}
@misc{StojanovicTrappRichteretal.2018,
  author    = {Stojanovic, Vladeta and Trapp, Matthias and Richter, Rico and D{\"o}llner, J{\"u}rgen Roland Friedrich},
  title     = {A service-oriented approach for classifying 3D points clouds by example of office furniture classification},
  series = {Web3D 2018: Proceedings of the 23rd International ACM Conference on 3D Web Technology},
  journal   = {Web3D 2018: Proceedings of the 23rd International ACM Conference on 3D Web Technology},
  publisher = {Association for Computing Machinery},
  address   = {New York},
  isbn      = {978-1-4503-5800-2},
  doi       = {10.1145/3208806.3208810},
  pages     = {1 -- 9},
  year      = {2018},
  abstract  = {The rapid digitalization of the Facility Management (FM) sector has increased the demand for mobile, interactive analytics approaches concerning the operational state of a building. These approaches provide the key to increasing stakeholder engagement associated with Operation and Maintenance (O\&M) procedures of living and working areas, buildings, and other built environment spaces. We present a generic and fast approach to process and analyze given 3D point clouds of typical indoor office spaces to create corresponding up-to-date approximations of classified segments and object-based 3D models that can be used to analyze, record and highlight changes of spatial configurations. The approach is based on machine-learning methods used to classify the scanned 3D point cloud data using 2D images. This approach can be used to primarily track changes of objects over time for comparison, allowing for routine classification, and presentation of results used for decision making. We specifically focus on classification, segmentation, and reconstruction of multiple different object types in a 3D point-cloud scene. We present our current research and describe the implementation of these technologies as a web-based application using a services-oriented methodology.},
  language  = {en}
}