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Title

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1		-A Machine Learning Approach for Node Size Approximation in Top-down Layout
	1	+Machine Learning Approach for Node Size Approximation in Top-down Layout

Content

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1		-Top-down Layout is a technique to draw large hierarchical diagrams from the root node downwards, scaling children down to fit in the space provided by their parents. This is in contrast to bottom-up layout where children are laid out first and the parents' dimensions are determined accordingly afterwards.
2		-
3	3	In top-down layout a strategy needs to be used to set node sizes without knowledge of the hierarchical contents of the node as that has not been processed/laid out at that point. Current strategies are:
4	4
5		-* Using a default base size
6		-* Counting the number of children and taking the square root as a multiplication factor for the default base size
7		-* Computing the layout of only the children (look-ahead layout)
	3	+* using a default base size
	4	+* counting the number of children and taking the square root as a multiplication factor for the default base size
	5	+* computing the layout of only the children (look-ahead layout)
8	8
9	9	The main challenge is to get an approximation that gives a suitable aspect ratio (close to what will actually be required).
10	10
11		-Graphs are complex feature vectors and the solution space is very large without necessarily one correct and optimal answer. Therefore, a machine learning (ML)-based approach may help find good solutions.
	9	+Because graphs are complex feature vectors and the solution space is very large without necessarily one correct and optimal answer a ML-based approach may help find good solutions.
12	12
13		-== Example Top-down Layout of an SCChart ==
	11	+This topic will be supervised in cooperation with the [[Intelligent Systems>>url:https://www.ins.informatik.uni-kiel.de/en||shape="rect"]] group.
14	14
15		-[[image:attach:Controller_topdown_v3.png]]
16		-
17	17	= Goals =
18	18
19		-* Use the KiCoDia benchmarking tool to extract feature vectors from existing models
20		-* Train and evaluate an ML model on the extracted data sets
21		-* Integrate the model as a new node size approximator into top-down layout
	15	+* use kicodia benchmarking tool to extract feature vectors from existing models
	16	+* train and evaluate an ML model on the extracted data sets
	17	+* integrate the model as a new node size approximator into top-down layout
22	22
	19	+== Example Top-down Layout of an SCChart ==
	20	+
	21	+[[image:attach:Controller_topdown_v3.png]]
	22	+
23	23	= Scope =
24	24
25		-Master's (Bachelor's) Thesis
	25	+Bachelor's/Master's Thesis
26	26
27	27	= Related Work/Literature =
28	28
29		-[Under Review] M. Kasperowski and R. von Hanxleden, //Top-down Layout: Utilizing Zoom as the Third Dimension//
	29	+[WIP] Top-down layout paper
30	30
31		-M. Nielsen, //Neural Networks and Deep Learning//, Determination Press, 2015 ([[http:~~/~~/neuralnetworksanddeeplearning.com/index.html>>url:http://neuralnetworksanddeeplearning.com/index.html||shape="rect"]])
	31	+[[http:~~/~~/neuralnetworksanddeeplearning.com/index.html>>url:http://neuralnetworksanddeeplearning.com/index.html||shape="rect"]]
32	32
33		-I. Goodfellow and Y. Bengio and A. Courville, //Deep Learning//, MIT Press, 2016 ([[https:~~/~~/www.deeplearningbook.org/>>url:https://www.deeplearningbook.org/||shape="rect"]])
	33	+[[https:~~/~~/www.deeplearningbook.org/>>url:https://www.deeplearningbook.org/||shape="rect"]]
34	34
35		-= Involved Languages/Technologies =
36		-
37		-* Java / Xtend, Python
38		-* KiCo
39		-* ML Frameworks (to be chosen)
40		-
41	41	= Supervised by =
42	42
43		-Maximilian Kasperowski in cooperation with the [[Intelligent Systems>>url:https://www.ins.informatik.uni-kiel.de/en||shape="rect"]] group.
44		-
45		-mka@informatik.uni-kiel.de
	37	+Maximilian Kasperowski

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Id

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1		-136183956
	1	+136183935

URL

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1		-https://rtsys.informatik.uni-kiel.de/confluence//wiki/spaces/RTSYS/pages/136183956/A Machine Learning Approach for Node Size Approximation in Top-down Layout
	1	+https://rtsys.informatik.uni-kiel.de/confluence//wiki/spaces/RTSYS/pages/136183935/Machine Learning Approach for Node Size Approximation in Top-down Layout