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Title

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1		-Machine Learning Approach for Node Size Approximation in Top-down Layout
	1	+A 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	+
1	1	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:
2	2
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)
	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)
6	6
7	7	The main challenge is to get an approximation that gives a suitable aspect ratio (close to what will actually be required).
8	8
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.
	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.
10	10
11	11	This topic will be supervised in cooperation with the [[Intelligent Systems>>url:https://www.ins.informatik.uni-kiel.de/en||shape="rect"]] group.
12	12
13		-= Goals =
14		-
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
18		-
19	19	== Example Top-down Layout of an SCChart ==
20	20
21	21	[[image:attach:Controller_topdown_v3.png]]
22	22
	19	+= Goals =
	20	+
	21	+* Use the KiCoDia benchmarking tool to extract feature vectors from existing models
	22	+* Train and evaluate an ML model on the extracted data sets
	23	+* Integrate the model as a new node size approximator into top-down layout
	24	+
23	23	= Scope =
24	24
25		-Bachelor's/Master's Thesis
	27	+Master's (Bachelor's) Thesis
26	26
27	27	= Related Work/Literature =
28	28
29		-[WIP] Top-down layout paper
	31	+[Under Review] M. Kasperowski and R. von Hanxleden, //Top-down Layout: Utilizing Zoom as the Third Dimension//
30	30
31		-[[http:~~/~~/neuralnetworksanddeeplearning.com/index.html>>url:http://neuralnetworksanddeeplearning.com/index.html||shape="rect"]]
	33	+M. Nielsen, //Neural Networks and Deep Learning//, Determination Press, 2015 ([[http:~~/~~/neuralnetworksanddeeplearning.com/index.html>>url:http://neuralnetworksanddeeplearning.com/index.html||shape="rect"]])
32	32
33		-[[https:~~/~~/www.deeplearningbook.org/>>url:https://www.deeplearningbook.org/||shape="rect"]]
	35	+I. Goodfellow and Y. Bengio and A. Courville, //Deep Learning//, MIT Press, 2016 ([[https:~~/~~/www.deeplearningbook.org/>>url:https://www.deeplearningbook.org/||shape="rect"]])
34	34
	37	+= Involved Languages/Technologies =
	38	+
	39	+* Java / Xtend, Python
	40	+* KiCo
	41	+* ML Frameworks (to be chosen)
	42	+
35	35	= Supervised by =
36	36
37	37	Maximilian Kasperowski
	46	+
	47	+mka@informatik.uni-kiel.de

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Id

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1		-136183935
	1	+136183954

URL

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1		-https://rtsys.informatik.uni-kiel.de/confluence//wiki/spaces/RTSYS/pages/136183935/Machine Learning Approach for Node Size Approximation in Top-down Layout
	1	+https://rtsys.informatik.uni-kiel.de/confluence//wiki/spaces/RTSYS/pages/136183954/A Machine Learning Approach for Node Size Approximation in Top-down Layout