@inbook{PB-VRVis-2017-015,
author = {Matkovi{\'c}, Kresimir and Abraham, Hrvoje and Hauser, Helwig},
title = {Quantitative Externalization of Visual Data Analysis Results Using Local Regression Models},
year = {2017},
booktitle = {Machine Learning and Knowledge Extraction: First IFIP TC 5,
  WG 8.4, 8.9, 12.9 International Cross-Domain Conference,
  CD-MAKE 2017, Reggio, Italy, August 29 -- September 1, 2017,
  Proceedings},
doi = {10.1007/978-3-319-66808-6_14},
editor = {Holzinger, Andreas and Kieseberg, Peter
  and Tjoa, A Min and Weippl, Edgar},
url = {https://www.vrvis.at/publications/PB-VRVis-2017-015},
publisher = {Springer International Publishing},
isbn = {978-3-319-66808-6},
pages = {199--218},
abstract = {Both interactive visualization and computational analysis  methods are useful for data studies and an integration of both  approaches is promising to successfully combine the  benefits of both methodologies. In interactive data  exploration and analysis workflows, we need successful  means to quantitatively externalize results from data  studies, amounting to a particular challenge for the  usually qualitative visual data analysis. In this paper,  we propose a hybrid approach in order to quantitatively  externalize valuable findings from interactive visual data  exploration and analysis, based on local linear regression  models. The models are built on user-selected subsets of  the data, and we provide a way of keeping track of these  models and comparing them. As an additional benefit, we  also provide the user with the numeric model coefficients.  Once the models are available, they can be used in  subsequent steps of the workflow. A model-based  optimization can then be performed, for example, or more  complex models can be reconstructed using an inversion of  the local models. We study two datasets to exemplify the  proposed approach, a meteorological data set for  illustration purposes and a simulation ensemble from the  automotive industry as an actual case study.},
}