We argue that there is a need for substantially more research on the use of generative data models in the validation and evaluation of visualization techniques. For example, user studies will require the display of representative and uncon-founded visual stimuli, while algorithms will need functional coverage and assessable benchmarks. However, data is often collected in a semi-automatic fashion or entirely hand-picked, which obscures the view of generality, impairs availability, and potentially violates privacy. There are some sub-domains of visualization that use synthetic data in the sense of generative data models, whereas others work with real-world-based data sets and simulations. Depending on the visualization domain, many generative data models are "side projects" as part of an ad-hoc validation of a techniques paper and thus neither reusable nor general-purpose. We review existing work on popular data collections and generative data models in visualization to discuss the opportunities and consequences for technique validation, evaluation, and experiment design. We distill handling and future directions, and discuss how we can engineer generative data models and how visualization research could benefit from more and better use of generative data models.
Description
Generative Data Models for Validation and Evaluation of Visualization Techniques
%0 Conference Paper
%1 2016SchulzGenerativeDataModels
%A Schulz, Christoph
%A Nocaj, Arlind
%A El-Assady, Mennatallah
%A Frey, Steffen
%A Hlawatsch, Marcel
%A Hund, Michael
%A Karch, Grzegorz Karol
%A Netzel, Rudolf
%A Schätzle, Christin
%A Butt, Miriam
%A Keim, Daniel A.
%A Ertl, Thomas
%A Brandes, Ulrik
%A Weiskopf, Daniel
%B Proceedings of the Workshop on Beyond Time and Errors: Novel Evaluation Methods for Visualization (BELIV)
%D 2016
%I ACM
%K A01 visus:hlawatml A02 A03 B02 sfbtrr161 D02 visus:netzelrf visus:ertl visus vis-ertl visus:karchgz visus:weiskopf 2016 vis-gis from:mueller vis(us) sfbtrr75 visus:schulzch visus:freysn
%P 112-124
%R 10.1145/2993901.2993907
%T Generative Data Models for Validation and Evaluation of Visualization Techniques
%U http://dx.doi.org/10.1145/2993901.2993907
%X We argue that there is a need for substantially more research on the use of generative data models in the validation and evaluation of visualization techniques. For example, user studies will require the display of representative and uncon-founded visual stimuli, while algorithms will need functional coverage and assessable benchmarks. However, data is often collected in a semi-automatic fashion or entirely hand-picked, which obscures the view of generality, impairs availability, and potentially violates privacy. There are some sub-domains of visualization that use synthetic data in the sense of generative data models, whereas others work with real-world-based data sets and simulations. Depending on the visualization domain, many generative data models are "side projects" as part of an ad-hoc validation of a techniques paper and thus neither reusable nor general-purpose. We review existing work on popular data collections and generative data models in visualization to discuss the opportunities and consequences for technique validation, evaluation, and experiment design. We distill handling and future directions, and discuss how we can engineer generative data models and how visualization research could benefit from more and better use of generative data models.
@inproceedings{2016SchulzGenerativeDataModels,
abstract = {We argue that there is a need for substantially more research on the use of generative data models in the validation and evaluation of visualization techniques. For example, user studies will require the display of representative and uncon-founded visual stimuli, while algorithms will need functional coverage and assessable benchmarks. However, data is often collected in a semi-automatic fashion or entirely hand-picked, which obscures the view of generality, impairs availability, and potentially violates privacy. There are some sub-domains of visualization that use synthetic data in the sense of generative data models, whereas others work with real-world-based data sets and simulations. Depending on the visualization domain, many generative data models are "side projects" as part of an ad-hoc validation of a techniques paper and thus neither reusable nor general-purpose. We review existing work on popular data collections and generative data models in visualization to discuss the opportunities and consequences for technique validation, evaluation, and experiment design. We distill handling and future directions, and discuss how we can engineer generative data models and how visualization research could benefit from more and better use of generative data models.},
added-at = {2020-10-09T12:34:23.000+0200},
author = {Schulz, Christoph and Nocaj, Arlind and El-Assady, Mennatallah and Frey, Steffen and Hlawatsch, Marcel and Hund, Michael and Karch, Grzegorz Karol and Netzel, Rudolf and Schätzle, Christin and Butt, Miriam and Keim, Daniel A. and Ertl, Thomas and Brandes, Ulrik and Weiskopf, Daniel},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/281f59bfcd0ec63db13bfc6ce23757e2b/visus},
booktitle = {Proceedings of the Workshop on Beyond Time and Errors: Novel Evaluation Methods for Visualization (BELIV)},
description = {Generative Data Models for Validation and Evaluation of Visualization Techniques},
doi = {10.1145/2993901.2993907},
interhash = {44fc915ec2c38e311bc349ec6601324b},
intrahash = {81f59bfcd0ec63db13bfc6ce23757e2b},
keywords = {A01 visus:hlawatml A02 A03 B02 sfbtrr161 D02 visus:netzelrf visus:ertl visus vis-ertl visus:karchgz visus:weiskopf 2016 vis-gis from:mueller vis(us) sfbtrr75 visus:schulzch visus:freysn},
pages = {112-124},
publisher = {ACM},
timestamp = {2020-10-09T10:34:23.000+0200},
title = {Generative Data Models for Validation and Evaluation of Visualization Techniques},
url = {http://dx.doi.org/10.1145/2993901.2993907},
year = 2016
}
