### Resumé

Sprog | Engelsk |
---|---|

Tidsskrift | Omega: The International Journal of Management Science |

Vol/bind | 86 |

Sider | 125-136 |

Antal sider | 12 |

ISSN | 0305-0483 |

DOI | |

Status | Udgivet - 2019 |

### Bibliografisk note

Published online: 26. July 2018

### Emneord

- Visualization
- Dynamic magnitude
- Multidimensional scaling
- Difference of convex optimization

### Citer dette

*Omega: The International Journal of Management Science*,

*86*, 125-136. DOI: 10.1016/j.omega.2018.07.008

}

*Omega: The International Journal of Management Science*, bind 86, s. 125-136. DOI: 10.1016/j.omega.2018.07.008

**Visualization of Complex Dynamic Datasets by Means of Mathematical Optimization.** / Carrizosa, Emilio; Guerrero, Vanesa; Romero Morales, Dolores .

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › peer review

TY - JOUR

T1 - Visualization of Complex Dynamic Datasets by Means of Mathematical Optimization

AU - Carrizosa,Emilio

AU - Guerrero,Vanesa

AU - Romero Morales,Dolores

N1 - Published online: 26. July 2018

PY - 2019

Y1 - 2019

N2 - In this paper we propose an optimization model and a solution approach to visualize datasets which are made up of individuals observed along different time periods. These individuals have attached a time-dependent magnitude and a dissimilarity measure, which may vary over time. Difference of convex optimization techniques, namely, the so-called Difference of Convex Algorithm, and nonconvex quadratic binary optimization techniques are used to heuristically solve the optimization model and develop this visualization framework. This way, the so-called Dynamic Visualization Map is obtained, in which the individuals are represented by geometric objects chosen from a catalogue. A Dynamic Visualization Map faithfully represents the dynamic magnitude by means of the areas of the objects, while it trades off three different goodness of fit criteria, namely the correct match of the dissimilarities between the individuals and the distances between the objects representing them, the spreading of such objects in the visual region, and the preservation of the mental map by ensuring smooth transitions along snapshots. Our procedure is successfully tested on dynamic geographic and linguistic datasets.

AB - In this paper we propose an optimization model and a solution approach to visualize datasets which are made up of individuals observed along different time periods. These individuals have attached a time-dependent magnitude and a dissimilarity measure, which may vary over time. Difference of convex optimization techniques, namely, the so-called Difference of Convex Algorithm, and nonconvex quadratic binary optimization techniques are used to heuristically solve the optimization model and develop this visualization framework. This way, the so-called Dynamic Visualization Map is obtained, in which the individuals are represented by geometric objects chosen from a catalogue. A Dynamic Visualization Map faithfully represents the dynamic magnitude by means of the areas of the objects, while it trades off three different goodness of fit criteria, namely the correct match of the dissimilarities between the individuals and the distances between the objects representing them, the spreading of such objects in the visual region, and the preservation of the mental map by ensuring smooth transitions along snapshots. Our procedure is successfully tested on dynamic geographic and linguistic datasets.

KW - Visualization

KW - Dynamic magnitude

KW - Multidimensional scaling

KW - Difference of convex optimization

KW - Visualization

KW - Dynamic magnitude

KW - Multidimensional scaling

KW - Difference of convex optimization

UR - https://sfx-45cbs.hosted.exlibrisgroup.com/45cbs?url_ver=Z39.88-2004&url_ctx_fmt=info:ofi/fmt:kev:mtx:ctx&ctx_enc=info:ofi/enc:UTF-8&ctx_ver=Z39.88-2004&rfr_id=info:sid/sfxit.com:azlist&sfx.ignore_date_threshold=1&rft.object_id=954921388918&rft.object_portfolio_id=&svc.holdings=yes&svc.fulltext=yes

U2 - 10.1016/j.omega.2018.07.008

DO - 10.1016/j.omega.2018.07.008

M3 - Journal article

VL - 86

SP - 125

EP - 136

JO - Omega: The International Journal of Management Science

T2 - Omega: The International Journal of Management Science

JF - Omega: The International Journal of Management Science

SN - 0305-0483

ER -