Scherer, Maximilian ; Landesberger von Antburg, Tatiana ; Schreck, Tobias (2013)
Topic Modeling for Search and Exploration in Multivariate Research Data Repositories.
Research and Advanced Technology for Digital Libraries.
doi: 10.1007/978-3-642-40501-3_39
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Huge amounts of multivariate research data are produced and made publicly available in digital libraries. Little research focused on similarity functions that take multivariate data documents as a whole into account. Such similarity functions are highly beneficial for users, by enabling them to browse and query large collections of multivariate data using nearest-neighbor indexing. In this paper we tackle this challenge and propose a novel similarity function for multivariate data documents based on topic-modeling. Based on a previously developed bag-of-words approach for multivariate data, we can then learn a topic model for a collection of multivariate data documents and represent each document as a mixture of topics. This representation is very suitable for efficient nearest-neighbor indexing and clustering according to the topic distribution of a document. We present a use-case where we apply this approach to retrieval of multivariate data in the field of climate research.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2013 |
| Autor(en): | Scherer, Maximilian ; Landesberger von Antburg, Tatiana ; Schreck, Tobias |
| Art des Eintrags: | Bibliographie |
| Titel: | Topic Modeling for Search and Exploration in Multivariate Research Data Repositories |
| Sprache: | Englisch |
| Publikationsjahr: | 2013 |
| Verlag: | Springer, Berlin, Heidelberg, New York |
| Reihe: | Lecture Notes in Computer Science (LNCS); 8092 |
| Veranstaltungstitel: | Research and Advanced Technology for Digital Libraries |
| DOI: | 10.1007/978-3-642-40501-3_39 |
| Kurzbeschreibung (Abstract): | Huge amounts of multivariate research data are produced and made publicly available in digital libraries. Little research focused on similarity functions that take multivariate data documents as a whole into account. Such similarity functions are highly beneficial for users, by enabling them to browse and query large collections of multivariate data using nearest-neighbor indexing. In this paper we tackle this challenge and propose a novel similarity function for multivariate data documents based on topic-modeling. Based on a previously developed bag-of-words approach for multivariate data, we can then learn a topic model for a collection of multivariate data documents and represent each document as a mixture of topics. This representation is very suitable for efficient nearest-neighbor indexing and clustering according to the topic distribution of a document. We present a use-case where we apply this approach to retrieval of multivariate data in the field of climate research. |
| Freie Schlagworte: | Forschungsgruppe Visual Search and Analysis (VISA), Multivariate data, Content based retrieval, Bag-of-words |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme |
| Hinterlegungsdatum: | 12 Nov 2018 11:16 |
| Letzte Änderung: | 22 Jul 2021 18:31 |
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