Reference list of sources used for two experimental data files dataBSRN and dataMixed


Autoria(s): Scherer, Maximilian; Bernard, Jürgen; Schreck, Tobias
Data(s)

21/01/2011

Resumo

Increasing amounts of data is collected in most areas of research and application. The degree to which this data can be accessed, analyzed, and retrieved, is a decisive in obtaining progress in fields such as scientific research or industrial production. We present a novel methodology supporting content-based retrieval and exploratory search in repositories of multivariate research data. In particular, our methods are able to describe two-dimensional functional dependencies in research data, e.g. the relationship between ination and unemployment in economics. Our basic idea is to use feature vectors based on the goodness-of-fit of a set of regression models to describe the data mathematically. We denote this approach Regressional Features and use it for content-based search and, since our approach motivates an intuitive definition of interestingness, for exploring the most interesting data. We apply our method on considerable real-world research datasets, showing the usefulness of our approach for user-centered access to research data in a Digital Library system.

Formato

text/tab-separated-values, 39400 data points

Identificador

https://doi.pangaea.de/10.1594/PANGAEA.756307

doi:10.1594/PANGAEA.756307

Idioma(s)

en

Publicador

PANGAEA

Relação

Scherer, Maximilian; Bernard, Jürgen; Schreck, Tobias (2011): Retrieval and exploratory search in multivariate research data repositories using regressional features. ACM/IEEE Joint Conference on Digital Libraries, doi:10.1145/1998076.1998144

Direitos

CC-BY: Creative Commons Attribution 3.0 Unported

Access constraints: unrestricted

Palavras-Chave #Author(s); Experiment; Persistent Identifier; Title; Year of Publication
Tipo

Dataset