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A Benchmark for Content-Based Retrieval in Bivariate Data Collections

Scherer, Maximilian and Landesberger, Tatiana von and Schreck, Tobias (2012):
A Benchmark for Content-Based Retrieval in Bivariate Data Collections.
Springer, Berlin, Heidelberg, New York, In: Theory and Practice of Digital Libraries, In: Lecture Notes in Computer Science (LNCS); 7489, DOI: 10.1007/978-3-642-33290-6₃₁, [Conference or Workshop Item]

Abstract

Huge amounts of various research data are produced and made publicly available in digital libraries. An important category is bivariate data (measurements of one variable versus the other). Examples of bivariate data include observations of temperature and ozone levels (e.g., in environmental observation), domestic production and unemployment (e.g., in economics), or education and income level levels (in the social sciences). For accessing these data, content-based retrieval is an important query modality. It allows researchers to search for specific relationships among data variables (e.g., quadratic dependence of temperature on altitude). However, such retrieval is to date a challenge, as it is not clear which similarity measures to apply. Various approaches have been proposed, yet no benchmarks to compare their retrieval effectiveness have been defined. In this paper, we construct a benchmark for retrieval of bivariate data. It is based on a large collection of bivariate research data. To define similarity classes, we use category information that was annotated by domain experts. The resulting similarity classes are used to compare several recently proposed content-based retrieval approaches for bivariate data, by means of precision and recall. This study is the first to present an encompassing benchmark data set and compare the performance of respective techniques. We also identify potential research directions based on the results obtained for bivariate data. The benchmark and implementations of similarity functions are made available, to foster research in this emerging area of content-based retrieval.

Item Type: Conference or Workshop Item
Erschienen: 2012
Creators: Scherer, Maximilian and Landesberger, Tatiana von and Schreck, Tobias
Title: A Benchmark for Content-Based Retrieval in Bivariate Data Collections
Language: English
Abstract:

Huge amounts of various research data are produced and made publicly available in digital libraries. An important category is bivariate data (measurements of one variable versus the other). Examples of bivariate data include observations of temperature and ozone levels (e.g., in environmental observation), domestic production and unemployment (e.g., in economics), or education and income level levels (in the social sciences). For accessing these data, content-based retrieval is an important query modality. It allows researchers to search for specific relationships among data variables (e.g., quadratic dependence of temperature on altitude). However, such retrieval is to date a challenge, as it is not clear which similarity measures to apply. Various approaches have been proposed, yet no benchmarks to compare their retrieval effectiveness have been defined. In this paper, we construct a benchmark for retrieval of bivariate data. It is based on a large collection of bivariate research data. To define similarity classes, we use category information that was annotated by domain experts. The resulting similarity classes are used to compare several recently proposed content-based retrieval approaches for bivariate data, by means of precision and recall. This study is the first to present an encompassing benchmark data set and compare the performance of respective techniques. We also identify potential research directions based on the results obtained for bivariate data. The benchmark and implementations of similarity functions are made available, to foster research in this emerging area of content-based retrieval.

Series Name: Lecture Notes in Computer Science (LNCS); 7489
Publisher: Springer, Berlin, Heidelberg, New York
Uncontrolled Keywords: Forschungsgruppe Visual Search and Analysis (VISA), Visual analytics, Digital libraries, Information retrieval, Benchmarking, Content based retrieval, Feature extraction, Search, Bivariate data
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
Event Title: Theory and Practice of Digital Libraries
Date Deposited: 12 Nov 2018 11:16
DOI: 10.1007/978-3-642-33290-6₃₁
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