Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/28075
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Author(s): Dividino, Renata
Gottron, Thomas
Scherp, Ansgar
Gröner, Gerd
Title: From changes to dynamics: Dynamics analysis of linked open data sources
Editor(s): Demidova, E
Dietze, S
Szymanski, J
Breslin, J
Citation: Dividino R, Gottron T, Scherp A & Gröner G (2014) From changes to dynamics: Dynamics analysis of linked open data sources. In: Demidova E, Dietze S, Szymanski J & Breslin J (eds.) Proceedings of the 1st International Workshop on Dataset Profiling & Federated Search for Linked Data co-located with the 11th Extended Semantic Web Conference (ESWC 2014)volume 1151. CEUR Workshop Proceedings, 1151. Profiles 2014, Anissaras, Greece, 26.05.2014-26.05.2014. Aachen, Germany: CEUR Workshop Proceedings
Issue Date: 31-Dec-2014
Series/Report no.: CEUR Workshop Proceedings, 1151
Conference Name: Profiles 2014
Conference Dates: 2014-05-26 - 2014-05-26
Conference Location: Anissaras, Greece
Abstract: The Linked Open Data (LOD) cloud changes frequently. Recent approaches focus mainly on quantifying the changes that occur in the LOD cloud by comparing two snapshots of a linked dataset captured at two different points in time. These change metrics are able to measure absolute changes between these two snapshots. However, they cannot determine the dynamics of a dataset over a period of time, i.e., the intensity of how the data evolved in this period. In this paper, we present a general framework to analyse the dynamics of linked datasets within a given time interval. We propose a function to measure the dynamics of a LOD dataset, which is defined as the aggregation of absolute, infinitesimal changes, provided by change metrics. Our method can be parametrised to incorporate and make use of existing change metrics. Furthermore, our framework enables the use of different decay functions within the dynamics computation for different weights on changes depending on when they occurred in the observed time interval. We apply our framework to conduct an investigation on the dynamics of selected LOD datasets. We apply our analysis on a large-scale LOD dataset that is obtained from the LOD cloud by weekly crawls over more than a year. Finally, we discuss the benefits and potential applications of our dynamics function in a real world scenario.
Status: VoR - Version of Record
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