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dc.contributor.authorMahlknecht G
dc.contributor.authorBöhlen M
dc.contributor.authorDignös A
dc.contributor.authorGamper J
dc.contributor.editorACM
dc.date.accessioned2017-11-16T11:05:30Z
dc.date.available2017-11-16T11:05:30Z
dc.date.issued2017
dc.identifier.isbn978-1-4503-5282-6
dc.identifier.urihttp://dx.doi.org/10.1145/3085504.3091115
dc.identifier.urihttp://doi.acm.org/10.1145/3085504.3091115
dc.identifier.urihttp://hdl.handle.net/10863/3880
dc.description.abstractIn this paper, we present the VISOR tool, which helps the user to explore data and their summary structures by visualizing the relationships between the size k of a data summary and the induced error. Given an ordered dataset, VISOR allows to vary the size k of a data summary and to immediately see the effect on the induced error, by visualizing the error and its dependency on k in an &epsis;-graph and Δ-graph, respectively. The user can easily explore different values of k and determine the best value for the summary size. VISOR allows also to compare different summarization methods, such as piecewise constant approximation, piecewise aggregation approximation or V-optimal histograms. We show several demonstration scenarios, including how to determine an appropriate value for the summary size and comparing different summarization techniques.en_US
dc.language.isoenen_US
dc.publisherACMen_US
dc.rights
dc.titleVISOR: Visualizing Summaries of Ordered Dataen_US
dc.typeBook chapteren_US
dc.date.updated2017-07-31T10:12:37Z
dc.publication.titleSSDBM '17 Proceedings of the 29th International Conference on Scientific and Statistical Database Management, Chicago, IL, USA, June 27 - 29, 2017
dc.language.isiEN-GB
dc.description.fulltextnoneen_US


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