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		<citationkey>AlencarOliPauMinAnd:2007:SiViTi</citationkey>
		<title>Temporal-PEx: similarity-based visualization of time series</title>
		<format>On-line</format>
		<year>2007</year>
		<numberoffiles>1</numberoffiles>
		<size>513 KiB</size>
		<author>Alencar, Aretha Barbosa,</author>
		<author>Oliveira, Maria Cristina Ferreira de,</author>
		<author>Paulovich, Fernando Vieira,</author>
		<author>Minghim, Rosane,</author>
		<author>Andrade, Marinho Gomes de,</author>
		<affiliation>Universidade de São Paulo</affiliation>
		<affiliation>Universidade de São Paulo</affiliation>
		<affiliation>Universidade de São Paulo</affiliation>
		<affiliation>Universidade de São Paulo</affiliation>
		<affiliation>Universidade de São Paulo</affiliation>
		<editor>Gonçalves, Luiz,</editor>
		<editor>Wu, Shin Ting,</editor>
		<conferencename>Brazilian Symposium on Computer Graphics and Image Processing, 20 (SIBGRAPI)</conferencename>
		<conferencelocation>Belo Horizonte</conferencelocation>
		<date>Oct. 7-10, 2007</date>
		<booktitle>Proceedings</booktitle>
		<publisher>Sociedade Brasileira de Computação</publisher>
		<publisheraddress>Porto Alegre</publisheraddress>
		<tertiarytype>Technical Poster</tertiarytype>
		<transferableflag>1</transferableflag>
		<contenttype>External Contribution</contenttype>
		<keywords>data visualization, time series.</keywords>
		<abstract>Time series analysis poses many challenges to professionals in a wide range of domains. Several visualization solutions integrated with mining algorithms have been proposed for exploratory tasks on time series collections. As the data sets grow large, though, the visual alternatives do not allow for a good association between similar time series. In this paper we introduce a visual representation or large time series data sets generated by multidimensional projections based on distance measures.</abstract>
		<language>en</language>
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