Show simple item record

dc.contributor.authorRibler, Randy L.en_US
dc.date.accessioned2014-03-14T20:21:22Z
dc.date.available2014-03-14T20:21:22Z
dc.date.issued1997-04-04en_US
dc.identifier.otheretd-1711111139751001en_US
dc.identifier.urihttp://hdl.handle.net/10919/30314
dc.description.abstractVisualization tools allow scientists to comprehend very large data sets and to discover relationships which are otherwise difficult to detect. Unfortunately, not all types of data can be visualized easily using existing tools. In particular, long sequences of nonnumeric data cannot be visualized adequately. Examples of this type of data include trace files of computer performance information, the nucleotides in a genetic sequence, a record of stocks traded over a period of years, and the sequence of words in this document. The term categorical time series is defined and used to describe this family of data. When visualizations designed for numerical time series are applied to categorical time series, the distortions which result from the arbitrary conversion of unordered categorical values to totally ordered numerical values can be profound. Examples of this phenomenon are presented and explained. Several new, general purpose techniques for visualizing categorical time series data have been developed as part of this work and have been incorporated into the Chitra performance analysis and visualization system. All of these new visualizations can be produced in O(n) time. The new visualizations for categorical time series provide general purpose techniques for visualizing aspects of categorical data which are commonly of interest. These include periodicity, stationarity, cross-correlation, autocorrelation, and the detection of recurring patterns. The effective use of these visualizations is demonstrated in a number of application domains, including performance analysis, World Wide Web traffic analysis, network routing simulations, document comparison, pattern detection, and the analysis of the performance of genetic algorithms.en_US
dc.publisherVirginia Techen_US
dc.relation.haspartetd.pdfen_US
dc.rightsI hereby grant to Virginia Tech or its agents the right to archive and to make available my thesis or dissertation in whole or in part in the University Libraries in all forms of media, now or hereafter known. I retain all proprietary rights, such as patent rights. I also retain the right to use in future works (such as articles or books) all or part of this thesis or dissertation.en_US
dc.subjectvisualizationen_US
dc.subjectcategorical dataen_US
dc.subjecttime seriesen_US
dc.subjectdata miningen_US
dc.subjectperformance analysisen_US
dc.subjectinformation visualizationen_US
dc.titleVisualizing Categorical Time Series Data with Applications to Computer and Communications Network Tracesen_US
dc.typeDissertationen_US
dc.contributor.departmentComputer Scienceen_US
dc.description.degreePh. D.en_US
thesis.degree.namePh. D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineComputer Scienceen_US
dc.contributor.committeechairAbrams, Marcen_US
dc.contributor.committeememberKriz, Ronald D.en_US
dc.contributor.committeememberEhrich, Roger W.en_US
dc.contributor.committeememberFoutz, Roberten_US
dc.contributor.committeememberRibbens, Calvin J.en_US
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-1711111139751001/en_US
dc.date.sdate1998-07-21en_US
dc.date.rdate1997-04-04
dc.date.adate1997-04-04en_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record