Computer-graphical exploration of large data sets from teletraffic.

Persistent Link:
http://hdl.handle.net/10150/186645
Title:
Computer-graphical exploration of large data sets from teletraffic.
Author:
Rauschenberg, David Edward.
Issue Date:
1994
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
The availability of large data sets and powerful computing resources has made data analysis an increasingly viable approach to understanding random processes. Of particular interest are exploratory techniques which provide insight into the local path behavior of highly positively correlated processes. We focus on actual and simulated teletraffic data in the form of time series. Our foremost objective is to develop a methodology of identifying and classifying shape features which are essentially unrecognizable with standard statistical descriptors. Using basic aspects of human vision as a heuristic guide, we have developed an algorithm which "sketches" data sequences. Our approach to summarizing path behavior is based on exploiting the simple structure of a sketch. We have developed a procedure whereby all the "shapes" of a sketch are summarized in a visually comprehensible manner. We do so by placing the shapes in classes, then displaying, for each class, both a representative shape and the number of shapes in the class. These "shape histograms" can provide substantial insight into the behavior of sample paths. We have also used sketches to help model data sequences. The idea here is that a model based on a sketch of a data sequence may provide a better fit under some circumstances than a model based directly on the data. By considering various sketches, one could, for example, develop a Markov chain model whose autocorrelation function approximates that of the original data. We have generalized this use of sketches so that a data sequence can be modeled as the superposition of several sketches, each capturing a different level of detail. Because the concept of path shape is highly visual, it is important that our techniques exploit the strengths of and accommodate for the weaknesses of human vision. We have addressed this by using computer graphics in a variety of novel ways.
Type:
text; Dissertation-Reproduction (electronic)
Keywords:
Dissertations, Academic.; Statistics.; Operations research.
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Applied Mathematics; Graduate College
Degree Grantor:
University of Arizona
Committee Chair:
Neuts, Marcel F.

Full metadata record

DC FieldValue Language
dc.language.isoenen_US
dc.titleComputer-graphical exploration of large data sets from teletraffic.en_US
dc.creatorRauschenberg, David Edward.en_US
dc.contributor.authorRauschenberg, David Edward.en_US
dc.date.issued1994en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractThe availability of large data sets and powerful computing resources has made data analysis an increasingly viable approach to understanding random processes. Of particular interest are exploratory techniques which provide insight into the local path behavior of highly positively correlated processes. We focus on actual and simulated teletraffic data in the form of time series. Our foremost objective is to develop a methodology of identifying and classifying shape features which are essentially unrecognizable with standard statistical descriptors. Using basic aspects of human vision as a heuristic guide, we have developed an algorithm which "sketches" data sequences. Our approach to summarizing path behavior is based on exploiting the simple structure of a sketch. We have developed a procedure whereby all the "shapes" of a sketch are summarized in a visually comprehensible manner. We do so by placing the shapes in classes, then displaying, for each class, both a representative shape and the number of shapes in the class. These "shape histograms" can provide substantial insight into the behavior of sample paths. We have also used sketches to help model data sequences. The idea here is that a model based on a sketch of a data sequence may provide a better fit under some circumstances than a model based directly on the data. By considering various sketches, one could, for example, develop a Markov chain model whose autocorrelation function approximates that of the original data. We have generalized this use of sketches so that a data sequence can be modeled as the superposition of several sketches, each capturing a different level of detail. Because the concept of path shape is highly visual, it is important that our techniques exploit the strengths of and accommodate for the weaknesses of human vision. We have addressed this by using computer graphics in a variety of novel ways.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.subjectDissertations, Academic.en_US
dc.subjectStatistics.en_US
dc.subjectOperations research.en_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineApplied Mathematicsen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.chairNeuts, Marcel F.en_US
dc.contributor.committeememberMaier, Robert S.en_US
dc.contributor.committeememberEmerson, Scott S.en_US
dc.identifier.proquest9424978en_US
dc.identifier.oclc722473338en_US
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