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Scalable data analytics for ensemble learning
Descriptive
TitleInfo
Title
Scalable data analytics for ensemble learning
Name
(type = personal)
NamePart
(type = family)
Aithal
NamePart
(type = given)
Suhas R
NamePart
(type = date)
1988-
DisplayForm
Suhas Aithal
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author
Name
(type = personal)
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(type = family)
Parashar
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Manish
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Manish Parashar
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Advisory Committee
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chair
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Pompili
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Dario
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Dario Pompili
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Advisory Committee
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internal member
Name
(type = personal)
NamePart
(type = family)
Gajic
NamePart
(type = given)
Zoran
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Zoran Gajic
Affiliation
Advisory Committee
Role
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(authority = RULIB)
internal member
Name
(type = personal)
NamePart
(type = family)
Rodero
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(type = given)
Ivan
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Ivan Rodero
Affiliation
Advisory Committee
Role
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(authority = RULIB)
outside member
Name
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NamePart
Rutgers University
Role
RoleTerm
(authority = RULIB)
degree grantor
Name
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NamePart
Graduate School - New Brunswick
Role
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(authority = RULIB)
school
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Text
Genre
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theses
OriginInfo
DateCreated
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2013
DateOther
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(type = degree)
2013-05
Place
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(type = code)
xx
Language
LanguageTerm
(authority = ISO639-2b);
(type = code)
eng
Abstract
(type = abstract)
Data Anlaytic techniques have enhanced human ability to solve a lot of data related problems. It has opened a window through which an analyst can devise techniques to solve a given problem by just looking at the related data alone. Such techniques may not be directly visible to the programmer. Machine learning in short is programming computers to generate such algorithm to solve a given problem using example data or past experience. Numerous models or functions can be developed which fits a distribution of data points. The magnitude and breadth of this data plays a major role in determining which model fits best. In the current world, data is growing at an alarming rate both in terms of size and the information it conveys. Analytics on large-scale data requires very high execution time with limited resources. Implementing such techniques to generate different models on a single machine becomes tedious and time consuming. Implementing the same on a distributed network is a possible solution which is highly interesting and challenging. Data Analytic Algorithms, to create these models, should be carefully chosen on the basis of reliability and data integrity and at the same time should be easily distributable. The range of algorithms should be wide enough to incorporate the same analytics on all the data with required performance. An analysis of various metrics such as measure of scalability, accuracy, execution time helps classify individual algorithm’s impact on the required performance. The above metrics are highly dependent on the data, type of algorithm, and type of platform under consideration. This work proposes a unique system for Scalable Data Analytics and gives an analysis of its scalability. It also shows how this system implements a unique way of ensemble learning in useful time without compromising on accuracy. Finally it demonstrates scalability on two levels 1. Scalability at an individual algorithm level 2. Scalability at implementing ensemble learning for different use cases.
Subject
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Topic
Electrical and Computer Engineering
RelatedItem
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TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier
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ETD
Identifier
ETD_4721
PhysicalDescription
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electronic resource
InternetMediaType
application/pdf
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text/xml
Extent
viii, 58 p. : ill.
Note
(type = degree)
M.S.
Note
(type = bibliography)
Includes bibliographical references
Note
(type = vita)
Includes vita
Note
(type = statement of responsibility)
by Suhas R Aithal
Subject
(authority = ETD-LCSH)
Topic
Machine learning
Subject
(authority = ETD-LCSH)
Topic
Computer science--Mathematics
Identifier
(type = hdl)
http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000068808
RelatedItem
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TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
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rucore19991600001
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(displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Identifier
(type = doi)
doi:10.7282/T38C9TVX
Genre
(authority = ExL-Esploro)
ETD graduate
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Rights
RightsDeclaration
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The author owns the copyright to this work.
RightsHolder
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Name
FamilyName
Aithal
GivenName
Suhas
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime
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(qualifier = exact);
(point = start)
2013-04-16 20:37:09
AssociatedEntity
Name
Suhas Aithal
Role
Copyright holder
Affiliation
Rutgers University. Graduate School - New Brunswick
AssociatedObject
Type
License
Name
Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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Technical
RULTechMD
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ContentModel
ETD
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windows xp
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Statistical Profile