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A cluster tracking algorithm for distributed data analytics

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TitleInfo
Title
A cluster tracking algorithm for distributed data analytics
Name (type = personal)
NamePart (type = family)
Lasluisa
NamePart (type = given)
Raul
DisplayForm
Raul Lasluisa
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
NamePart (type = family)
Parashar
NamePart (type = given)
Manish
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Manish Parashar
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Advisory Committee
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chair
Name (type = personal)
NamePart (type = family)
Pompili
NamePart (type = given)
Dario
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Dario Pompili
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Advisory Committee
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internal member
Name (type = personal)
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Jha
NamePart (type = given)
Shantenu
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Shantenu Jha
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Advisory Committee
Role
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internal member
Name (type = corporate)
NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (type = corporate)
NamePart
Graduate School - New Brunswick
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school
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Text
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theses
OriginInfo
DateCreated (qualifier = exact)
2012
DateOther (qualifier = exact); (type = degree)
2012-05
CopyrightDate (qualifier = exact)
2012
Place
PlaceTerm (type = code)
xx
Language
LanguageTerm (authority = ISO639-2b); (type = code)
eng
Abstract (type = abstract)
Large-scale data analytics has enabled society to model, and inspect their data to the point where useful information can be extracted, conclusions can be drawn and decision making can be enhanced.The breadth of data being analyzed today has enabled us to make proactive decision in processes we otherwise could not. At the same time the data being analyzed is both becoming larger and more distributed, making it more complex to aggregate the data to a central location and process in a timely manner in order to make decisions.This can be attributed to the scale of current distributed computational infrastructures used to solve complex problems, while generating an increasing amount of data.This data is being created not only from applications solving problems but also from the systems running the applications as well. Creating a situation where centralized data analytics benefits decline as appose to decentralized approaches. Data analytics algorithms must therefore meet several new requirements in order to continue to process data in a timely manner. One approach to process distributed data is to use algorithms that themselves can run in a distributed manner. Using such algorithms benefit a variety of situations where there is a desire to reduce the cost of transporting and subsequently storing data. Examples can be seen in autonomic computing, where the goal is to manage large system with minimal intervention by administrators and scientific visualization where visualization techniques are performed using a secondary system. In this work we show that combining online (and distributed) data clustering, and cluster tracking can be effectively used to detect meaningful changes in data patterns occurring in the multiple streams. In doing so, we provide an alternative to a centralized approach where data must be centralized before any analytics may be executed. Specifically, we propose an cluster tracking algorithm which takes advantage of a decentralized clustering algorithm in order to detect changes in data to then take proactive decisions. We demonstrate its accuracy and effectiveness in three different case: 1) VM provisioning 2) scheduling of Hadoop resources, and 3) object tracking in scientific applications.
Subject (authority = RUETD)
Topic
Electrical and Computer Engineering
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TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_3990
PhysicalDescription
Form (authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
vi, 48 p. : ill.
Note (type = degree)
M.S.
Note (type = bibliography)
Includes bibliographical references
Note (type = statement of responsibility)
by Raul S Lasluisa
Subject (authority = ETD-LCSH)
Topic
Autonomic computing
Subject (authority = ETD-LCSH)
Topic
Computer algorithms
Identifier (type = hdl)
http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000065189
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TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier (type = local)
rucore19991600001
Location
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NjNbRU
Identifier (type = doi)
doi:10.7282/T3445KD1
Genre (authority = ExL-Esploro)
ETD graduate
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Rights

RightsDeclaration (ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Lasluisa
GivenName
Raul
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2012-04-16 13:43:52
AssociatedEntity
Name
Raul Lasluisa
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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