RUcore: Rutgers University Community Repository
Art ticker
Descriptive
TitleInfo
Title
Art ticker
SubTitle
discovering emerging artists on the web
Name
(type = personal)
NamePart
(type = family)
Patel
NamePart
(type = given)
Saad
DisplayForm
Saad Patel
Role
RoleTerm
(authority = RULIB)
author
Name
(type = personal)
NamePart
(type = family)
Imielinski
NamePart
(type = given)
Tomasz
DisplayForm
Tomasz Imielinski
Affiliation
Advisory Committee
Role
RoleTerm
(authority = RULIB)
chair
Name
(type = personal)
NamePart
(type = family)
Elgammal
NamePart
(type = given)
Ahmed
DisplayForm
Ahmed Elgammal
Affiliation
Advisory Committee
Role
RoleTerm
(authority = RULIB)
co-chair
Name
(type = personal)
NamePart
(type = family)
Borgida
NamePart
(type = given)
Alex
DisplayForm
Alex Borgida
Affiliation
Advisory Committee
Role
RoleTerm
(authority = RULIB)
co-chair
Name
(type = corporate)
NamePart
Rutgers University
Role
RoleTerm
(authority = RULIB)
degree grantor
Name
(type = corporate)
NamePart
Graduate School - New Brunswick
Role
RoleTerm
(authority = RULIB)
school
TypeOfResource
Text
Genre
(authority = marcgt)
theses
OriginInfo
DateCreated
(encoding = w3cdtf);
(qualifier = exact)
2016
DateOther
(qualifier = exact);
(type = degree)
2016-01
CopyrightDate
(encoding = w3cdtf);
(qualifier = exact)
2016
Place
PlaceTerm
(type = code)
xx
Language
LanguageTerm
(authority = ISO639-2b);
(type = code)
eng
Abstract
(type = abstract)
Considering the large number of artists that exist, there is valuable talent to be discovered. But the question arises, how to find promising and emerging artists from hundreds of thousands of names listed among many different aggregations websites such as artfacts.org, and thousands of art galleries? We introduce an application named ArtTicker which uses many features of Machine Learning, Information Retrieval, Data Mining and Text Mining to crawl, rank, and analyze artists and their popularity on the web. We start by identifying names of artists who are not yet listed in large aggregate directories (such as artfacts) but are already represented by some galleries. This task requires crawling and extraction of artist names from thousands of art galleries. These web sites share a lot of common structures, however there is also significant variety among them and artist name extraction requires complex heuristics. We harvest thousands of artist names this way. Then we enter the second phase of the project – ranking this artists by their “web presence”. Since the wealth of any data mining model is the actual data, the data collection period consisted of extensive crawling from a vast number of news publication websites. To this end we gather and cluster news from several leading art related news websites and also use many signals to rank and classify these art news sources. The artists’s score is based on how significantly an individual artist was featured in the art news stream of articles. The final objective of finding the emerging artists is met by identifying the names which are present on gallery web sites, have high media presence (high score) and are not listed yet on the artist aggregate sites. The working prototype analyzes over 150 sources in English language but can be easily extended based on automatically crawling and analyzing related sources. It currently holds over 250,000 artists and over 70,000 articles from all these news sources. In essence, this is a streaming application for which given any geographic area (say Lower Manhattan) identifies the “hottest” artists who are not yet known.
Subject
(authority = RUETD)
Topic
Computer Science
RelatedItem
(type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier
(type = RULIB)
ETD
Identifier
ETD_7023
PhysicalDescription
Form
(authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
1 online resource (xi, 64 p. : ill.)
Note
(type = degree)
M.S.
Note
(type = bibliography)
Includes bibliographical references
Subject
(authority = ETD-LCSH)
Topic
Artists
Subject
(authority = ETD-LCSH)
Topic
Data mining
Note
(type = statement of responsibility)
by Saad Patel
RelatedItem
(type = host)
TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier
(type = local)
rucore19991600001
Location
PhysicalLocation
(authority = marcorg);
(displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Identifier
(type = doi)
doi:10.7282/T3D79DGW
Genre
(authority = ExL-Esploro)
ETD graduate
Back to the top
Rights
RightsDeclaration
(ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder
(type = personal)
Name
FamilyName
Patel
GivenName
Saad
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime
(encoding = w3cdtf);
(qualifier = exact);
(point = start)
2016-01-14 11:34:49
AssociatedEntity
Name
Saad Patel
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.
RightsEvent
DateTime
(encoding = w3cdtf);
(qualifier = exact);
(point = start)
2016-01-31
DateTime
(encoding = w3cdtf);
(qualifier = exact);
(point = end)
2018-01-30
Type
Embargo
Detail
Access to this PDF has been restricted at the author's request. It will be publicly available after January 30th, 2018.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
Back to the top
Technical
RULTechMD
(ID = TECHNICAL1)
ContentModel
ETD
OperatingSystem
(VERSION = 5.1)
windows xp
Back to the top
Statistical Profile