Staff View
Searching heterogeneous personal data

Descriptive

TitleInfo
Title
Searching heterogeneous personal data
Name (type = personal)
NamePart (type = family)
Vianna
NamePart (type = given)
Daniela Quitete de Campos
NamePart (type = date)
1979-
DisplayForm
Daniela Quitete de Campos Vianna
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
NamePart (type = family)
Marian
NamePart (type = given)
Amélie
DisplayForm
Amélie Marian
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
chair
Name (type = personal)
NamePart (type = family)
Nguyen
NamePart (type = given)
Thu
DisplayForm
Thu Nguyen
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Zhang
NamePart (type = given)
Yongfeng
DisplayForm
Yongfeng Zhang
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Srivastava
NamePart (type = given)
Divesh
DisplayForm
Divesh Srivastava
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
outside member
Name (type = corporate)
NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (type = corporate)
NamePart
School of Graduate Studies
Role
RoleTerm (authority = RULIB)
school
TypeOfResource
Text
Genre (authority = marcgt)
theses
OriginInfo
DateCreated (encoding = w3cdtf); (keyDate = yes); (qualifier = exact)
2019
DateOther (encoding = w3cdtf); (qualifier = exact); (type = degree)
2019-10
CopyrightDate (encoding = w3cdtf); (qualifier = exact)
2019
Language
LanguageTerm (authority = ISO 639-3:2007); (type = text)
English
Abstract (type = abstract)
Personal data is now pervasive, as digital devices are capturing every part of our lives. Users are constantly collecting and saving more data, either actively in files, emails, social media interactions, etc., or passively by GPS tracking of mobile devices, or records of financial transactions. Unlike traditional information seeking, which focuses on discovering new information, search on personal data is usually focused on retrieving information that users know exists in their own dataset, even though most of the time they do not have a perfect recollection of where it is stored. Attempting to retrieve and cross-reference personal information leads to a tedious process of individually accessing all the relevant sources of data and manually linking their information. In this scenario, traditional searches are often inefficient, making it critical for search tools to be capable of accessing heterogeneous and decentralized data in a flexible and accurate way by taking into consideration the additional knowledge the user is likely to have about the target information.

In this dissertation, we introduce a set of techniques that allow users to easily access their own data. We start by presenting a unified and intuitive multidimensional data model following a combination of dimensions that naturally summarize various aspects of the data collection: who, when, where, what, why, how. We then proceed by designing frequency-based scoring models that leverage the correlation between users (who), time (when), location (where), data topics (what), and provenance (how) to improve search over personal data. Since the scoring model proposed needs to generalize well over user-specific datasets, we extend the static scoring function by adopting a learning-to-rank approach using the state of the art LambdaMART algorithm. Due to the lack of pre-existing personal training data, a combination of known-item query generation techniques and an unsupervised ranking model (field-based BM25) is used to build our own training sets.

To validate the data and scoring models, we implemented tools for data extraction, classification, entity recognition, and topic modeling. A thorough qualitative evaluation performed over a publicly available email collection and a personal digital data trace collection from a real user show that our approach significantly improves search accuracy when compared with traditional personal search tools such as Apple's Spotlight and Apache Solr, and techniques like TF-IDF, BM25, and field-based BM25.
Subject (authority = RUETD)
Topic
Computer Science
Subject (authority = local)
Topic
Personal data
Subject (authority = LCSH)
Topic
Database searching
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_10375
PhysicalDescription
Form (authority = gmd)
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
1 online resource (xiii, 86 pages) : illustrations
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
RelatedItem (type = host)
TitleInfo
Title
School of Graduate Studies Electronic Theses and Dissertations
Identifier (type = local)
rucore10001600001
Location
PhysicalLocation (authority = marcorg); (displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Identifier (type = doi)
doi:10.7282/t3-vrst-v776
Genre (authority = ExL-Esploro)
ETD doctoral
Back to the top

Rights

RightsDeclaration (ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Vianna
GivenName
Daniela
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2019-09-30 10:34:54
AssociatedEntity
Name
Daniela Vianna
Role
Copyright holder
Affiliation
Rutgers University. School of Graduate Studies
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
Back to the top

Technical

RULTechMD (ID = TECHNICAL1)
ContentModel
ETD
OperatingSystem (VERSION = 5.1)
windows xp
CreatingApplication
Version
1.5
ApplicationName
pdfTeX-1.40.17
DateCreated (point = end); (encoding = w3cdtf); (qualifier = exact)
2019-09-30T07:05:09
DateCreated (point = end); (encoding = w3cdtf); (qualifier = exact)
2019-09-30T07:05:09
Back to the top
Version 8.3.13
Rutgers University Libraries - Copyright ©2021