rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: PerCon
    sub title: a Personal Digital Library for Heterogeneous Data Management and Analysis
  name:
    name part: Park, Su Inn
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Shipman, Frank M.
    role:
      role term: thesis advisor.
  name:
    name part: Texas A & M University
    role:
      role term: degree granting institution.
  type of resource: text
  genre: bibliography
  origin info:
    place:
      place term: txu
    date issued: 2015
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource.
  abstract: Systems are needed to support access to and analysis of larger and more heterogeneous scientific datasets. Users need support in the location, organization, analysis, and interpretation of data to support their current activities with appropriate services and tools. We developed PerCon, a data management and analysis environment, to support such use. PerCon processes and integrates data gathered via queries to existing data providers to create a personal or a small group digital library of data. Users may then search, browse, visualize, annotate, and organize the data as they proceed with analysis and interpretation. Analysis and interpretation in PerCon takes place in a visual workspace in which multiple data visualizations and annotations are placed into spatial arrangements based on the current task. The system watches for patterns in the user's data selection, exploration, and organization, then through mixed-initiative interaction assists users by suggesting potentially relevant data from unexplored data sources. In order to identify relevant data, PerCon builds up various precomputed feature tables of data objects including their metadata (e.g. similarities, distances) and a user interest model to infer the user interest or specific information need. In particular, probabilistic networks in PerCon model user interactions (i.e. event features) and predict the data type of greatest interest through network training. In turn, the most relevant data objects of interest in the inferred data type are identified through a weighted feature computation then recommended to the user. PerCon's data location and analysis capabilities were evaluated in a controlled study with 24 users. The study participants were asked to locate and analyze heterogeneous weather and river data with and without the visual workspace and mixed-initiative interaction, respectively. Results indicate that the visual workspace facilitated information representation and aided in the identification …
  note: by Su Inn Park.
  note: "Major Subject: Computer Science"
  note: Includes vita.
  note: Doctor of Philosophy Texas A & M University 2015 http://hdl.handle.net/1969.1/155033
  note: Includes bibliographical references.
  note: Text (Dissertation)
  subject:
    topic: Major Computer Science
  subject:
    topic: Heterogeneous data
  subject:
    topic: data management
  subject:
    topic: data analysis
  subject:
    topic: software infrastructure
  subject:
    topic: human-computer interaction
  subject:
    topic: digital library
  subject:
    topic: visual workspace
  subject:
    topic: recommendation
  subject:
    topic: recommender system
  subject:
    topic: mixed-initiative interaction
  identifier: http://hdl.handle.net/1969.1/155033
  record info:
    record content source: TXA
    record creation date: 160301
    record change date: 20170928112040.0
    record identifier: in00003604554
    language of cataloging:
      language term: eng