Zobrazit v EDS

Automatic SQL query generation

Uloženo v:
Podrobná bibliografie
Název: Automatic SQL query generation
Autoři: Arbabifard, Kamyar
Přispěvatelé: Gudivada, Venkat N, Computer Science
Informace o vydavateli: East Carolina University
Rok vydání: 2017
Sbírka: East Carolina University: The ScholarShip at ECU
Témata: Personalized Learning, Question Generation, SQL (Computer program language), MOOCs (Web-based instruction), Educational evaluation
Popis: Automatic generation of questions for learning assessment has been an area of research interest for long. The advent of Massive Open Online Courses (MOOCs) as well as the goal of providing immediate contextualized feedback to enhance student learning has created renewed interest in automatic question generation. This thesis motivates the automatic question generation problem, gives an overview of the current approaches, and describes the proposed novel approach to automatic generation of SQL queries using the notion of grammar graph.
Druh dokumentu: master thesis
Popis souboru: application/pdf
Jazyk: English
Relation: http://hdl.handle.net/10342/6401
Dostupnost: http://hdl.handle.net/10342/6401
Přístupové číslo: edsbas.87EF884C
Databáze: BASE
FullText Text:
  Availability: 0
CustomLinks:
  – Url: http://hdl.handle.net/10342/6401#
    Name: EDS - BASE (s4221598)
    Category: fullText
    Text: View record from BASE
  – Url: https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=EBSCO&SrcAuth=EBSCO&DestApp=WOS&ServiceName=TransferToWoS&DestLinkType=GeneralSearchSummary&Func=Links&author=Arbabifard%20K
    Name: ISI
    Category: fullText
    Text: Nájsť tento článok vo Web of Science
    Icon: https://imagesrvr.epnet.com/ls/20docs.gif
    MouseOverText: Nájsť tento článok vo Web of Science
Header DbId: edsbas
DbLabel: BASE
An: edsbas.87EF884C
RelevancyScore: 717
AccessLevel: 3
PubType: Dissertation/ Thesis
PubTypeId: dissertation
PreciseRelevancyScore: 716.807495117188
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Automatic SQL query generation
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Arbabifard%2C+Kamyar%22">Arbabifard, Kamyar</searchLink>
– Name: Author
  Label: Contributors
  Group: Au
  Data: Gudivada, Venkat N<br />Computer Science
– Name: Publisher
  Label: Publisher Information
  Group: PubInfo
  Data: East Carolina University
– Name: DatePubCY
  Label: Publication Year
  Group: Date
  Data: 2017
– Name: Subset
  Label: Collection
  Group: HoldingsInfo
  Data: East Carolina University: The ScholarShip at ECU
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Personalized+Learning%22">Personalized Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Question+Generation%22">Question Generation</searchLink><br /><searchLink fieldCode="DE" term="%22SQL+%28Computer+program+language%29%22">SQL (Computer program language)</searchLink><br /><searchLink fieldCode="DE" term="%22MOOCs+%28Web-based+instruction%29%22">MOOCs (Web-based instruction)</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+evaluation%22">Educational evaluation</searchLink>
– Name: Abstract
  Label: Description
  Group: Ab
  Data: Automatic generation of questions for learning assessment has been an area of research interest for long. The advent of Massive Open Online Courses (MOOCs) as well as the goal of providing immediate contextualized feedback to enhance student learning has created renewed interest in automatic question generation. This thesis motivates the automatic question generation problem, gives an overview of the current approaches, and describes the proposed novel approach to automatic generation of SQL queries using the notion of grammar graph.
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: master thesis
– Name: Format
  Label: File Description
  Group: SrcInfo
  Data: application/pdf
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: NoteTitleSource
  Label: Relation
  Group: SrcInfo
  Data: http://hdl.handle.net/10342/6401
– Name: URL
  Label: Availability
  Group: URL
  Data: http://hdl.handle.net/10342/6401
– Name: AN
  Label: Accession Number
  Group: ID
  Data: edsbas.87EF884C
PLink https://erproxy.cvtisr.sk/sfx/access?url=https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.87EF884C
RecordInfo BibRecord:
  BibEntity:
    Languages:
      – Text: English
    Subjects:
      – SubjectFull: Personalized Learning
        Type: general
      – SubjectFull: Question Generation
        Type: general
      – SubjectFull: SQL (Computer program language)
        Type: general
      – SubjectFull: MOOCs (Web-based instruction)
        Type: general
      – SubjectFull: Educational evaluation
        Type: general
    Titles:
      – TitleFull: Automatic SQL query generation
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Arbabifard, Kamyar
      – PersonEntity:
          Name:
            NameFull: Gudivada, Venkat N
      – PersonEntity:
          Name:
            NameFull: Computer Science
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2017
          Identifiers:
            – Type: issn-locals
              Value: edsbas
ResultId 1