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  1. A generalized email classification system for workflow analysis
    Autor*in: Chaipornkaew, Piyanuch; Prexawanprasut, Takorn; Chang, Chia-Lin; McAleer, Michael
    Erschienen: [2017]
    Verlag:  [Econometric Institute, Erasmus School of Economics], [Rotterdam]

    Standort:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signatur:
    VS 57 (2017,21)
    Fernleihe:
    keine Fernleihe
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    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Weitere Identifier:
    hdl: 1765/101762
    Auflage/Ausgabe: Revised: July 2017
    Schriftenreihe: [Econometric Institute research papers] ; EI2017, 21
    Schlagworte: E-Mail; Wirtschaftsdaten; Workflow-Management-System
    Umfang: 1 Online-Ressource (circa 20 Seiten), Illustrationen
  2. A generalized Email classification system for workflow analysis
    Autor*in: Chaipornkaew, Piyanuch; Prexawanprasut, Takorn; Chang, Chia-Lin; McAleer, Michael
    Erschienen: 2017
    Verlag:  Tinbergen Institute, Amsterdam

    One of the most powerful internet communication channels is email. As employees and their clients communicate primarily via email, much crucial business data is conveyed via email content. Where businesses are understandably concerned, they need a... mehr

    Standort:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signatur:
    DS 432 (2017,66)
    Fernleihe:
    keine Fernleihe

     

    One of the most powerful internet communication channels is email. As employees and their clients communicate primarily via email, much crucial business data is conveyed via email content. Where businesses are understandably concerned, they need a sophisticated workflow management system to manage their transactions. A workflow management system should also be able to classify any incoming emails into suitable categories. Previous research has implemented a system to categorize emails based on the words found in email messages. Two parameters affected the accuracy of the program, namely the number of words in a database compared with sample emails, and an acceptable percentage for classifying emails. As the volume of email has become larger and more sophisticated, this research classifies email messages into a larger number of categories and changes a parameter that affects the accuracy of the program. The first parameter, namely the number of words in a database compared with sample emails, remains unchanged, while the second parameter is changed from an acceptable percentage to the number of matching words. The empirical results suggest that the number of words in a database compared with sample emails is 11, and the number of matching words to categorize emails is 7. When these settings are applied to categorize 12,465 emails, the accuracy of this experiment is approximately 65.3%. The optimal number of words that yields high accuracy levels lies between 11 and 13, while the number of matching words lies between 6 and 8.

     
    Export in Literaturverwaltung   RIS-Format
      BibTeX-Format
    Hinweise zum Inhalt
    Volltext (kostenfrei)
    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Weitere Identifier:
    hdl: 10419/177634
    Auflage/Ausgabe: This version: revised July 2017
    Schriftenreihe: Array ; TI 2017, 066
    Schlagworte: E-Mail; Wirtschaftsdaten; Workflow-Management-System
    Umfang: 1 Online-Ressource (circa 22 Seiten), Illustrationen