• xmlui.mirage2.page-structure.header.title
    • français
    • English
  • Help
  • Login
  • Language 
    • Français
    • English
View Item 
  •   BIRD Home
  • LAMSADE (UMR CNRS 7243)
  • LAMSADE : Publications
  • View Item
  •   BIRD Home
  • LAMSADE (UMR CNRS 7243)
  • LAMSADE : Publications
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Browse

BIRDResearch centres & CollectionsBy Issue DateAuthorsTitlesTypeThis CollectionBy Issue DateAuthorsTitlesType

My Account

LoginRegister

Statistics

Most Popular ItemsStatistics by CountryMost Popular Authors
Thumbnail - Request a copy

Mining Business Process Activities from Email Logs

Jlailaty, Diana; Grigori, Daniela; Belhajjame, Khalid (2017), Mining Business Process Activities from Email Logs, 2017 IEEE International Conference on Cognitive Computing (ICCC), IEEE - Institute of Electrical and Electronics Engineers : Piscataway, NJ, p. 112-119. 10.1109/IEEE.ICCC.2017.28

Type
Communication / Conférence
Date
2017
Conference date
2017
Book title
2017 IEEE International Conference on Cognitive Computing (ICCC)
Publisher
IEEE - Institute of Electrical and Electronics Engineers
Published in
Piscataway, NJ
ISBN
978-1-5386-2007-6
Pages
112-119
Publication identifier
10.1109/IEEE.ICCC.2017.28
Metadata
Show full item record
Author(s)
Jlailaty, Diana

Grigori, Daniela

Belhajjame, Khalid
Abstract (EN)
Due to its wide use in personal, but most importantly, professional contexts, email represents a valuable source of information that can be harvested for understanding, reengineering and repurposing undocumented business processes of companies and institutions. Few researchers have investigated the problem of extracting and analyzing the process-oriented information contained in emails. In this paper, we go forward in this direction by proposing a new method to discover business process activities from email logs. Towards this aim, emails are grouped according to the process model they belong to. This is followed by sub-grouping and labeling the emails of each process model into business activity types. These tasks are applied by deploying an unsupervised mining technique accompanied by semantic similarity measurement methods. Two representative similarity measurement methods are examined: Latent Semantic Indexing (LSA) and Word2vec. These methods are compared to prove that Word2vec provides a better performance than LSA in grouping emails according to what process model they are related to, and in discovering emails belonging to the same activity type. Experimental results are detailed to illustrate and prove our approach contributions.
Subjects / Keywords
Email analysis; Word2vec; LSA; process mining; process modeling

Related items

Showing items related by title and author.

  • Thumbnail
    A framework for mining process models from emails logs 
    Jlailaty, Diana; Grigori, Daniela; Belhajjame, Khalid (2016) Document de travail / Working paper
  • Thumbnail
    Business Process Instances Discovery from Email Logs 
    Jlailaty, Diana; Grigori, Daniela; Belhajjame, Khalid (2017) Communication / Conférence
  • Thumbnail
    Multi-level clustering for extracting process-related information from email logs 
    Jlailaty, Diana; Grigori, Daniela; Belhajjame, Khalid (2017) Communication / Conférence
  • Thumbnail
    Email Business Activities Extraction and Annotation 
    Jlailaty, Diana; Grigori, Daniela; Belhajjame, Khalid (2018) Communication / Conférence
  • Thumbnail
    On the elicitation and annotation of business activities based on emails 
    Al Jlailaty, Diana; Grigori, Daniela; Belhajjame, Khalid (2019) Communication / Conférence
Dauphine PSL Bibliothèque logo
Place du Maréchal de Lattre de Tassigny 75775 Paris Cedex 16
Phone: 01 44 05 40 94
Contact
Dauphine PSL logoEQUIS logoCreative Commons logo