Towards process patterns for processing data having various qualities

Agung Wahyudi*, Marijn Janssen

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

2 Citations (Scopus)

Abstract

Organizations become more data-intensive and companies try to reap the benefits from this. Although there is a large amount of data available, this data has often different qualities which hinders use. Creating value from big data requires dealing with the variations in quality. Depending on their quality, data need to be processed in various ways to prepare this data for use. Although the processes vary, dealing with certain levels of data quality is a recurring challenge for many organizations. By developing generic process patterns organizations can reuse each other solutions. In this paper, process patterns for dealing with various levels of data quality are derived based on a case study of a large telecom company that employs all kinds of data to create operational value. The process patterns can possibly be used by other organizations.

Original languageEnglish
Title of host publicationProceedings of the 15th IFIP WG 6.11 Conference on e-Business, e-Services, and e-Society, I3E 2016
Subtitle of host publicationSocial Media: The Good, the Bad, and the Ugly
PublisherSpringer
Pages493-504
Number of pages12
Volume9844
ISBN (Print)9783319452333
DOIs
Publication statusPublished - 2016
Event15th IFIP WG 6.11 Conference on e-Business, e-Services, and e-Society, I3E 2016: Social Media: The Good, the Bad, and the Ugly - Swansea, United Kingdom
Duration: 13 Sept 201615 Sept 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9844
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

Conference15th IFIP WG 6.11 Conference on e-Business, e-Services, and e-Society, I3E 2016
Country/TerritoryUnited Kingdom
CitySwansea
Period13/09/1615/09/16

Keywords

  • Big data
  • Data processing
  • Data quality
  • Process patterns
  • Telecom

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