The Dark Side of Process Mining. How Identifiable Are Users Despite Technologically Anonymized Data? A Case Study from the Health Sector.
Resource URI: https://dblp.l3s.de/d2r/resource/publications/conf/bpm/BadeVKKC22
Home
|
Example Publications
Property
Value
dcterms:
bibliographicCitation
<
http://dblp.uni-trier.de/rec/bibtex/conf/bpm/BadeVKKC22
>
dc:
creator
<
https://dblp.l3s.de/d2r/resource/authors/Andr%E2%88%9A%C2%A9_Coners
>
dc:
creator
<
https://dblp.l3s.de/d2r/resource/authors/Carolin_Vollenberg
>
dc:
creator
<
https://dblp.l3s.de/d2r/resource/authors/Friederike-Maria_Bade
>
dc:
creator
<
https://dblp.l3s.de/d2r/resource/authors/Jannis_Koch
>
dc:
creator
<
https://dblp.l3s.de/d2r/resource/authors/Julian_Koch
>
foaf:
homepage
<
http://dx.doi.org/doi.org%2F10.1007%2F978-3-031-16103-2%5F16
>
foaf:
homepage
<
https://doi.org/10.1007/978-3-031-16103-2_16
>
dc:
identifier
DBLP conf/bpm/BadeVKKC22
(xsd:string)
dc:
identifier
DOI doi.org%2F10.1007%2F978-3-031-16103-2%5F16
(xsd:string)
dcterms:
issued
2022
(xsd:gYear)
rdfs:
label
The Dark Side of Process Mining. How Identifiable Are Users Despite Technologically Anonymized Data? A Case Study from the Health Sector.
(xsd:string)
foaf:
maker
<
https://dblp.l3s.de/d2r/resource/authors/Andr%E2%88%9A%C2%A9_Coners
>
foaf:
maker
<
https://dblp.l3s.de/d2r/resource/authors/Carolin_Vollenberg
>
foaf:
maker
<
https://dblp.l3s.de/d2r/resource/authors/Friederike-Maria_Bade
>
foaf:
maker
<
https://dblp.l3s.de/d2r/resource/authors/Jannis_Koch
>
foaf:
maker
<
https://dblp.l3s.de/d2r/resource/authors/Julian_Koch
>
swrc:
pages
219-233
(xsd:string)
dcterms:
partOf
<
https://dblp.l3s.de/d2r/resource/publications/conf/bpm/2022
>
owl:
sameAs
<
http://bibsonomy.org/uri/bibtexkey/conf/bpm/BadeVKKC22/dblp
>
owl:
sameAs
<
http://dblp.rkbexplorer.com/id/conf/bpm/BadeVKKC22
>
rdfs:
seeAlso
<
http://dblp.uni-trier.de/db/conf/bpm/bpm2022.html#BadeVKKC22
>
rdfs:
seeAlso
<
https://doi.org/10.1007/978-3-031-16103-2_16
>
swrc:
series
<
https://dblp.l3s.de/d2r/resource/conferences/bpm
>
dc:
title
The Dark Side of Process Mining. How Identifiable Are Users Despite Technologically Anonymized Data? A Case Study from the Health Sector.
(xsd:string)
dc:
type
<
http://purl.org/dc/dcmitype/Text
>
rdf:
type
swrc:InProceedings
rdf:
type
foaf:Document