Detecting Anomalies in Migrated Data for Enterprise Resource Planning Implementation
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URL
Journal Title
Journal ISSN
Volume Title
School of Business |
Master's thesis
Authors
Date
2019
Department
Major/Subject
Mcode
Degree programme
Information and Service Management (ISM)
Language
en
Pages
89+3
Series
Description
Thesis advisor
Malo, PekkaKuosmanen, Timo
Keywords
ERP system, ERP implementation, data mining, anomaly detection, data migration, data quality