Name
Abstract | ||
|---|---|---|
Errors in business processes result in poor data accuracy. This article proposes an architecture analysis method which utilises ArchiMate and the Probabilistic Relational Model formalism to model and analyse data accuracy. Since the resources available for architecture analysis are usually quite scarce, the method advocates interviews as the primary data collection technique. A case study demonstrates that the method yields correct data accuracy estimates and is more resource-efficient than a competing sampling-based data accuracy estimation method. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1080/17517575.2010.507878 | Enterprise IS |
Keywords | Field | DocType |
poor data accuracy,accuracy estimate,data accuracy assessment,architecture analysis,analyse data accuracy,method advocates interview,architecture analysis method,sampling-based data accuracy estimation,method yields correct data,probabilistic relational model formalism,primary data collection technique,enterprise architecture,computer science,accuracy,data collection,data quality,business process | Data collection,Data mining,ArchiMate,Enterprise architecture,Data quality,Computer science,Logical data model,Sampling (statistics),Data model,Probabilistic database | Journal |
Volume | Issue | ISSN |
5 | 1 | 1751-7575 |
Citations | PageRank | References |
21 | 0.72 | 12 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Per Närman | 1 | 300 | 16.08 |
| Hannes Holm | 2 | 191 | 14.59 |
| Pontus Johnson | 3 | 788 | 55.88 |
| Johan König | 4 | 84 | 5.37 |
| Moustafa Chenine | 5 | 50 | 3.68 |
| Mathias Ekstedt | 6 | 634 | 49.70 |