Name
Abstract | ||
|---|---|---|
There are many reasons to maintain high quality data in databases and other structured data sources. High quality data ensures better discovery, automated data analysis, data mining, migration and re-use. However, due to human errors or faults in data systems themselves data can become corrupted. In this paper existing data quality problem taxonomies for structured textual data and several improvements are analysed. A new classification of data quality problems and a framework for detecting data errors both with and without data operator assistance is proposed. |
Year | Venue | Field |
|---|---|---|
2015 | ICEIS (3-1) | Data warehouse,Data mining,Data stream mining,Data cleansing,Data quality,Data analysis,Information retrieval,Data retrieval,Computer science,Data pre-processing,Data model |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
4 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Arturs Zogla | 1 | 0 | 1.01 |
| Inga Meirane | 2 | 0 | 0.68 |
| Edgars Salna | 3 | 0 | 0.68 |