Name
Abstract | ||
|---|---|---|
It is an accepted fact that a value for a data quality metric can be acceptable or not, depending on the context in which data are produced and consumed. In particular, in a data warehouse (DW), the context for the value of a measure is given by the dimensions, and external data. In this paper we propose the use of logic rules to assess the quality of measures in a DW, accounting for the context in which these measures are considered. For this, we propose the use of three sets of rules: one, for representing the DW; a second one, for defining the particular context for the measures in the warehouse; and a third one for representing data quality metrics. This provides an uniform, elegant, and flexible framework for context-aware DW quality assessment. Our representation is implementation independent, and not only allows us to assess the quality of measures at the lowest granularity level in a data cube, but also the quality of aggregate and dimension data. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-43946-4_20 | Lecture Notes in Computer Science |
Field | DocType | Volume |
Data warehouse,Data mining,Rule-based system,Data quality,Fact table,Computer science,Granularity,Rule of inference,Data cube | Conference | 9829 |
ISSN | Citations | PageRank |
0302-9743 | 2 | 0.41 |
References | Authors | |
9 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Adriana Marotta | 1 | 4 | 1.85 |
| Alejandro A. Vaisman | 2 | 657 | 55.94 |