Name
Abstract | ||
|---|---|---|
Data quality can be relevant to many applications. Especially applications coping with sensor data cannot take a single sensor value for granted. Because of technical and physical restrictions each sensor reading is associated with an uncertainty. To improve quality, an application can combine data values from different sensors or, more generally, data providers. But as different data providers may have diverse opinions about a certain real world phenomenon, another issue arises: inconsistency. When handling data from different data providers, the application needs to consider their trustworthiness. This naturally introduces a third aspect of quality: trust. In this paper we propose a novel processing model integrating the three aspects of quality: uncertainty, inconsistency and trust. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-04559-2_12 | QuaCon |
Keywords | Field | DocType |
different sensor,single sensor value,different data provider,data provider,context data,abstract processing model,certain real world phenomenon,sensor reading,data value,data quality,sensor data,diverse opinion,process model | Data science,Data quality,Trustworthiness,Computer science,Coping (psychology),Uncertain data,Relational algebra,Phenomenon,Probability density function | Conference |
Volume | ISSN | ISBN |
5786 | 0302-9743 | 3-642-04558-8 |
Citations | PageRank | References |
3 | 0.43 | 15 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Matthias Grossmann | 1 | 181 | 15.01 |
| Nicola Hönle | 2 | 45 | 6.66 |
| Carlos Lübbe | 3 | 3 | 1.78 |
| Harald Weinschrott | 4 | 79 | 5.23 |