Abstract
Key indicators, such as key performance indicators or key compliance indicators are at the heart of modern business intelligence applications. Key indicators are metrics, i.e., numbers, that help an organization to measure and assess how successful it is in reaching predefined goals (e.g., lowering process execution times or increasing compliance with regulations), and typically the people looking at them simply trust the values they see when taking decisions. However, it is important to recognize that in real business environments we cannot always rely on fully trusted or certain data, yet indicators are to be computed. In this paper, we tackle the problem of computing uncertain indicators from uncertain data, we characterize the problem in a modern business scenario (combining techniques from uncertain and probabilistic data management), and we describe how we addressed and implemented the problem in a European research project.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 2009 International Conference on Information Quality, ICIQ 2009 |
| Publication status | Published - 2009 |
| Externally published | Yes |
| Event | 14th International Conference on Information Quality, ICIQ 2009 - Potsdam, Germany Duration: 7 Nov 2009 → 8 Nov 2009 |
Conference
| Conference | 14th International Conference on Information Quality, ICIQ 2009 |
|---|---|
| Country | Germany |
| City | Potsdam |
| Period | 7.11.09 → 8.11.09 |
Fingerprint
Keywords
- Business Process Intelligence
- Data Warehousing
- Key Indicators
- Probabilistic Indicators
- Uncertain Indicators
- Uncertain/Probabilistic Data
ASJC Scopus subject areas
- Information Systems
- Safety, Risk, Reliability and Quality
Cite this
Computing uncertain key indicators from uncertain data. / Rodríguez, Carlos; Daniel, Florian; Casati, Fabio; Cappiello, Cinzia.
Proceedings of the 2009 International Conference on Information Quality, ICIQ 2009. 2009.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Computing uncertain key indicators from uncertain data
AU - Rodríguez, Carlos
AU - Daniel, Florian
AU - Casati, Fabio
AU - Cappiello, Cinzia
PY - 2009
Y1 - 2009
N2 - Key indicators, such as key performance indicators or key compliance indicators are at the heart of modern business intelligence applications. Key indicators are metrics, i.e., numbers, that help an organization to measure and assess how successful it is in reaching predefined goals (e.g., lowering process execution times or increasing compliance with regulations), and typically the people looking at them simply trust the values they see when taking decisions. However, it is important to recognize that in real business environments we cannot always rely on fully trusted or certain data, yet indicators are to be computed. In this paper, we tackle the problem of computing uncertain indicators from uncertain data, we characterize the problem in a modern business scenario (combining techniques from uncertain and probabilistic data management), and we describe how we addressed and implemented the problem in a European research project.
AB - Key indicators, such as key performance indicators or key compliance indicators are at the heart of modern business intelligence applications. Key indicators are metrics, i.e., numbers, that help an organization to measure and assess how successful it is in reaching predefined goals (e.g., lowering process execution times or increasing compliance with regulations), and typically the people looking at them simply trust the values they see when taking decisions. However, it is important to recognize that in real business environments we cannot always rely on fully trusted or certain data, yet indicators are to be computed. In this paper, we tackle the problem of computing uncertain indicators from uncertain data, we characterize the problem in a modern business scenario (combining techniques from uncertain and probabilistic data management), and we describe how we addressed and implemented the problem in a European research project.
KW - Business Process Intelligence
KW - Data Warehousing
KW - Key Indicators
KW - Probabilistic Indicators
KW - Uncertain Indicators
KW - Uncertain/Probabilistic Data
UR - http://www.scopus.com/inward/record.url?scp=84871565575&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84871565575&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84871565575
BT - Proceedings of the 2009 International Conference on Information Quality, ICIQ 2009
ER -