Computing uncertain key indicators from uncertain data

Carlos Rodríguez, Florian Daniel, Fabio Casati, Cinzia Cappiello

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)


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 languageEnglish
Title of host publicationProceedings of the 2009 International Conference on Information Quality, ICIQ 2009
Publication statusPublished - 2009
Externally publishedYes
Event14th International Conference on Information Quality, ICIQ 2009 - Potsdam, Germany
Duration: 7 Nov 20098 Nov 2009


Conference14th International Conference on Information Quality, ICIQ 2009


  • 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

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