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QloUD
Quality of Uncertain Data
Projektbeschreibung
Many real-life applications, for example data integration, data extraction, risk-management or sensor systems, naturally produce uncertain data. One of the most important goals in these applications is to produce data of high quality. This leads to the following open questions:
  • What does high quality exactly mean with respect to uncertainty and impreciseness?
  • What metrics are most qualified for quantifying the quality with respect to these means?
Currently, the most quality metrics have been defined for appropriately scoring the fineness of certain data and hence only insufficiently capture what is intuitively the quality of uncertain data. As we think, for adequately scoring quality of uncertain data new metrics for existing quality criteria as well as new quality criteria themselves are required. Moreover, as one of the most important methods for improving quality, we consider the integration of uncertain data. In this context, we focus on three elementary questions:
  • How to efficiently and effectively detect duplicates, if data are not only unclean but also imprecise and uncertain?
  • How to combine the uncertain information given by multiple duplicates so that a tuple of higher quality results?
  • How can the expressive modeling power of uncertain data models be used to capture uncertainty coming up during the integration process?
The QloUD project aims to develop techniques for properly scoring the quality of uncertain data as well as to develop techniques for properly integrating uncertain data.
Beteiligte Personen
Publikationen im Projekt
2012
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Fabian Panse, Wolfram Wingerath, Steffen Friedrich, Norbert Ritter
Key-based Blocking of Duplicates in Entity-Independent Probabilistic Data
In: The 17th International Conference on Information Quality
2011
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Fabian Panse, Norbert Ritter
Incorporating Domain Knowledge and User Expertise in Probabilistic Tuple Merging
In: 5th International Conference on Scalable Uncertainty Management
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Fabian Panse, Norbert Ritter
Relational Data Completeness in the Presence of Maybe-Tuples
In: Ingenierie des Systemes d'Information
Studentische Arbeiten im Projekt
2018
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Masterarbeit von Jennifer Soltau
Analyse und Klassifikation existierender Verfahren zur Kollektiven Duplikaterkennung
Gutachter: Norbert Ritter, Fabian Panse
2014
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Masterarbeit von Erik Meyer
Erzeugung von probabilistischen und bestimmten Daten mittels des Datengenerierungswerkzeugs ProbGee
Betreuer: Wolfram Wingerath, Steffen Friedrich     Gutachter: Norbert Ritter, Dirk Bade
2012
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Diplomarbeit von Lennart Helm
Anpassung eines Nearest-Neighbor basierten Duplikaterkennungsverfahrens an das Konzept der probabilistischen Daten
Betreuer: Fabian Panse     Gutachter: Norbert Ritter, Matthias Rarey
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Diplomarbeit von David Haasenleder
Ein Umfragesystem zur Generierung realistischer probabilistischer Testdatenbasen
Betreuer: Fabian Panse     Gutachter: Norbert Ritter, Guido Gryczan