Updating and Querying Databases that Track Mobile Units
Distributed and Parallel Databases - Special issue on mobile data management and applications
Spatially distributed databases on sensors
Proceedings of the 8th ACM international symposium on Advances in geographic information systems
Design and evaluation of a conit-based continuous consistency model for replicated services
ACM Transactions on Computer Systems (TOCS)
The Management of Probabilistic Data
IEEE Transactions on Knowledge and Data Engineering
Capturing the Uncertainty of Moving-Object Representations
SSD '99 Proceedings of the 6th International Symposium on Advances in Spatial Databases
Evaluating probabilistic queries over imprecise data
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Supporting uncertainty in moving objects in network databases
Proceedings of the 13th annual ACM international workshop on Geographic information systems
Trio: a system for data, uncertainty, and lineage
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Databases with uncertainty and lineage
The VLDB Journal — The International Journal on Very Large Data Bases
Cleaning uncertain data with quality guarantees
Proceedings of the VLDB Endowment
Making the World Wide Space happen: New challenges for the Nexus context platform
PERCOM '09 Proceedings of the 2009 IEEE International Conference on Pervasive Computing and Communications
On a generic uncertainty model for position information
QuaCon'09 Proceedings of the 1st international conference on Quality of context
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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.