SSD '95 Proceedings of the 4th 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
Indexing multi-dimensional uncertain data with arbitrary probability density functions
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Probabilistic Ranking Queries on Gaussians
SSDBM '06 Proceedings of the 18th International Conference on Scientific and Statistical Database Management
Efficient indexing methods for probabilistic threshold queries over uncertain data
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Probabilistic nearest-neighbor query on uncertain objects
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
Probabilistic similarity join on uncertain data
DASFAA'06 Proceedings of the 11th international conference on Database Systems for Advanced Applications
Hot Item Detection in Uncertain Data
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
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There are a lot of application domains, e.g. sensor databases, traffic management or recognition systems, where objects have to be compared based on vague and uncertain data. Feature databases with uncertain data require special methods for effective similarity search. In this paper, we propose an effective and efficient probabilistic similarity ranking algorithm that exploits the full information given by inexact object representations. Thereby, we assume that the objects are given in form of discrete probabilistic object locations in particular several object snapshots with confidence values. Based on the given object representations, we suggest diverse variants of probabilistic ranking schemes. In a detailed experimental evaluation, we demonstrate the benefits of our probabilistic ranking approaches. The experiments show that we can achieve high quality query results while keeping the computational cost quite small.