Fibonacci heaps and their uses in improved network optimization algorithms
Journal of the ACM (JACM)
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
Nearest neighbor queries in road networks
GIS '03 Proceedings of the 11th ACM international symposium on Advances in geographic information systems
The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Group Nearest Neighbor Queries
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Aggregate Nearest Neighbor Queries in Road Networks
IEEE Transactions on Knowledge and Data Engineering
Aggregate nearest neighbor queries in spatial databases
ACM Transactions on Database Systems (TODS)
Efficient query evaluation on probabilistic databases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Finding aggregate nearest neighbor efficiently without indexing
Proceedings of the 2nd international conference on Scalable information systems
Ranking queries on uncertain data: a probabilistic threshold approach
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Probabilistic Group Nearest Neighbor Queries in Uncertain Databases
IEEE Transactions on Knowledge and Data Engineering
Graph Theory
Efficient Processing of Top-k Queries in Uncertain Databases
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Proceedings of the twenty-eighth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Monitoring path nearest neighbor in road networks
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Probabilistic nearest-neighbor query on uncertain objects
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
Superseding Nearest Neighbor Search on Uncertain Spatial Databases
IEEE Transactions on Knowledge and Data Engineering
Mining Frequent Subgraph Patterns from Uncertain Graph Data
IEEE Transactions on Knowledge and Data Engineering
Querying Graphs in Protein-Protein Interactions Networks Using Feedback Vertex Set
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
k-nearest neighbors in uncertain graphs
Proceedings of the VLDB Endowment
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Most recently, uncertain graph data begin attracting significant interests of database research community, because uncertainty is the intrinsic property of the real-world and data are more suitable to be modeled as graphs in numbers of applications, e.g. social network analysis, PPI networks in biology, and road network monitoring. Meanwhile, as one of the basic query operators, aggregate nearest neighbor (ANN) query retrieves a data entity whose aggregate distance, e.g. sum, max, to the given query data entities is smaller than those of other data entities in a database. ANN query on both certain graph data and high dimensional data has been well studied by previous work. However, existing ANN query processing approaches cannot handle the situation of uncertain graphs, because topological structures of an uncertain graph may vary in different possible worlds. Motivated by this, we propose the aggregate nearest neighbor query in uncertain graphs (UG-ANN) in this paper. First of all, we give the formal definition of UG-ANN query and the basic UG-ANN query algorithm. After that, to improve the efficiency of UG-ANN query processing, we develop two kinds of pruning approaches, i.e. structural pruning and instance pruning. The structural pruning takes advantages the monotonicity of the aggregate distance to derive the upper and lower bounds of the aggregate distance for reducing the graph size. Whereas, the instance pruning decreases the number of possible worlds to be checked in the searching tree. Comprehensive experimental results on real-world data sets demonstrate that the proposed method significantly improves the efficiency of the UG-ANN query processing.