Efficient processing of spatial joins using R-trees
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
A cost model for query processing in high dimensional data spaces
ACM Transactions on Database Systems (TODS)
Analysis of the Clustering Properties of the Hilbert Space-Filling Curve
IEEE Transactions on Knowledge and Data Engineering
High Dimensional Similarity Joins: Algorithms and Performance Evaluation
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Proceedings of the 17th International Conference on Data Engineering
Efficient OLAP Operations in Spatial Data Warehouses
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
An optimal and progressive algorithm for skyline queries
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
IEEE Transactions on Knowledge and Data Engineering
On computing top-t most influential spatial sites
VLDB '05 Proceedings of the 31st international conference on Very large data bases
DADA: a data cube for dominant relationship analysis
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Progressive computation of the min-dist optimal-location query
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Continuous Skyline Queries for Moving Objects
IEEE Transactions on Knowledge and Data Engineering
Alternative Algorithm for Hilbert's Space-Filling Curve
IEEE Transactions on Computers
On dominating your neighborhood profitably
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Location-Dependent Skyline Query
MDM '08 Proceedings of the The Ninth International Conference on Mobile Data Management
In-Route skyline querying for location-based services
W2GIS'04 Proceedings of the 4th international conference on Web and Wireless Geographical Information Systems
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
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Real-life spatial objects are usually described by their geographic locations (e.g., longitude and latitude), and multiple quality attributes. Conventionally, spatial data are queried by two orthogonal aspects: spatial queries involve geographic locations only; skyline queries are used to retrieve those objects that are not dominated by others on all quality attributes. Specifically, an object p i is said to dominate another object p j if p i is no worse than p j on all quality attributes and better than p j on at least one quality attribute. In this paper, we study a novel query that combines both aspects meaningfully. Given two spatial datasets P and S , and a neighborhood distance *** , the most endangered object query (MEO) returns the object s *** S such that within the distance *** from s , the number of objects in P that dominate s is maximized. MEO queries appropriately capture the needs that neither spatial queries nor skyline queries alone have addressed. They have various practical applications such as business planning, online war games, and wild animal protection. Nevertheless, the processing of MEO queries is challenging and it cannot be efficiently evaluated by existing solutions. Motivated by this, we propose several algorithms for processing MEO queries, which can be applied in different scenarios where different indexes are available on spatial datasets. Extensive experimental results on both synthetic and real datasets show that our proposed advanced spatial join solution achieves the best performance and it is scalable to large datasets.