Efficient processing of top-k spatial preference queries
Proceedings of the VLDB Endowment
Preference-based top-k spatial keyword queries
Proceedings of the 1st international workshop on Mobile location-based service
A safe-exit approach for efficient network-based moving range queries
Data & Knowledge Engineering
PFRF: An adaptive data replication algorithm based on star-topology data grids
Future Generation Computer Systems
Daisy: the center for data-intensive systems at Aalborg University
ACM SIGMOD Record
GeoRank: an efficient location-aware news feed ranking system
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Efficient top-k spatial distance joins
SSTD'13 Proceedings of the 13th international conference on Advances in Spatial and Temporal Databases
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A spatial preference query ranks objects based on the qualities of features in their spatial neighborhood. For example, using a real estate agency database of flats for lease, a customer may want to rank the flats with respect to the appropriateness of their location, defined after aggregating the qualities of other features (e.g., restaurants, cafes, hospital, market, etc.) within their spatial neighborhood. Such a neighborhood concept can be specified by the user via different functions. It can be an explicit circular region within a given distance from the flat. Another intuitive definition is to assign higher weights to the features based on their proximity to the flat. In this paper, we formally define spatial preference queries and propose appropriate indexing techniques and search algorithms for them. Extensive evaluation of our methods on both real and synthetic data reveals that an optimized branch-and-bound solution is efficient and robust with respect to different parameters.