The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Balancing push and pull for data broadcast
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Multidimensional access methods
ACM Computing Surveys (CSUR)
Efficient algorithms for scheduling data broadcast
Wireless Networks
Client-server computing in mobile environments
ACM Computing Surveys (CSUR)
Journal of Computer and System Sciences - 30th annual ACM symposium on theory of computing
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Data on Air: Organization and Access
IEEE Transactions on Knowledge and Data Engineering
Query Processing in Broadcasted Spatial Index Trees
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Broadcasting and blocking large data sets with an index tree
Broadcasting and blocking large data sets with an index tree
Energy efficient exact kNN search in wireless broadcast environments
Proceedings of the 12th annual ACM international workshop on Geographic information systems
Aggregate nearest neighbor queries in spatial databases
ACM Transactions on Database Systems (TODS)
Efficient query execution on broadcasted index tree structures
Data & Knowledge Engineering
Disseminating dependent data in wireless broadcast environments
Distributed and Parallel Databases
Effective protocols for kNN search on broadcast multi-dimensional index trees
Information Systems
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Data broadcasting provides an effective way to disseminate information in wireless mobile environments using a broadcast channel. Such a technology can be applied to many services, including the location-based service. In this paper, we consider how to provide the group k nearest neighbors (GkNN) queries using data broadcasting. Given a dataset D and a group of points G, GkNN retrieves the k closest data points to G in D. We assume that the data is indexed by an R-tree. Since the data are broadcast sequentially on broadcast channels, adapting the previous proposed algorithms to the broadcasting environment may result in a longer latency (time elapsed between issuing and termination of the query) and a larger tuning time (the amount of time spent listening to the broadcast). We propose a simple but efficient protocol for exact GkNN queries on a broadcast R-tree in terms of the tuning time, latency, and memory usage on the clients. We last validate the proposed protocol by extensive experiments.