Computational geometry: algorithms and applications
Computational geometry: algorithms and applications
NiagaraCQ: a scalable continuous query system for Internet databases
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
Space-time trade-offs for some ranking and searching queries
Information Processing Letters
Design of Dynamic Data Structures
Design of Dynamic Data Structures
Continual Queries for Internet Scale Event-Driven Information Delivery
IEEE Transactions on Knowledge and Data Engineering
An Evaluation of Non-Equijoin Algorithms
VLDB '91 Proceedings of the 17th International Conference on Very Large Data Bases
PSoup: a system for streaming queries over streaming data
The VLDB Journal — The International Journal on Very Large Data Bases
Deterministic sampling and range counting in geometric data streams
SCG '04 Proceedings of the twentieth annual symposium on Computational geometry
Range counting over multidimensional data streams
SCG '04 Proceedings of the twentieth annual symposium on Computational geometry
Data streams: algorithms and applications
Foundations and Trends® in Theoretical Computer Science
Streaming queries over streaming data
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Monitoring streams: a new class of data management applications
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Scalable continuous query processing by tracking hotspots
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Input-sensitive scalable continuous join query processing
ACM Transactions on Database Systems (TODS)
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A continuous query is a standing query over a dynamic data set whose query result needs to be constantly updated as new data arrive. We consider the problem of constructing a data structure on a set of continuous band-join queries over two data sets R and S, where each band-join query asks for reporting the set { (r,s)∈ R× S | a≤ r–s≤ b} for some parameters a and b, so that given a data update in R or S, one can quickly identify the subset of continuous queries whose results are affected by the update, and compute changes to these results. We present the first nontrivial data structure for this problem that simultaneously achieves subquadratic space and sublinear query time. This is achieved by first decomposing the original problem into two independent subproblems, and then carefully designing data structures suitable for each case, by exploiting the particular structure in each subproblem. A key step in the above construction is a data structure whose performance increases with the degree of clusteredness of the band-joins being indexed. We believe that this structure is of independent interest and should have broad impact in practice. We present the details in [1].