The design and analysis of spatial data structures
The design and analysis of spatial data structures
A predicate matching algorithm for database rule systems
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Segment indexes: dynamic indexing techniques for multi-dimensional interval data
SIGMOD '91 Proceedings of the 1991 ACM SIGMOD international conference on Management of data
Selection predicate indexing for active databases using interval skip lists
Information Systems
Multidimensional access methods
ACM Computing Surveys (CSUR)
Filtering algorithms and implementation for very fast publish/subscribe systems
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Continuously adaptive continuous queries over streams
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Efficient query monitoring using adaptive multiple key hashing
Proceedings of the eleventh international conference on Information and knowledge management
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Efficient Interval Indexing for Content-Based Subscription E-Commerce and E-Service
CEC-EAST '04 Proceedings of the E-Commerce Technology for Dynamic E-Business, IEEE International Conference
Monitoring streams: a new class of data management applications
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Efficient structural joins with on-the-fly indexing
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
Adaptive load shedding for windowed stream joins
Proceedings of the 14th ACM international conference on Information and knowledge management
An Exact Closed-Form Formula for d-Dimensional Quadtree Decomposition of Arbitrary Hyperrectangles
IEEE Transactions on Knowledge and Data Engineering
Proceedings of the 6th international conference on Mobile systems, applications, and services
Prefilter: predicate pushdown at streaming speeds
SSPS '08 Proceedings of the 2nd international workshop on Scalable stream processing system
Measuring evolving data streams' behavior through their intrinsic dimension
New Generation Computing
A multi-objective multi-modal optimization approach for mining stable spatio-temporal patterns
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Functional brain imaging with multi-objective multi-modal evolutionary optimization
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Fast burst correlation of financial data
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Efficient filtering query indexing in data stream
WISE'06 Proceedings of the 7th international conference on Web Information Systems
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A large number of continual range queries can be issued against a data stream. Usually, a main memory-based query index with a small storage cost and a fast search time is needed, especially if the stream is rapid. In this paper, we present a CEI-based query index that meets both criteria for efficient processing of continual interval queries in a streaming environment. This new query index is centered around a set of predefined virtual containment-encoded intervals, or CEIs. The CEIs are used to first decompose query intervals and then perform efficient search operations. The CEIs are defined and labeled such that containment relationships among them are encoded in their IDs. The containment encoding makes decomposition and search operations efficient because integer additions and logical shifts can be used to carry out most of the operations. Simulations are conducted to evaluate the effectiveness of the CEI-based query index and to compare it with alternative approaches. The results show that the CEI-based query index significantly outperforms existing approaches in terms of both storage cost and search time.