Queueing networks and Markov chains: modeling and performance evaluation with computer science applications
Clustering Algorithms
Computer Networking: A Top-Down Approach Featuring the Internet
Computer Networking: A Top-Down Approach Featuring the Internet
A Framework for Generating Network-Based Moving Objects
Geoinformatica
IEEE Transactions on Computers
Management of Dynamic Location Information in DOMINO
EDBT '02 Proceedings of the 8th International Conference on Extending Database Technology: Advances in Database Technology
Modeling and Querying Moving Objects
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
QoS-Driven Load Shedding on Data Streams
EDBT '02 Proceedings of the Worshops XMLDM, MDDE, and YRWS on XML-Based Data Management and Multimedia Engineering-Revised Papers
Approximate join processing over data streams
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Flux: An Adaptive Partitioning Operator for Continuous Query Systems
Flux: An Adaptive Partitioning Operator for Continuous Query Systems
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Towards scalable location-aware services: requirements and research issues
GIS '03 Proceedings of the 11th ACM international symposium on Advances in geographic information systems
Load Shedding for Aggregation Queries over Data Streams
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
SINA: scalable incremental processing of continuous queries in spatio-temporal databases
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Semantic Approximation of Data Stream Joins
IEEE Transactions on Knowledge and Data Engineering
Data Triage: An Adaptive Architecture for Load Shedding in TelegraphCQ
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
SEA-CNN: Scalable Processing of Continuous K-Nearest Neighbor Queries in Spatio-temporal Databases
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Run-time operator state spilling for memory intensive long-running queries
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Load shedding in stream databases: a control-based approach
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Window-aware load shedding for aggregation queries over data streams
VLDB '06 Proceedings of the 32nd 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
Load shedding in a data stream manager
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
CAPE: continuous query engine with heterogeneous-grained adaptivity
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
On discovering moving clusters in spatio-temporal data
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
Location-dependent query processing: Where we are and where we are heading
ACM Computing Surveys (CSUR)
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Moving object environments are characterized by large numbers of objects continuously sending location updates. At times, data arrival rates may spike up, causing the load on the system to exceed its capacity. This may result in increased output latencies, potentially leading to invalid or obsolete answers. Dropping data randomly, the most frequently used approach in the literature for load shedding, may adversely affect the accuracy of the results. We thus propose a load shedding technique customized for spatio-temporal stream data. In our model, spatio-temporal properties, such as location, time, direction and speed over time, serve as critical factors in the load shedding decision. The main idea is to abstract similarly moving objects into moving clusters which serve as summaries of their members' movement. Based on resource restrictions, members within clusters may be selectively discarded, while their locations are being approximated by their respective moving clusters. Our experimental study illustrates the performance gains achieved by our load-shedding framework and the tradeoff between the amount of data shed and the result accuracy.