Fault-tolerant, load-balancing queries in telegraph
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
TelegraphCQ: continuous dataflow processing
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Load Shedding for Aggregation Queries over Data Streams
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
The VLDB Journal — The International Journal on Very Large Data Bases
TinyDB: an acquisitional query processing system for sensor networks
ACM Transactions on Database Systems (TODS) - Special Issue: SIGMOD/PODS 2003
Load shedding in a data stream manager
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Memory-limited execution of windowed stream joins
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Resource sharing in continuous sliding-window aggregates
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Scheduling strategies and their evaluation in a data stream management system
BNCOD'06 Proceedings of the 23rd British National Conference on Databases, conference on Flexible and Efficient Information Handling
The anatomy of a stream processing system
BNCOD'06 Proceedings of the 23rd British National Conference on Databases, conference on Flexible and Efficient Information Handling
Load shedding in data stream management systems using application semantics
BNCOD'10 Proceedings of the 27th British national conference on Data Security and Security Data
Multilevel secure data stream processing: Architecture and implementation
Journal of Computer Security - DBSec 2011
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In data stream management systems (DSMSs), Quality of Service (or QoS) requirements, as specified by users, are extremely important. To satisfy QoS requirements throughout the life of a data stream, result characteristics need to be monitored at runtime and adjustments made continuously. It has been shown that in a DSMS, switching scheduling strategies at runtime can change tuple latency requirements. DSMSs also experience significant fluctuations in input rates (termed bursty inputs). In order to meet the QoS requirements in the presence of bursty inputs, a load shedding strategy is critical. This also entails monitoring of QoS measures at run-time to meet expected QoS requirements.This paper addresses load shedding issues for MavStream, a DSMS being developed at UT Arlington. To cope with situations where the arrival rates of input streams exceed the processing capacity of the system, we have incorporated load shedders into the query processing model. The runtime optimizer continually monitors the output and decides when to turn on the shedders and how much to shed. Choice of shedders is done to minimize the error in the output. Shedders have been incorporated as part of the buffers to minimize the overhead for load shedding. Finally, load shedders are activated and deactivated dynamically by the runtime optimizer. Both random and semantic load shedding techniques are supported to match application semantics.