An efficient implementation of a scaling minimum-cost flow algorithm
Journal of Algorithms
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SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
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Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
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PDIS '91 Proceedings of the first international conference on Parallel and distributed information systems
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Proceedings of the 2002 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
Histogramming Data Streams with Fast Per-Item Processing
ICALP '02 Proceedings of the 29th International Colloquium on Automata, Languages and Programming
Exploiting Punctuation Semantics in Continuous Data Streams
IEEE Transactions on Knowledge and Data Engineering
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ACM SIGMOD Record
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ICDE '02 Proceedings of the 18th International Conference on Data Engineering
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The VLDB Journal — The International Journal on Very Large Data Bases
Semantic Approximation of Data Stream Joins
IEEE Transactions on Knowledge and Data Engineering
Maximizing the output rate of multi-way join queries over streaming information sources
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Processing sliding window multi-joins in continuous queries over data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Window query processing for joining data streams with relations
CASCON '07 Proceedings of the 2007 conference of the center for advanced studies on Collaborative research
Transformation of continuous aggregation join queries over data streams
SSTD'07 Proceedings of the 10th international conference on Advances in spatial and temporal databases
Processing exact results for sliding window joins over data streams using disk storage
International Journal of Intelligent Information and Database Systems
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Load shedding techniques generate approximate sliding window join results when memory constraints prevent exact computation. The previously proposed random load shedding method drops input tuples without consideration for the number of outputs created, while the recently proposed semantic load shedding technique aims to produce the largest possible result set. We consider a new model in which data stream tuples contain numerical importance values relevant to the query source and seek to maximize the importance of the approximate join result. We show that both random load shedding and semantic load shedding are sub-optimal in this situation, while the techniques presented in this paper satisfy the objective function by considering both tuple importance and join attribute distributions. We extend the existing offline semantic approximation technique to make it compatible with our objective function and show that it is less space and time efficient than our new optimal offline algorithm for small and large join memory allotments. We also introduce four efficient online algorithms, which are quite promising in maximizing the importance of the approximate join result without foreknowledge of input streams.