A General Method for Estimating Correlated Aggregates Over a Data Stream

Tirthapura, Srikanta; Woodruff, David P.

doi:10.1007/s00453-014-9917-1

Cited by 3 publications

(2 citation statements)

References 30 publications

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“…However, AQ-K-slack adjust α based on the quality of produced query results and do not apply result revision. Approximation-based [7,9,33,34]. There has been considerable research on computing approximate aggregates over data streams.…”

Section: Related Workmentioning

confidence: 99%

Quality-driven processing of sliding window aggregates over out-of-order data streams

Zhou

Jerzak

et al. 2015

Proceedings of the 9th ACM International Conference on Distributed Event-Based Systems

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One fundamental challenge in data stream processing is to cope with the ubiquity of disorder of tuples within a stream caused by network latency, operator parallelization, merging of asynchronous streams, etc. High result accuracy and low result latency are two conflicting goals in out-of-order stream processing. Different applications may prefer different extent of trade-offs between the two goals. However, existing disorder handling solutions either try to meet one goal to the extreme by sacrificing the other, or try to meet both goals but have shortcomings including unguaranteed result accuracy or increased complexity in operator implementation and application logic.To meet different application requirements on the latency versus result accuracy trade-off in out-of-order stream processing, in this paper, we propose to make this trade-off user-configurable. Particularly, focusing on sliding window aggregates, we introduce AQ-K-slack, a buffer-based qualitydriven disorder handling approach. AQ-K-slack leverages techniques from the fields of sampling-based approximate query processing and control theory. It can adjust the input buffer size dynamically to minimize the result latency, while respecting user-specified threshold on relative errors in produced query results. AQ-K-slack requires no a priori knowledge of disorder characteristics of data streams, and imposes no changes to the query operator implementation or the application logic. Experiments over real-world out-of-order data streams show that, compared to the stateof-art, AQ-K-slack can reduce the average buffer size, thus the average result latency, by at least 51% while respecting user-specified requirement on the accuracy of query results.

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Section: Related Workmentioning

confidence: 99%

Quality-driven processing of sliding window aggregates over out-of-order data streams

Zhou

Jerzak

et al. 2015

Proceedings of the 9th ACM International Conference on Distributed Event-Based Systems

View full text Add to dashboard Cite

show abstract

“…The increasing development and use of large numbers of sensors has added to this situation. These advances in technology have led to streams of data [18,19]. In order to be able to make sense of these data, stream mining algorithms are needed [5,9,20].…”

Section: Introduction and Related Workmentioning

confidence: 99%

Stream mining of frequent sets with limited memory

Cameron

Cuzzocrea²,

Leung

2013

Proceedings of the 28th Annual ACM Symposium on Applied Computing

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With advances in technology, streams of data are produced in many applications. Efficient techniques for extracting implicit, previously unknown, and potentially useful information (e.g., in the form frequent sets) from data streams are in demand. Many existing stream mining algorithms capture important streaming data and assume that the captured data can fit into main memory. However, problem arose when the available memory is so limited that such an assumption does not hold. In this paper, we propose a novel data structure called DSTable to capture important data from the streams onto the disk. The DSTable can be easily maintained; it can be applicable for mining frequent sets from datasets, especially in limited memory environments.

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Frequent Subgraph Mining from Streams of Uncertain Data

Leung

Cuzzocrea

2008

Proceedings of the Eighth International C* Conference on Computer Science &Amp; Software Engineering - C3S2E '15

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A General Method for Estimating Correlated Aggregates Over a Data Stream

Cited by 3 publications

References 30 publications

Quality-driven processing of sliding window aggregates over out-of-order data streams

Quality-driven processing of sliding window aggregates over out-of-order data streams

Stream mining of frequent sets with limited memory

Frequent Subgraph Mining from Streams of Uncertain Data

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