CAPE1Continuous Query Engine with Heterogeneous-Grained Adaptivity

Rundensteiner, Elke A.; DING, L; SUTHERLAND, T; ZHU, Y; PIELECH, B; MEHTA, N

doi:10.1016/b978-012088469-8/50145-5

Cited by 32 publications

(47 citation statements)

References 5 publications

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“…To ensure a fair comparison, all systems are implemented in the CAPE platform [5]. Each implementation uses as much of the same code base as possible.…”

Section: Experimental Designmentioning

confidence: 99%

High performance stream query processing with correlation-aware partitioning

Cao

Rundensteiner

2013

Proc. VLDB Endow.

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State-of-the-art optimizers produce one single optimal query plan for all stream data, in spite of such a singleton plan typically being sub-optimal or even poor for highly correlated data. Recently a new stream processing paradigm, called multi-route approach, has emerged as a promising approach for tackling this problem. Multiroute first divides data streams into several partitions and then creates a separate query plan for each combination of partitions. Unfortunately current approaches suffer from severe shortcomings, in particular, the lack of an effective partitioning strategy and the prohibitive query optimization expense. In this work we propose the first practical multi-route optimizer named correlation-aware multi-route stream query optimizer (or CMR) that solves both problems. By exploiting both intra-and inter-stream correlations of streams, CMR produces effective partitions without having to undertake repeated expensive query plan generation. The produced partitions not only are best served by distinct optimal query plans, but also leverage the partition-driven pruning opportunity. Experimental results with both synthetic and real life stream data confirm that CMR outperforms the state-of-the-art solutions up to an order of magnitude in both the query optimization time and the run-time execution performance.

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“…To ensure a fair comparison, all systems are implemented in the CAPE platform [5]. Each implementation uses as much of the same code base as possible.…”

Section: Experimental Designmentioning

confidence: 99%

High performance stream query processing with correlation-aware partitioning

Cao

Rundensteiner

2013

Proc. VLDB Endow.

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“…We have implemented the AMoS adaptive scheduling selection framework in the CAPE continuous query system [18]. To fully test the capability of AMoS, in our experiments, we use both synthetic data and real-world data from the Internet Traffic Archive [12].…”

Section: Methodsmentioning

confidence: 99%

“…We have experimentally evaluated the four scheduling algorithms described in Section 2.1, namely Round Robin, FIFO, MTIQ and Chain, in our continuous query system called CAPE [18]. Figure 2 shows the performance of these algorithms with regard to the total queue sizes and the result latency.…”

Section: Experimental Evaluation Of Scheduling Algorithmsmentioning

confidence: 99%

“…• We experimentally study the performance of a variety of state-of-the-art scheduling algorithms in an actual continuous query system named CAPE [18] to examine their strengths and weaknesses under varying performance objectives and query workloads.…”

Section: Our Approach: Adaptive Scheduling Selection Frameworkmentioning

confidence: 99%

“…Correspondingly, stream processing systems, such as Aurora [1], CAPE [18], NiagaraCQ [7], * The research was partly supported by the RDC grant 2003-04 on "Online Stream Monitoring Systems: Untethered Healthcare, Intrusion Detection, and Beyond." Thanks to TotalETL for providing equipments.…”

Section: Introductionmentioning

confidence: 99%

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An Adaptive Multi-Objective Scheduling Selection Framework for Continuous Query Processing

Sutherland

Pielech

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et al.

9th International Database Engineering &Amp;amp; Application Symposium (IDEAS'05)

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A Semantics for a Query Language over Sensors, Streams and Relations

Brenninkmeijer

Galpin

Fernandes

et al.

Lecture Notes in Computer Science

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Abstract. We introduce a query language over sensors, streams and relations and formally describe its semantics. Although the language was specifically designed for sensor network querying, where data is pulled into streams, the semantics contributed in the paper also encompasses the case in which data is pushed onto streams or else lies stored in classical relations. The approach taken is that continuous queries over streams are an extension of classical queries over stored extents. Apart from the fact that query evaluation over streams is reactive, or periodic, the main difference is the conception of windows as an additional collection type with the consequent use of type converter operations to and from streams and windows (which, as bounded collections of tuples, can be operated on in a relational-algebraic setting). The language and the semantics we provide for it advance on previous work in being more comprehensive with respect to the collection types allowed and in being more flexible as to the number and content of the windows contributing to the result at each evaluation event of a continuous query. The formalization advances on previous work in clarifying the implementation onus.

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CAPE1Continuous Query Engine with Heterogeneous-Grained Adaptivity

Cited by 32 publications

References 5 publications

High performance stream query processing with correlation-aware partitioning

High performance stream query processing with correlation-aware partitioning

An Adaptive Multi-Objective Scheduling Selection Framework for Continuous Query Processing

A Semantics for a Query Language over Sensors, Streams and Relations

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