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Cloud‐based data streams optimization

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Many modern applications of sensor networks and transaction analysis require real‐time processing of their stream data sets. These data streams vary continuously over time. Current stream processing approaches focus on only one of the two optimization perspectives, proposing optimization techniques for data streams processing regardless of the processing environment or improving the processing environment only. In this paper, a brief survey of recent approaches to data streams processing coming from the two optimizations perspectives is proposed; their shortcomings are presented as well. Then, a proposal to an innovative and integrative framework is developed; it is referred to as the continuous query optimization based on multiple plans (CQOMP) for data streams over the cloud environment. CQOMP combines the two optimization perspectives and provides an optimized stream clusters processing using multiple split query plans. Each plan is constructed for a cluster of data that has nearest characteristics and it processes streams tuples over the cloud. We also propose a novel algorithm called the optimized multiple plans (OMP) for processing data streams clusters on Cloud Computing. The OMP algorithm efficiently divides data streams and generates optimized multiple split plans. Each plan is for processing a group of data streams on the cloud. We present the experimental results of the OMP solution compared to the alternative state‐of‐the‐art data stream approaches. The experiments show the efficiency and the scalability of the combined OMP algorithm on different cloud environments, the real Amazon cloud environment, and the simulated windows azure cloud environment.

This article is categorized under:

  • Fundamental Concepts of Data and Knowledge > Big Data Mining
  • Technologies > Classification
  • Technologies > Data Preprocessing
  • Technologies > Structure Discovery and Clustering
The proposed CQOMP framework
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The average processed tuples over time of the OMP and the operator tree algorithms. (a) The average number of processed tuples over time of the OMP and the operator tree over data set of 200 tuples and (b) The average number of processed tuples over time of the OMP and the operator tree over data set of 1,000 tuples
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The average the processed tuples over time of the compile‐time optimization and the OMP algorithm. (a) The average number of processed tuples over time of the OMP and the compile‐time optimization over data set of 200 tuples and (b) The average number of processed tuples over time of the OMP and the compile‐time optimization over data set of 1,000 tuples
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The average overall execution time of the operator tree and the OMP algorithm. (a) The average execution time of the OMP and the operator tree over data set of 200 tuples and (b) The average execution time of the OMP and the operator tree over data set of 1,000 tuples
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The average overall execution time of the compile‐time optimization and the OMP algorithm. (a) The average execution time of the OMP and the compile time optimization over data set of 200 tuples and (b) The average execution time of the OMP and the compile time optimization over data set of 1,000 tuples
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The average overall execution time on different cloud environments
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The average overall execution time on the Amazon cloud environment
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The average improvement percentages of execution times of the proposed OMP algorithm over different numbers of VMs. (a) The average improvement percentage of execution time of the proposed OMP over different VMs over data set of 200 tuples and (b) The average improvement percentage of execution time of the proposed OMP over different VMs over data set of 1,000 tuples
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The processed tuples over time of the OMP and the NS‐QM algorithms. (a) The average number of processed tuples over time of the OMP and the NS‐QM over data set of 200 tuples and (b) The average number of processed tuples over time of the OMP and the NS‐QM over data set of 1,000 tuples
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The average overall execution time of the OMP algorithm and the NS‐QM algorithm. (a) The average execution time of the OMP and NS‐QM over data set 200 tuples and (b) The average execution time of the OMP and NS‐QM over data set 1,000 tuples
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The processed tuples over time of the operator‐set‐cloud methodology and the OMP algorithm. (a) The average number of processed tuples over time of the OMP and the operator set cloud over data set of 200 tuples and (b) The average number of processed tuples over time of the OMP and the operator set cloud over data set of 1,000 tuples
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The average overall execution time of the operator‐set‐cloud methodology and the OMP algorithm. (a) The average execution time of the operator set cloud and the OMP over data set of 200 tuples and (b) The average execution time of the operator set cloud and the OMP over data set of 1,000 tuples
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The proposed OMP algorithm
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Fundamental Concepts of Data and Knowledge > Big Data Mining
Technologies > Classification
Technologies > Data Preprocessing
Technologies > Structure Discovery and Clustering

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