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- W2754021958 abstract "As services for Big Data Analysis (BDA) become prevalent, analysis services with intelligence and autonomy using automatic service composition (ASC) show bright prospects in the BDA market. Selection is one of the most important phases of successful ASC process. Moreover, it became competitive with the rise of demand for the services and criticalness of the BDA process. It is a challenge to accomplish a successful uninterruptable composition while serving diverse custom selection requirements. In the case of failure, it results in complete loss of time and resources. Traditional approaches are not applicable to handle failures during long running transactions. Instead, compensation suggests to being an error recovery. Therefore, analytics transactions scheduled as a composition of a set of compensable transactions. However, compensable services are a higher price and consume more time. Moreover, consumers equipped with diverse requirements. It is necessary to guarantee the critical stages of workflow using compensable services rather than whole workflow. Therefore, we proposed customizable Transaction and QoS-aware service selection approach under five user custom settings based on genetic algorithm (GA) to address above concerns. QoS-awareness facilitated by multi-objective QoS criteria and GA is used for multivariate optimization. We conducted a thorough evaluation, and it shows proposed method effectively and efficiently reach the global optimal of the overall selection criteria." @default.
- W2754021958 created "2017-09-25" @default.
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- W2754021958 date "2017-06-01" @default.
- W2754021958 modified "2023-09-27" @default.
- W2754021958 title "QoS and Customizable Transaction-Aware Selection for Big Data Analytics on Automatic Service Composition" @default.
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- W2754021958 doi "https://doi.org/10.1109/scc.2017.22" @default.
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