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TOSCA4Mashups: enhanced method for on-demand data mashup provisioning

机译:TOSCA4Mashups:用于按需数据mashup设置的增强方法

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摘要

Nowadays, the amount of data increases tremendously. Extracting information and generating knowledge from this data is a great challenge. To cope with this issue -oftentimes referred to as big data problem - we need effective means for efficient data integration, data processing, and data analysis. To enable flexible, explorative and ad-hoc data processing, several data mashup approaches and tools have been developed in the past. One of these tools is FlexMash -a data mashup tool developed at the University of Stuttgart. By offering domain-specific graphical modeling as well as a pattern-based execution, FlexMash enables usage by a wide range of users, both domain experts and technical experts. The core idea of FlexMash is a flexible execution of data mashups using different, user-requirement-dependent execution components. In this paper, we present a new approach for on-demand, automated provisioning of these components in a cloud computing environment using the Topology and Orchestration Specification for Cloud Applications. This enables many advantages for mashup execution such as scalability, availability and cost savings.
机译:如今,数据量急剧增加。从这些数据中提取信息并产生知识是一个巨大的挑战。为了解决此问题(通常称为大数据问题),我们需要有效的手段来进行有效的数据集成,数据处理和数据分析。为了实现灵活的,探索性的和即席的数据处理,过去已经开发了几种数据混搭方法和工具。这些工具之一是FlexMash-一种由斯图加特大学开发的数据混搭工具。通过提供特定领域的图形建模以及基于模式的执行,FlexMash使得领域专家和技术专家都能被广泛的用户使用。 FlexMash的核心思想是使用不同的,依赖于用户需求的执行组件来灵活地执行数据混搭。在本文中,我们提出了一种新方法,该方法使用云应用程序的拓扑和编排规范在云计算环境中按需自动配置这些组件。这为混搭执行提供了许多优势,例如可伸缩性,可用性和成本节省。

著录项

  • 来源
    《Computer science》 |2017年第4期|291-300|共10页
  • 作者单位

    Institute of Parallel and Distributed Systems, University of Stuttgart, Universitaetsstr. 38, D-70569 Stuttgart, Germany;

    Institute of Parallel and Distributed Systems, University of Stuttgart, Universitaetsstr. 38, D-70569 Stuttgart, Germany;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Data Mashups; TOSCA; Provisioning; Cloud Computing;

    机译:数据混搭;TOSCA;供应;云计算;

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