FlexMash 2.0 - Flexible Modeling and Execution of Data Mashups
FlexMash 2.0 - Flexible Modeling and Execution of Data Mashups
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FlexMash 2.0 - 数据混搭的灵活建模和执行
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发表时间：
2016
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通讯作者：
M. Behringer
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作者：
Pascal Hirmer;M. Behringer
近年来，通过廉价的硬件、快速的网络技术以及大多数领域内的日益数字化，数据量大幅增加。产生的数据通常是异构的、动态的，并且来自许多高度分布式的数据源。从这些数据中获取信息和知识可以提高解决问题的效率，从而为公司带来更高的利润。然而，这是一个巨大的挑战-通常被称为大数据问题。斯图加特大学开发的数据mashup工具FlexMash通过提供一种集成和处理异构动态数据源的方法来应对这一挑战。通过这样做，FlexMash专注于（i）基于管道和过滤器模式由领域专家对数据集成和处理场景进行建模的简单方法，（ii）基于用户的非功能性需求的灵活执行，以及（iii）支持通用方法的高度可扩展性。该工具的第一个版本在2015年ICWE快速混搭挑战赛期间发布。在本文中，我们介绍了新版本FlexMash 2.0，它引入了新功能，例如基于云的执行和运行时的人机交互。这些概念已经在ICWE 2016年快速混搭挑战赛上提出。
In recent years, the amount of data highly increases through cheap hardware, fast network technology, and the increasing digitization within most domains. The data produced is oftentimes heterogeneous, dynamic and originates from many highly distributed data sources. Deriving information and, as a consequence, knowledge from this data can lead to a higher effectiveness for problem solving and thus higher profits for companies. However, this is a great challenge – oftentimes referred to as Big Data problem. The data mashup tool FlexMash, developed at the University of Stuttgart, tackles this challenge by offering a means for integration and processing of heterogeneous, dynamic data sources. By doing so, FlexMash focuses on (i) an easy means to model data integration and processing scenarios by domain-experts based on the Pipes and Filters pattern, (ii) a flexible execution based on the user’s non-functional requirements, and (iii) high extensibility to enable a generic approach. A first version of this tool was presented during the ICWE Rapid Mashup Challenge 2015. In this article, we present the new version FlexMash 2.0, which introduces new features such as cloud-based execution and human interaction during runtime. These concepts have been presented during the ICWE Rapid Mashup Challenge 2016.