Breaking BAD: A Data Serving Vision for Big Active Data.
Breaking BAD: A Data Serving Vision for Big Active Data.
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DOI：
10.1145/2933267.2933313
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发表时间：
2016-06
期刊：
影响因子：
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通讯作者：
Tsotras VJ
中科院分区：
文献类型：
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作者：
Carey MJ;Jacobs S;Tsotras VJ
几乎所有今天的大数据系统本质上都是被动的。在这里，我们描述了一个将大数据平台从被动转变为主动的项目。我们详细介绍了可扩展系统的愿景，该系统可以持续可靠地捕获大数据，从而能够及时、自动地向大量感兴趣的用户提供新信息，并支持对历史信息的分析。我们目前正在构建一个大数据(BAD)系统，方法是在这个主动方向上扩展现有的可扩展开源BDM(AsterixDB)。第一篇论文详细介绍了这个糟糕的谜题的数据服务部分，包括它的关键概念和用户模型。
Virtually all of today’s Big Data systems are passive in nature. Here we describe a project to shift Big Data platforms from passive to active. We detail a vision for a scalable system that can continuously and reliably capture Big Data to enable timely and automatic delivery of new information to a large pool of interested users as well as supporting analyses of historical information. We are currently building a Big Active Data (BAD) system by extending an existing scalable open-source BDMS (AsterixDB) in this active direction. This first paper zooms in on the Data Serving piece of the BAD puzzle, including its key concepts and user model.