DiAl: Distributed Streaming Analytics Anywhere, Anytime
DiAl: Distributed Streaming Analytics Anywhere, Anytime
复制标题
DiAl：随时随地分布式流分析
DOI：
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发表时间：
2013
影响因子：
2.5
通讯作者：
J. Goldstein
中科院分区：
文献类型：
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作者：
I. Santos;M. Tilly;Badrish Chandramouli;J. Goldstein
预计到2020年，联网设备将增长到500亿台。通过我们的行业合作伙伴及其用例，我们验证了飞行数据处理的重要性，以产生低延迟的结果，特别是本地和全球数据分析能力。为了科普分布式流分析场景带来的可扩展性挑战，我们提出了两种新技术：（1）JStreams，一种低占用空间和高效的JavaScript复杂事件处理引擎，支持异构设备上的本地分析;（2）DiAlM，一种利用云边缘演进拓扑的分布式分析管理服务。在演示中，基于一个真实的制造用例，我们通过操作员通过全局分析来监督制造设备，并通过本地检查制造设备生成的数据来深入研究工厂车间的警报案例。
Connected devices are expected to grow to 50 billion in 2020. Through our industrial partners and their use cases, we validated the importance of inflight data processing to produce results with low latency, in particular local and global data analytics capabilities. In order to cope with the scalability challenges posed by distributed streaming analytics scenarios, we propose two new technologies: (1) JStreams, a low footprint and efficient JavaScript complex event processing engine supporting local analytics on heterogeneous devices and (2) DiAlM, a distributed analytics management service that leverages cloud-edge evolving topologies. In the demonstration, based on a real manufacturing use case, we walk through a situation where operators supervise manufacturing equipment through global analytics, and drill down into alarm cases on the factory floor by locally inspecting the data generated by the manufacturing equipment.