大数据流处理

Yidan Wang, M. HoseinyFarahabady, Z. Tari, Albert Y. Zomaya
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引用次数: 56

摘要

21世纪初,一种新的流线型数据处理模式引起了人们的研究兴趣,当今市场上的海量数据已经无法按照传统的方式存储和处理数据。数据流处理(DSP)是一种数据计算范式,它能够实时处理连续的数据流,而不是保持数据流之间的静态关系。在这个模型中,大量的原始数据元组以快速、连续和流的方式进入生态系统。这样的流集在大小上是无界的,而数据到达时间和数据处理时间具有在线的性质。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Big Data stream processing
At the beginning of twenty-first century, the research interest of a new model of streamlined data processing has been arising, involving a huge volume of data in today's market that makes it impossible to store and process data along with the traditional way. Data stream processing (DSP) is a data computational paradigm that enables the real-time processing of continuous data streams instead of maintaining the static relationship among them. In this model, a large volume of raw tuple of data enters in a rapid, continuous, and streaming manner to the ecosystem. Such a set of streams is unbounded in size, while the data arrival time and data processing time have an online nature.
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