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Data Streams Processing Techniques Data Streams Processing Techniques

Data Streams Processing Techniques Data Streams Processing Techniques
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Author(s): Fatma Mohamed (Ain Shams University, Egypt), Rasha M. Ismail (Ain Shams University, Egypt), Nagwa. L. Badr (Ain Shams University, Egypt)and Mohamed F. Tolba (Ain Shams University, Egypt)
Copyright: 2017
Pages: 25
Source title: Handbook of Research on Machine Learning Innovations and Trends
Source Author(s)/Editor(s): Aboul Ella Hassanien (Cairo University, Egypt)and Tarek Gaber (Suez Canal University, Egypt)
DOI: 10.4018/978-1-5225-2229-4.ch015

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Abstract

Many modern applications in several domains such as sensor networks, financial applications, web logs and click-streams operate on continuous, unbounded, rapid, time-varying streams of data elements. These applications present new challenges that are not addressed by traditional data management techniques. For the query processing of continuous data streams, we consider in particular continuous queries which are evaluated continuously as data streams continue to arrive. The answer to a continuous query is produced over time, always reflecting the stream data seen so far. One of the most critical requirements of stream processing is fast processing. So, parallel and distributed processing would be good solutions. This paper gives (1) analysis to the different continuous query processing techniques; (2) a comparative study for the data streams execution environments; and (3) finally, we propose an integrated system for processing data streams based on cloud computing which apply continuous query optimization technique on cloud environment.

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