Dataflow model for cloud computing frameworks in big data

Dong Dai, Yong Chen, Gangyong Jia

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

In recent years, the Big Data challenge has attracted increasing attention [1-4]. Compared with traditional data-intensive applications [5-10], these “Big Data” applications tend to be more diverse: they not only need to process the potential large data sets but also need to react to real-time updates of data sets and provide low-latency interactive access to the latest analytic results. A recent study [11] exemplifies a typical formation of these 4applications: computation/processing will be performed on both newly arrived data and historical data simultaneously and support queries on recent results. Such applications are becoming more and more common; for example, real-time tweets published on Twitter [12] need to be analyzed in real time for finding users’ community structure [13], which is needed for recommendation services and target promotions/advertisements. The transactions, ratings, and click streams collected in real time from users of online retailers like Amazon [14] or eBay [15] also need to be analyzed in a timely manner to improve the back-end recommendation system for better predictive analysis constantly.

Original languageEnglish
Title of host publicationHigh Performance Computing for Big Data
Subtitle of host publicationMethodologies and Applications
PublisherCRC Press
Pages3-18
Number of pages16
ISBN (Electronic)9781498784009
ISBN (Print)9781498783996
DOIs
StatePublished - Jan 1 2017

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    Dai, D., Chen, Y., & Jia, G. (2017). Dataflow model for cloud computing frameworks in big data. In High Performance Computing for Big Data: Methodologies and Applications (pp. 3-18). CRC Press. https://doi.org/10.1201/b21235