@article{MFA34ADF8,
title = "Squall: A Real-time Big Data Processing Framework based on TMO Model for Real-time Events and Micro-batch Processing",
journal = "Journal of KIISE, JOK",
year = "2017",
issn = "2383-630X",
doi = "",
author = "Jae Gi Son,Kim, Jung Guk",
keywords = "real-time big data,distributed real-time object,stream big data,big data processing",
abstract = "Recently, the importance of velocity, one of the characteristics of big data (5V: Volume, Variety, Velocity, Veracity, and Value), has been emphasized in the data processing, which has led to several studies on the real-time stream processing, a technology for quick and accurate processing and analyses of big data. In this paper, we propose a Squall framework using Time-triggered Message-triggered Object (TMO) technology, a model that is widely used for processing real-time big data. Moreover, we provide a description of Squall framework and its operations under a single node. TMO is an object model that supports the non-regular real-time processing method for certain conditions as well as regular periodic processing for certain amount of time. A Squall framework can support the real-time event stream of big data and micro-batch processing with outstanding performances, as compared to Apache storm and Spark Streaming. However, additional development for processing real-time stream under multiple nodes that is common under most frameworks is needed. In conclusion, the advantages of a TMO model can overcome the drawbacks of Apache storm or Spark Streaming in the processing of real-time big data. The TMO model has potential as a useful model in real-time big data processing."
}