Real-Time Stream Processing in Social Networks with RAM 3 S

Ilaria Bartolini and Marco Patella
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Ilaria Bartolini: DISI, University of Bologna, 40100 Bologna, Italy
Marco Patella: DISI, University of Bologna, 40100 Bologna, Italy

Future Internet, 2019, vol. 11, issue 12, 1-16

Abstract: The avalanche of (both user- and device-generated) multimedia data published in online social networks poses serious challenges to researchers seeking to analyze such data for many different tasks, like recommendation, event recognition, and so on. For some such tasks, the classical “batch” approach of big data analysis is not suitable, due to constraints of real-time or near-real-time processing. This led to the rise of stream processing big data platforms, like Storm and Flink, that are able to process data with a very low latency. However, this complicates the task of data analysis since any implementation has to deal with the technicalities of such platforms, like distributed processing, synchronization, node faults, etc. In this paper, we show how the RAM 3 S framework could be profitably used to easily implement a variety of applications (such as clothing recommendations, job suggestions, and alert generation for dangerous events), being independent of the particular stream processing big data platforms used. Indeed, by using RAM 3 S, researchers can concentrate on the development of their data analysis application, completely ignoring the details of the underlying platform.

Keywords: stream processing; social networks; big data (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2019
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