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Related Experiment Video

Updated: Jul 11, 2025

gP2S, an Information Management System for CryoEM Experiments

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Published on: June 10, 2021

A stream processing abstraction framework.

Ilaria Bartolini¹, Marco Patella¹

¹Department of Computer Science and Engineering (DISI), Alma Mater Studiorum, University of Bologna, Bologna, Italy.

Frontiers in Big Data

|November 13, 2023

Summary

This summary is machine-generated.

Big Data streaming platforms like Apache Flink are powerful but complex. The new SPAF framework simplifies real-time stream processing application development for Big Data analysis.

Keywords:

Big Data multimedia data streams real-time analysis software framework stream processing

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Area of Science:

Computer Science
Data Engineering

Background:

Big Data streaming platforms (e.g., Apache Flink, Samza) enable efficient real-time analysis of large multimedia streams.
Existing platforms offer raw functionalities, posing challenges for effective exploitation by analysts.

Purpose of the Study:

To describe the evolution of RAM3S into SPAF, an abstraction framework.
To provide a simple yet powerful API for developing stream processing applications.

Main Methods:

Evolution of RAM3S software infrastructure into the SPAF abstraction framework.
Development of a simplified API for stream processing application development.

Main Results:

SPAF offers an abstraction layer over Big Data stream processing platforms.
Programmers can easily implement real-time complex analyses on massive streams using SPAF.
The framework effectively manages the volume and velocity of Big Data streams.

Conclusions:

SPAF simplifies the development of real-time Big Data stream processing applications.
The framework empowers analysts to transform data into value by easing complex analysis.
SPAF enhances the usability of raw Big Data streaming platform functionalities.