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A general framework for real-time analysis of massive multimedia streams

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Abstract

Big Data platforms provide opportunities for the management and analysis of large quantities of information, but the services they provide are often too raw, since they focus on issues of fault-tolerance, increased parallelism, and so on. An additional software layer is, therefore, needed to effectively use such architectures for advanced applications in several important real-world domains, such as scientific and health care sensors, user-generated data, supply chain systems and financial companies, to name a few. In this paper, we present RAM\(^3\)S, a framework for the real-time analysis of massive multimedia streams, where data come from multiple data sources (such as sensors and cameras) that are widely located on the territory, with the final goal to discovery new and hidden information from the output of data sources as they occur, thus with very limited latency. We apply RAM\(^3\)S to the use case of automatic detection of “suspect” people from several concurrent video streams, and instantiate it on top of three different open source engines for the analysis of streaming Big Data (i.e., Apache Spark, Apache Storm, and Apache Flink). The effectiveness and scalability of RAM\(^3\)S instantiation is experimentally evaluated on real data, also comparing the performance of the three considered Big Data platforms. Such comparison is performed both on a cluster of physical machines in our datalab and on the Google Cloud Platform.

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Notes

  1. http://hadoop.apache.org.

  2. http://spark.apache.org.

  3. http://storm.apache.org.

  4. http://flink.apache.org.

  5. http://kafka.apache.org.

  6. http://openimaj.org.

  7. http://cloud.google.com.

  8. http://www-db.disi.unibo.it/research/datalab/.

  9. http://www.cs.tau.ac.il/~wolf/ytfaces/.

  10. http://benchmark.ini.rub.de.

  11. http://www.cvl.isy.liu.se/research/datasets/traffic-signs-dataset/.

  12. http://www.rabbitmq.com.

  13. http://www.nist.gov/programs-projects/face-video-evaluation-five.

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Acknowledgements

The authors thank the datalab students that participated in the software development of the RAM\(^3\)S framework.

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Authors and Affiliations

  1. DISI-Alma Mater Studiorum, Università di Bologna, Bologna, Italy

    Ilaria Bartolini & Marco Patella

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  1. Ilaria Bartolini
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  2. Marco Patella
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Correspondence to Ilaria Bartolini.

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Communicated by M. Wang.

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Bartolini, I., Patella, M. A general framework for real-time analysis of massive multimedia streams. Multimedia Systems 24, 391–406 (2018). https://doi.org/10.1007/s00530-017-0566-5

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