Abstract
Big Data is an emerged architecture and technology paradigm that is used by many organizations to extract valuable information either to take decisions. Big Data is a technique and method used to retrieve, collect, process and analyze a very big volume of unstructured and structured data. The challenge is processing and analyzing the huge volume of data coming in from network sensors. Practically, it’s too late to stop an abnormal comportment, if we collect the incoming streams and wait for many days for processing and analyzing the stored streams. Big Data in video surveillance systems, offer ETL (Extract Transform and Load) challenges related to the Van Newman Bottleneck and Data Locality. In this chapter we propose a conceptual model with architectural elements and proposed tools for monitoring in RTAP (Real Time Analytical Processing) mode smart areas.
Our model is based on lambda architecture, in order to resolve the problem of latency which is imposed in transactional requests (GAB Network). We consider the real example that data comes from different sources (Automatic monitoring Centers, GAB, Facebook, Twitter, Instagram, LinkedIn, Medical Centers, Commercial Centers and any other data collected by satellites.) which is n dimension and we which to reduce with PCA algorithm the number of components to reduce the processing time and increase the speed of execution.
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Ezzahout, A., Oubaha, J. (2019). The Big Data-RTAP: Toward a Secured Video Surveillance System in Smart Environment. In: Zbakh, M., Essaaidi, M., Manneback, P., Rong, C. (eds) Cloud Computing and Big Data: Technologies, Applications and Security. CloudTech 2017. Lecture Notes in Networks and Systems, vol 49. Springer, Cham. https://doi.org/10.1007/978-3-319-97719-5_10
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DOI: https://doi.org/10.1007/978-3-319-97719-5_10
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