Research Article Open Access

Towards a Generic Multimodal Architecture for Batch and Streaming Big Data Integration

Siham Yousfi1, Maryem Rhanoui1 and Dalila Chiadmi1
  • 1 Mohammed V University in Rabat, Morocco
Journal of Computer Science
Volume 15 No. 1, 2019, 207-220

DOI: https://doi.org/10.3844/jcssp.2019.207.220

Submitted On: 9 July 2018 Published On: 31 January 2019

How to Cite: Yousfi, S., Rhanoui, M. & Chiadmi, D. (2019). Towards a Generic Multimodal Architecture for Batch and Streaming Big Data Integration. Journal of Computer Science, 15(1), 207-220. https://doi.org/10.3844/jcssp.2019.207.220

Abstract

Big Data are rapidly produced from various heterogeneous data sources. They are of different types (text, image, video or audio) and have different levels of reliability and completeness. One of the most interesting architectures that deal with the large amount of emerging data at high velocity is called the lambda architecture. In fact, it combines two different processing layers namely batch and speed layers, each providing specific views of data while ensuring robustness, fast and scalable data processing. However, most papers dealing with the lambda architecture are focusing one single type of data generally produced by a single data source. Besides, the layers of the architecture are implemented independently, or, at best, are combined to perform basic processing without assessing either the data reliability or completeness. Therefore, inspired by the lambda architecture, we propose in this paper a generic multimodal architecture that combines both batch and streaming processing in order to build a complete, global and accurate insight in near-real-time based on the knowledge extracted from multiple heterogeneous Big Data sources. Our architecture uses batch processing to analyze the data structures and contents, build the learning models and calculate the reliability index of the involved sources, while the streaming processing uses the built-in models of the batch layer to immediately process incoming data and rapidly provide results. We validate our architecture in the context of urban traffic management systems in order to detect congestions.

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Keywords

  • Big Data Integration
  • Lambda Architecture
  • Heterogeneous Data
  • Urban Traffic Management Systems