  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.
