LAUSR

Knowledge discovery is an emerging topic in many domains addressing a variety of methodologies for extracting useful knowledge from data. In an era of explosive data growth, together with wide-spreading powerful distributive and parallel computing, we are faced with an urgent demand for research and development of more efficient, effective and smart methodologies. On the other hand, it is also crucially challenging to extract, summarize, and even generate knowledge due to the large-scale, noisy, heterogeneous nature of big data. To this end, significant efforts have been reported in the literature on social networks, computer vision, data science, machine learning, data mining, statistical analysis, and fast computing. A number of successful models have recently emerged and led to great impact in the field. Interestingly, despite the diverse research topics and applications, these works recognize that cognitivelyinspired mechanisms should be investigated in order to make the algorithms more intelligent, powerful, and effective in extracting insightful knowledge, from huge amounts of heterogeneous Big data.