Knowledge extracting from big textual datasets

Abstract

Generally, data mining seeks to uncover, examine, and analyse significant information from large data sources using
various techniques and algorithms. However, with big data, processing and extracting knowledge using traditional methods
and algorithms presents substantial challenges. Knowledge mining from big data involves extracting insights and patterns from
vast data sets, utilizing the lambda architecture and the parallel processing paradigm of big data. The article discusses a new
approach to big data mining. The originality of this work is based on the representation of knowledge in the form of a graph
model, as well as the assembling of a single knowledge model from individual graph fragments of knowledge. Combining these
two concepts within the context of lambda architecture enables efficient execution of large-scale mining tasks in parallel
processing of big data.