Metaknowledge Templates for On-the-Fly Clustering of Big Data Streams

Summary form only given. This poster paper proposes a novel processing technique to optimize on-the-fly clusterization of disorganized and unclassified Big data from a vast number of sources. The technique is based on the fuzzy logic using fault-tolerant indexing with error-correction codes. This research aims to introduce a processing template for this demanding "Big Data" processing methodology -- clustering of amorphous data items in data stream mode. As researchers, we are currently investigating a novel approach for the development of meta-language templates in the form of 23-bit questions. The presented approach is based on the previously developed construction of FuzzyFind Dictionary utilizing the error-correcting Golay Code. Realization of this technique requires processing of intensive continuous data streams, which can be effectively implemented using multi-core pipelining with forced interrupts. The objective of this research is to bring forward a new novel simple and efficient tool for one of the most demanding operations of this "Big Data" clusterization of amorphous data from diverse sources.