Big data classification method of non relational distributed submission information under differentiated requirements - Lu HAN; Weiyu CHEN; Fei ZHANG; Jianfeng HE; Huaizhen SU (Dianxin kexue)
The classification method of non-relational distributed submitted information big data under the differentiated demand was studied, aiming at the problem of multi-source heterogeneous, widely distributed submitted information with more differentiated application requirements and inability to distinguish the available information.Firstly, the usability, openness and expansibility of the non-relational distributed submission information database were analyzed.The unstructured database storage TRIP was used to store non-relational distributed submission information by combining the basic requirements of field types.Then, the hashing process within the Hamming hash family was analyzed.Under the constraint of linearity level requirements, cellular automata with multiple attractors were used to optimize the system.The optimal parameters of the multiple attractor cellular automata classifier were improved through genetic algorithm, thus improving the big data classification method.Experimental results show that this method can effectively identify and classify structured data and unstructured data in non relational distributed submission information, and has high classification accuracy.