Abstract

Banks are collecting unprecedentedly large amount of data about their customers from difference sources, considering their cyber, physical, social activities. The focus of this paper is to study the problem of information sharing and lower the communication overhead among different nodes for a specific data mining approach in distributed big data architectures. This problem can be abstracted as how to efficiently search under a specific cluster node topology. This paper proposes a new design rule for topologies including 1) low coordination number, 2) high packing density, and 3) having a 3D structure. According to this rule, a Rhombic Dodecahedron topology is proposed. A distributed banking big data mining framework based on the proposed topology is implemented. The experiments based on multi-optimization benchmark functions show the excellent searching ability of the proposed topology; and a banking customer feature reduction prototype has been implemented to showcase the practicality of the data mining framework.

Keywords

• Financial Big Data
• Cyber-Physical-Social Systems
• Swarm Optimization
• Rhombic Dodecahedron

Bibtex

@article{hwang:TCSS19,
  title={A Rhombic Dodecahedron Topology for Human-Centric Banking Big Data},
  author={Hao Wang and Shenglan Ma and Hong-Ning Dai},
  journal={IEEE Transactions on Computational Social Systems},
  year={2019},
  volume={6},
  number={5},
  pages={1095 - 1105},
  doi={10.1109/TCSS.2019.2918193}
  publisher={IEEE},
}