A Unification of Heterogeneous Data Sources into a Graph Model in E-commerce

Abstract

The incorporation of heterogeneous data models into large-scale e-commerce applications incurs various complexities and overheads, such as redundancy of data, maintenance of different data models, and communication among different models for query processing. Graphs have emerged as data modelling techniques for large-scale applications with heterogeneous, schemaless, and relationship-centric data. Models exist for mapping different types of data to a graph; however, the unification of data from heterogeneous source models into a graph model has not received much attention. To address this, we propose a new framework in this study. The proposed framework first transforms data from various source models into graph models individually and then unifies them into a single graph. To justify the applicability of the proposed framework in e-commerce applications, we analyse and compare query performance, scalability, and database size of the unified graph with heterogeneous source data models for a predefined set of queries. We also access some qualitative measures, such as flexibility, completeness, consistency, and maturity for the proposed unified graph. Based on the experimental results, the unified graph outperforms heterogeneous source models for query performance and scalability; however, it falls behind for database size.

Memory-Enhanced Transformer for Representation Learning on Temporal Heterogeneous Graphs

“…However, most graphs in the real world are naturally heterogeneous and dynamic, which cannot be accurately represented by static homogeneous graphs. Several studies incorporate heterogeneous data models into a unified graph model [8], promoting the research of graph data. Taking the example of a user-item interaction network in e-commerce scenarios, illustrated in Fig.…”