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.
Highlights
The era of data modelling began with relational models representing data in the form of relations and interconnections among them using foreign keys [9, 32]
We propose a framework for the unification of data from heterogeneous source models into a graph model, which consists of three steps: (i) data source identification, (ii) schema and data conversion, and (iii) schema and data unification
We propose an algorithm for schema conversion from an ontology model into a graph model based on guidelines discussed in [16]
Summary
The era of data modelling began with relational models representing data in the form of relations and interconnections among them using foreign keys [9, 32]. The relational model is the most widely accepted data model owing to various features, such as normalisation, integrity, and consistency [5, 9] This model is not suitable for applications requiring flexible schema and a higher number of joins for query processing [2, 21, 33]. To overcome such disadvantages of the relational model, various other not-only-structured-query-language (NoSQL) models have been proposed (e.g. key–value models, document models, graph models, etc.). We propose a framework for the unification of data from heterogeneous source models into a graph model.
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