Encoding Feature Models in Neo4j Graph Database

Abstract

This study introduces an innovative approach to encoding and analyzing feature models within the Network Exploration and Optimization for Java (Neo4j) graph database, significantly enhancing the management of complex Software Product Lines (SPLs). We present a comparative analysis of traditional loading techniques against Neo4j's batch importer and the Awesome Procedures on Cypher (APOC) library, demonstrating the superior efficiency and effectiveness of our proposed methods, especially in handling large datasets. Our methodology extends beyond mere encoding; it capitalizes on Neo4j's Graph Data Science (GDS) library, employing Depth-First Search (DFS) and other advanced traversal techniques to navigate and manipulate these complex structures. The findings reveal not only a significant enhancement in the processing and analysis of feature models but also underscore the potential for more sophisticated SPL management strategies. By integrating innovative loading techniques, encoding strategies, and GDS traversal methods, this study lays a robust foundation for future advancements in the field.

Department(s)

Computer Science

Document Type

Conference Proceeding

DOI

10.1145/3603287.3651199

Keywords

Cypher, data science, feature model, graph data science library, graph traversals, load data in Neo4j, performance measurement

Publication Date

4-18-2024

Journal Title

Proceedings of the 2024 ACM Southeast Conference Acmse 2024

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