Towards Efficient Path Query on Social Network with Hybrid RDF Management

TL;DR

This paper proposes a hybrid RDF data management framework combining in-memory and disk-based techniques to improve the efficiency of property path queries in social networks, addressing performance issues with large, complex data.

Contribution

It introduces a novel hybrid framework with an in-memory algebra operator for property path queries and a cost estimation method for optimization, enhancing query performance.

Findings

Achieves a good balance between data load and query performance.

Demonstrates effectiveness on benchmark and real datasets.

Improves scalability for large social network data.

Abstract

The scalability and exibility of Resource Description Framework(RDF) model make it ideally suited for representing online social networks(OSN). One basic operation in OSN is to find chains of relations,such as k-Hop friends. Property path query in SPARQL can express this type of operation, but its implementation suffers from performance problem considering the ever growing data size and complexity of OSN.In this paper, we present a main memory/disk based hybrid RDF data management framework for efficient property path query. In this hybrid framework, we realize an efficient in-memory algebra operator for property path query using graph traversal, and estimate the cost of this operator to cooperate with existing cost-based optimization. Experiments on benchmark and real dataset demonstrated that our approach can achieve a good tradeoff between data load expense and online query performance.