CF-RDF: A Lightweight and Efficient Large Scale RDF Data Management System

Abstract

As more and more scholars focus on cognitive intelligence, knowledge graph has been widely concerned. Because knowledge graph uses RDF to describe various resources, the storage and management of massive RDF data becomes a challenge. Existing methods are mainly based on relational database, RDF triples and graph model to store and query RDF data [1]. The method based on RDF triples is to build index according to (subject, predict, object) model. It is characterized by building multiple indexes to speed up the query, but it brings a great burden to the database storage. For example, RDF-3X constructs six three-dimensional indexes, six two-dimensional indexes and three one-dimensional indexes, uses a lot of storage space. At the same time, it uses the mapping dictionary to compress the storage space, which will slow down the query speed.To address the above issues, this paper proposes a lightweight and efficient method to store and query RDF data. Instead of mapping RDF data into a relational database or building an index based on RDF triples as most methods do, we develop a new index based on the Cuckoo Filter. In addition, the method of data block storage is adopted, which not only facilitates the storage management, but also makes the storage structure compact, and speeds up the data reading. At the same time, in order to ensure the uniqueness of the data in the index, we use the method of fingerprint value. Through comparative experiments on three large datasets, it is proved that our method improves the query performance by 450% over RDF-3X and 64% over gStore.