Compressed Indexes for Fast Search of Semantic Data
This work addresses the need for scalable semantic data processing, offering significant improvements for database and knowledge graph applications, though it is incremental in optimizing existing indexing approaches.
The paper tackles the problem of efficiently indexing RDF data for fast SPARQL query resolution by proposing a trie-based index layout with novel compression techniques, resulting in a 30-60% space reduction and query speed-ups of 2-81x compared to state-of-the-art methods.
The sheer increase in volume of RDF data demands efficient solutions for the triple indexing problem, that is devising a compressed data structure to compactly represent RDF triples by guaranteeing, at the same time, fast pattern matching operations. This problem lies at the heart of delivering good practical performance for the resolution of complex SPARQL queries on large RDF datasets. In this work, we propose a trie-based index layout to solve the problem and introduce two novel techniques to reduce its space of representation for improved effectiveness. The extensive experimental analysis conducted over a wide range of publicly available real-world datasets, reveals that our best space/time trade-off configuration substantially outperforms existing solutions at the state-of-the-art, by taking 30-60% less space and speeding up query execution by a factor of 2-81x.