Building self-clustering RDF databases using Tunable-LSH

The Resource Description Framework (RDF) is a W3C standard for representing graph-structured data, and SPARQL is the standard query language for RDF. Recent advances in information extraction, linked data management and the Semantic Web have led to a rapid increase in both the volume and the variety of RDF data that are publicly available. As businesses start to capitalize on RDF data, RDF data management systems are being exposed to workloads that are far more diverse and dynamic than what they were designed to handle. Consequently, there is a growing need for developing workload-adaptive and self-tuning RDF data management systems. To realize this objective, we introduce a fast and efficient method for dynamically clustering records in an RDF data management system. Specifically, we assume nothing about the workload upfront, but as SPARQL queries are executed, we keep track of records that are co-accessed by the queries in the workload and physically cluster them. To decide dynamically and in constant-time where a record needs to be placed in the storage system, we develop a new locality-sensitive hashing (LSH) scheme, Tunable-LSH. Using Tunable-LSH, records that are co-accessed across similar sets of queries can be hashed to the same or nearby physical pages in the storage system. What sets Tunable-LSH apart from existing LSH schemes is that it can auto-tune to achieve the aforementioned clustering objective with high accuracy even when the workloads change. Experimental evaluation of Tunable-LSH in an RDF data management system as well as in a standalone hashtable shows end-to-end performance gains over existing solutions.