chameleon-db: a workload-aware robust RDF data management system

The Resource Description Framework (RDF) is a World Wide Web Consortium (W3C) standard for the conceptual modeling of web resources, and SPARQL is the standard query language for RDF. As RDF is becoming more widely utilized, RDF data management systems are being exposed to workloads that are much more diverse and dynamic than they were designed to support, for which they are unable to provide consistently good performance. The problem arises because these systems are workload-agnostic; that is, they rely on a database structure and types of indexes that are fixed a priori, which cannot be modified at runtime. In this paper, we introduce chameleon-db, which is a work-load-aware RDF data management system that we have de-veloped. chameleon-db automatically and periodically ad-justs its layout of the RDF database to optimize for queries so that they can be executed efficiently. Since one cannot afford to stop processing queries, we propose a novel de-sign that enables partitions to be concurrently updated. We demonstrate that chameleon-db can achieve robust perfor-mance across a diverse spectrum of query workloads, out-performing its competitors by up to 2 orders of magnitude, and that it can easily adapt to changing workloads. 1