Storage, Indexing, Query Processing, and Benchmarking in Centralized and Distributed RDF Engines: A Survey

TL;DR

This survey reviews recent developments in centralized and distributed RDF engines, focusing on storage, indexing, query processing, and benchmarking, highlighting their impact on performance and efficiency in Semantic Web applications.

Contribution

It provides a comprehensive overview of RDF engine components, comparing various approaches and their effects on query performance and system scalability.

Findings

Different storage strategies impact space and runtime.

Indexing methods are crucial for fast data retrieval.

Query execution optimization affects response times.

Abstract

The recent advancements of the Semantic Web and Linked Data have changed the working of the traditional web. There is significant adoption of the Resource Description Framework (RDF) format for saving of web-based data. This massive adoption has paved the way for the development of various centralized and distributed RDF processing engines. These engines employ various mechanisms to implement critical components of the query processing engines such as data storage, indexing, language support, and query execution. All these components govern how queries are executed and can have a substantial effect on the query runtime. For example, the storage of RDF data in various ways significantly affects the data storage space required and the query runtime performance. The type of indexing approach used in RDF engines is critical for fast data lookup. The type of the underlying querying language (e.g., SPARQL or SQL) used for query execution is a crucial optimization component of the RDF storage solutions. Finally, query execution involving different join orders significantly affects the query response time. This paper provides a comprehensive review of centralized and distributed RDF engines in terms of storage, indexing, language support, and query execution.