In the last years we have seen an explosion of massive amounts of graph shaped data coming from a variety of applications that are related to social networks like facebook, twitter, blogs and other on-line media and telecommunication networks. Around such data and their applications, there is strongly increasing interest in native Graph Database solutions, that are increasing being adoped.
Furthermore, the W3C linking open data initiative has boosted the publication and interlinkage of a large number of datasets on the semantic web resulting to the Linked Data Cloud. These datasets with billions of RDF triples such as Wikipedia, U.S. Census bureau, CIA World Factbook, DBPedia, and government sites have been created and published online. Moreover, numerous datasets and vocabularies from e-science are published nowadays as RDF graphs most notably in life and earth sciences, astronomy in order to facilitate community annotation and interlinkage of both scientific and scholarly data of interest.
Given the abundance of new products and technologies in this space, it is difficult for IT practitioners to compare the different products, among each other, and with existing relational database technologies. This new data management paradigm also provides an opportunity for research results to impact young innovative companies working on RDF and graph data management to start playing a significant role in this new data economy.
The Linked Data Benchmark Council (LDBC) aims to establish industry cooperation between vendors of RDF and Graph database technologies in developing, endorsing, and publishing reliable and insightful benchmark results.
For this purpose, an initial set of database technology vendors have teamed up with leading database and semantic web researchers to create a benchmark council, its organizational structure, its benchmarking auditing mechanisms, and an initial set of benchmarks.
This is timely and urgent since non-relational data management is emerging as a critical need for the new data economy based on large, distributed, heterogeneous, and complexly structured data sets. In order to establish a Graph and RDF technologies in the ecosystem, their properties need to be widely understood and benchmarks will help herein. Further such industry accepted benchmarks are likely to stimulate and accelerate technical progress in this field.