Tackling Challenges in Implementing Large-Scale Graph Databases

Graph databases (GDBs)13,30 have gained momentum with the rise of large unstructured repositories of information that emphasize relations between entities. Dozens of GDB management systems,8,22,25,31 prototypes,1,2,15,21 models and languages,3,10,12,14 large knowledge graphs like Wikidata,33 and efforts from companies like Apache, Facebook, Google, Microsoft, Neo4j, and Oracle, illustrate the growing interest in this technology. While the expressive power and flexibility of their data model and query languages is the key to their success, the efficiency challenges posed by their implementation is the main obstacle to the wider adoption of GDBs. Latin America has a long-standing tradition in fundamental research areas like database theory, string processing, information retrieval, and the design and analysis of algorithms and data structures--all of which are relevant for the development of GDBs. In the last few years, several researchers in Chile started collaborating on algorithms and systems for evaluating complex queries on large-scale GDBs.