What is the common challenge in detecting frauds in financial transactions, analyzing source code repositories and performing runtime verification on cyber-physical systems? These applications operate on large, continuously changing graphs and use complex queries, but also require quick response times. However, these queries are usually known in advance, so a smart query engine can rely on previous results and only calculate the differences of the last change.
ingraph is a query engine for continuous and live queries. It allows users to register graph queries, precompute their results and evaluate changes in milliseconds.
Gábor Szárnyas is a researcher working on graph processing techniques. His core research areas include incremental graph pattern matching, benchmarking graph transformations, and analyzing large-scale networks.
Gábor works at the Budapest University of Technology and Economics, teaching system modelling and database theory. He visited McGill University as a graduate research trainee and is currently a member of the openCypher Implementers Group. He received 1st prize in the ACM Student Research Competition at the MODELS 2016 conference.