Towards scalable querying of large-scale models

Hawk is a modular and scalable framework that supports monitoring and indexing large collections of models stored in diverse version control repositories. Due to the aggregate size of indexed models, providing a reliable, usable, and fast mechanism for querying Hawk's index is essential. This paper presents the integration of Hawk with an existing model querying language, discusses the efficiency challenges faced, and presents an approach based on the use of derived features and indexes as a means of improving the performance of particular classes of queries. The paper also reports on the evaluation of a prototype that implements the proposed approach against the Grabats benchmark query, focusing on the observed efficiency benefits in terms of query execution time. It also compares the size and resource use of the model index against one created without using such optimizations.