Gábor Szárnyas

IncQuery-D: A Distributed Incremental Model Query Framework in the Cloud

Queries are the foundations of data intensive applications. In model-driven software engineering (MDE), model queries are core technologies of tools and transformations. As software models are rapidly increasing in size and complexity, traditional tools exhibit scalability issues that decrease productivity and increase costs [17]. While scalability is a hot topic in the database community and recent NoSQL efforts have partially addressed many shortcomings, this happened at the cost of sacrificing the ad-hoc query capabilities of SQL. Unfortunately, this is a critical problem for MDE applications due to their inherent workload complexity. In this paper, we aim to address both the scalability and ad-hoc querying challenges by adapting incremental graph search techniques – known from the EMF-IncQuery framework – to a distributed cloud infrastructure. We propose a novel architecture for distributed and incremental queries, and conduct experiments to demonstrate that IncQuery-D, our prototype system, can scale up from a single workstation to a cluster that can handle very large models and complex incremental queries efficiently.

IncQuery-D: incremental graph search in the cloud

Queries are the foundations of data intensive applications. In model-driven software engineering (MDE), model queries are core technologies of tools and transformations. As software models are rapidly increasing in size and complexity, traditional MDE tools frequently exhibit scalability issues that decrease productivity and increase costs. While such scalability challenges are a constantly hot topic in the database community and recent efforts of the NoSQL movement have partially addressed many shortcomings, this happened at the cost of sacrificing the powerful ad-hoc query capabilities of SQL. Unfortunately, this is a critical problem for MDE applications, as their queries can be significantly more complex than in general database applications. In this paper, we aim to address this challenge by adapting incremental graph search techniques -- known from the EMF-IncQuery framework -- to the distributed cloud infrastructure. IncQuery-D, our prototype system can scale up from a single-node tool to a cluster of nodes that can handle very large models and complex queries efficiently. The feasibility of our approach is supported by early experimental results.

The Train Benchmark: cross-technology performance evaluation of continuous model queries

In model-driven development of safety-critical systems (like automotive, avionics or railways), well-formedness of models is repeatedly validated in order to detect design flaws as early as possible. In many industrial tools, validation rules are still often implemented by a large amount of imperative model traversal code which makes those rule implementations complicated and hard to maintain. Additionally, as models are rapidly increasing in size and complexity, efficient execution of validation rules is challenging for the currently available tools. Checking well-formedness constraints can be captured by declarative queries over graph models, while model update operations can be specified as model transformations. This paper presents a benchmark for systematically assessing the scalability of validating and revalidating well-formedness constraints over large graph models. The benchmark defines well-formedness validation scenarios in the railway domain: a metamodel, an instance model generator and a set of well-formedness constraints captured by queries, fault injection and repair operations (imitating the work of systems engineers by model transformations). The benchmark focuses on the performance of query evaluation, i.e. its execution time and memory consumption, with a particular emphasis on reevaluation. We demonstrate that the benchmark can be adopted to various technologies and query engines, including modeling tools; relational, graph and semantic databases. The Train Benchmark is available as an open-source project with continuous builds from https://github.com/FTSRG/trainbenchmark.

Towards the Automated Generation of Consistent, Diverse, Scalable and Realistic Graph Models

Automated model generation can be highly beneficial for various application scenarios including software tool certification, validation of cyber-physical systems or benchmarking graph databases to avoid tedious manual synthesis of models. In the paper, we present a long-term research challenge how to generate graph models specific to a domain which are consistent, diverse, scalable and realistic at the same time.

Formalising openCypher Graph Queries in Relational Algebra

Graph database systems are increasingly adapted for storing and processing heterogeneous network-like datasets. However, due to the novelty of such systems, no standard data model or query language has yet emerged. Consequently, migrating datasets or applications even between related technologies often requires a large amount of manual work or ad-hoc solutions, thus subjecting the users to the possibility of vendor lock-in. To avoid this threat, vendors are working on supporting existing standard languages (e.g. SQL) or standardising languages.

Towards the characterization of realistic models: evaluation of multidisciplinary graph metrics

Custom generators of graph-based models are used in MDE for many purposes such as functional testing and performance benchmarking of modeling environments to ensure the correctness and scalability of tools. However, while existing generators may generate large models in increasing size, these models are claimed to be simple and synthetic, which hinders their credibility for industrial and research benchmarking purposes. But how to characterize a realistic model used in software and systems engineering? This question is investigated in the paper by collecting over 17 different widely used graph metrics taken from other disciplines (e.g. network theory) and evaluating them on 83 instance models originating from six modeling domains. Our preliminary results show that certain metrics are similar within a domain, but differ greatly between domains, which makes them suitable input for future instance model generators to derive more realistic models.

Model-Driven Engineering of an OpenCypher Engine: Using Graph Queries to Compile Graph Queries

Graph database systems are increasingly adapted for storing and processing heterogeneous network-like datasets. Many challenging applications with near real-time requirements—such as financial fraud detection, on-the-fly model validation and root cause analysis—can be formalised as graph problems and tackled with graph databases efficiently. However, as no standard graph query language has yet emerged, users are subjected to the possibility of vendor lock-in.

Incremental View Maintenance for Property Graph Queries

Graph processing challenges are common in modern database systems, with the property graph data model gaining widespread adoption. Due to the novelty of the field, graph databases and frameworks typically provide their own query language, such as Cypher for Neo4j, Gremlin for TinkerPop and GraphScript for SAP HANA. These languages often lack a formal background for their data model and semantics. To address this, the openCypher initiative aims to standardise a subset of the Cypher language, for which it currently provides grammar specification and a set of acceptance tests to allow vendors to implement their openCypher compatible engine.