Recursive graph queries are increasingly popular for extracting information from interconnected data found in various domains such as social networks, life sciences, and business analytics. Graph data often come with schema information that describe how nodes and edges are organized. We propose a type inference mechanism that enriches recursive graph queries with relevant structural information contained in a graph schema. We show that this schema information can be useful in order to improve the performance when evaluating recursive graph queries. Furthermore, we prove that the proposed method is sound and complete, ensuring that the semantics of the query is preserved during the schema-enrichment process.

Abstract We must explicitly capture relationships and hierarchies between the multitude of system and security standards requirements. Current security requirements specification methods do not capture such structure effectively, making requirements management and traceability harder, consequently increasing costs and time to market for developing certified ICS. We propose a novel requirements repository model for ICS that uses labelled property graphs to structure and store system-specific and standards-based requirements using well-defined relationship types. Furthermore, we integrate the proposed requirements repository with design-time ICS tools to establish requirements traceability. A wind turbine case study illustrates the overall workflow in our framework. We demonstrate that a robust requirements traceability matrix is a natural consequence of using labelled property graphs. We also introduce a compatible requirements change management procedure that aids in adapting to changes in development and certification schemes.

Abstract Contemporary labeled property graph databases are either schema-less or schema-optional to support frequent changes in the structure of data found in domains requiring high flexibility. However, the lack of structure impacts data transformation and loading operations from heterogeneous sources into graph databases. We present a formal algebra for specifying and generating graph schema for labeled property graph databases. We formally define and demonstrate the use of generated graph schemas to systematically transform and load data-sets related to domains of cyber-physical systems, big data analytics and tourism. Findings from three disparate case studies show that -generated schemas assist in enforcing integrity constraints that reduce the chance of data corruption, hence assuring data consistency and integrity.

With the influx of Web 3.0 the focus in Big Data Analytics has shifted towards modelling highly interconnected data and analysing relationships between them. Graph databases befit the requirements of Big Data Analytics yet organizations still depend on relational databases. A major roadblock in the industry wide adoption of graph databases is that a standard query language is still in its inception stage hence withholding interoperability between the two technologies. In this research we propose a tool FLUX for translating relational database queries to graph database queries.

Graph databases provide better support for highly interconnected datasets than relational databases. However, labeled property graph databases, which have become increasingly popular, are schema-optional, making them prone to data corruption, especially when new users switch from relational databases to graph databases. In this work, we provide a schema-driven formalism for graph databases. This formalism enables schema-driven loading of graph databases from other sources, such as relational databases. Also, this formalism enables schema-driven data analytics that allows for a more structured analysis of data stored in graph databases. Such analytics are based on a boilerplate approach allowing users who are not experts in the use of graph database query languages to carry out analytics efficiently. We showcase the utility of the proposed formalism by considering a case study from Airbnb for illustrating schema-based loading procedures. The proposed schema-driven analytics process is illustrated using another case study from an industrial cyber-physical systems standard. Overall, the schema-driven formalism provides several useful features, such as preventing both data corruption and long-term degradation of graph database structures.

The ongoing fourth Industrial Revolution depends mainly on robust Industrial Cyber-Physical Systems (ICPS). ICPS includes computing (software and hardware) abilities to control complex physical processes in distributed industrial environments. Industrial agents, originating from the well-established multi-agent systems field, provide complex and cooperative control mechanisms at the software level, allowing us to develop larger and more feature-rich ICPS. The IEEE P2660.1 standardisation project, "Recommended Practices on Industrial Agents: Integration of Software Agents and Low Level Automation Functions" focuses on identifying Industrial Agent practices that can benefit ICPS systems of the future. A key problem within this project is identifying the best-fit industrial agent practices for a given ICPS. This paper reports on the design and development of a tool to address this challenge. This tool, called IASelect, is built using graph databases and provides the ability to flexibly and visually query a growing repository of industrial agent practices relevant to ICPS. IASelect includes a front-end that allows industry practitioners to interactively identify best-fit practices without having to write manual queries.