IASelect: Finding Best-fit Agent Practices in Industrial CPS Using Graph Databases

Abstract

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 frontend that allows industry practitioners to interactively identify best-fit practices without having to write manual queries.

Systematic Literature Review: Current Products, Topic, and Implementation of Graph Database

“…For social networks tends to the application: nodes are people or groups, whereas links show the relationships or flow between nodes. Some examples are friendships, business relationships, sexual contact patterns, research networks (collaboration, co-authorship), communication records (e-mail, phone calls, e-mail), computer networks, and national security [50,51]. This topic is highly developed in the subtopics of social network analysis or in visualization and data processing techniques for these networks.…”

“…The Graph Processing category includes studies comprising tools for graph data [22], how much scale of data, large data processing [27], [57]. The third category includes studies containing new approaches to the Graph Database, including papers related to graph query [13], graph base database model [6,7,51,58], and discussion about any tool or product of Graph Database is used. Our taxonomy was then structured, as shown in Fig.…”

Tracing security requirements in industrial control systems using graph databases

“…The repository enables querying of complete requirements by specifying graph patterns such as a tree structure, chain structure, chain-set structure and forest structures [43]. Furthermore, the result sets can be filtered based on requirement identifiers and properties associated with nodes and edges of an LPG [42,47]. In this article, we utilise the querying ability provided by graph database tool.…”

“…A relational query language like SQL may result in a longer response time when data is highly interconnected. Graph databases are efficient in storing and managing highly interconnected data [43,44,47]. Furthermore, relationships between data are calculated at the database creation stage; therefore, unlike relational query languages, the relationships are not calculated when data are retrieved from the database [43].…”

“…A labelled property graph database uses a graph structure for storing and managing data, allowing the modelling of real-world entities as nodes and edges [46]. Nodes are used to store data, and relationships between data are stored as edges [42,44,47]. In the LPG database, nodes and edges have labels associated with them.…”