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.
Cloud computing has pioneered the area of On-demand services. The customer can choose the resources according to the current needs with the facility of incrementing and decrementing the resources in the future. It generally follows a pay-as-you-use model which has proven beneficial for enterprises and individual users alike. Since the services are hosted over the internet, one of the recent concerns that are rising among the users is about the location of their data. Sometimes it is necessary for the data to stay in a particular jurisdiction. Therefore, it may be required for the organization to verify the location of their data from time to time. Here in this paper we propose a mechanism based on remote attestation technology of trusted platform module. Remote attestation technique is used to validate the current location of the data, and the generated result is passed to the user/verifier. The very fact that the trusted platform module is tamper proof provides the basis for the accuracy of the result.
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.
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