Abstract
Database management and monitoring is an inseparable part of any industry. A uniform scheme of monitoring relational databases without explicit user access to database servers is not much explored outside the database environment. In this paper, we present an information distribution scheme related to databases using Open Platform Communication Unified Architecture (OPC UA) servers to clients when multiple databases are involved in a factory. The aim is for external, but relevant clients, to be able to monitor this information mesh independent of explicit access to user schemas. A methodology to dispense data from, as well as check changes in databases using SQL queries and events is outlined and implemented using OPC UA servers. The structure can be used as a remote viewing application for multiple databases in one address space of an OPC UA server.
Highlights
Data management and distribution in an industry is one of the important pillars of smart manufacturing
Using aggregation dynamics provided by Open Platform Communication Unified Architecture (OPC UA) specifications (Grosmann et al 2014), multiple address spaces can be pooled into one common space for group view of data contained in OPC UA transactions
Since the objective is monitoring of databases, the database meta-data which exposes the description of incorporated tables along with columns is transferred to the server address space in the test database representation
Summary
Data management and distribution in an industry is one of the important pillars of smart manufacturing. In contrast to it, where the design of a production network is such that machine data is directly stored into databases, there is a need to monitor these in real time with possible diagnostic capability. To monitor such a group of relational databases where intricate knowledge or access to it is not required, this paper presents an OPC UA based database management. This includes the address space node structure when multiple databases are involved, particular event management in servers as well as aggregations over such huge amounts of data. The final sections disclose the observations from such an implementation with advantages as well as scope for improvements
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