Automated analysis can sometimes lead to surprising results, in which case systems engineers would be justified to question whether or not the big data, and the automated analysis, is accurate. Everyone has heard the old adage, “garbage in, garbage out,” but the reality is that ensuring the quality and integrity of big data is not particularly easy. In fact, the sheer quantity of data churned out by thousands and thousands of sensors can put a tremendous load on legacy data acquisition methods. In addition, with wireless technology quickly becoming the connection option of choice for IIoT applications, mainly due to its convenience and mobility, the stability of wireless communications is a critical issue. A cause for concern is the inevitable unexpected connection interruptions that plague any wireless network, and which could result in data loss and expensive shutdowns of important business processes. Moxa’s smart data acquisition method helps to shrink the amount of data that needs to be transmitted and ensure data completeness. In short, smart data acquisition enhances the quality and integrity of big data, resulting in more accurate analyses.
When used together, Moxa’s ioLogik 2500 series, MX-AOPC UA Server, and MX-AOPC UA Logger form a turnkey solution that provides real-time data acquisition, data buffering in local storage devices, and automatic data completeness after network failures. MX-AOPC UA Logger imports data from MX-AOPC UA Server into a database in real time. When the network fails and then recovers, the logger automatically retrieves data logs, with timestamp matching the duration of the disconnection, from the data buffers of specific ioLogik 2500 devices, and then pushes the supplementary data into the database.
About ten years ago, Moxa introduced its patented Active OPC concept, which is implemented by Moxa’s ioLogik products. The ioLogik can poll local meters and sensors as frequently as it likes without putting any burden on the Ethernet network, and only sends readings to the OPC server (over the Ethernet network) when certain pre-configured conditions are met. Engineers can decide between updating data by polling and updating data by exception for efficient data collection. With this efficient data collection method, MX-AOPC UA Logger can deploy higher quality data to the private or public cloud for big data analysis.