A Self-Evolving Method of Data Model for Cloud-Based Machine Data Ingestion

In the case of a cloud-based remote control system such as SCADA (Supervisory Control and Data Acquisition) that enables users to collect data from cloud-connected machines deployed anywhere at any time. However, machine data models may not be updated in a timely manner after the devices are upgrades or modified. This leads to mismatches between the machine data and data models. A key obstacle of the matching is that the machines can be modified. To address this, we present a self-evolving method for machine data model. We give the description of the evolution of machine data models and the self-evolving method for the models in details. The method detects the conflicts between the machine data and models, and transfer or derive models if necessary. Our method can thus facilitate the evolution of machine data models and ensure that every machine in the cloud corresponds to the correct machine data model automatically. At last, we present two case studies to validate our method.