When data stored in the database is shown in a graph structure composed of nodes and edges,
the overall meaning of the connected patterns become easier to understand.
Broadening the understanding and perspective of data may help with efficient data management throughout a vast network.
Graph Vision®
CASE STUDIES
Data Provenance in Ship Manufacturing
As a ship manufacturing company, DSME has a data platform that keeps track of manufacturing history. DSME was determined to improve data management and search services using AgensGraph, while also maintaining its existing relational database for storing data. The multi-model functionality of AgensGraph was more than enough to utilize the advantages of both the relational and graph databases. Thus, Bitnine developed a fast, analytical knowledge platform that made it easier to analyze relationships and impacts between data.
Failure Analysis & Monitoring System in Power Network
Swiftly identifying a problematic device has been one of the key challenges in the energy industry. In order to precisely locate a failed device among webs of devices, one should be able to monitor the entire devices in the network and quickly monitor their status in real-time. Visualizing and pinpointing a faulty device became possible thanks to graph technology provided via AgensGraph. Company D was able to save time & manpower by placing the right management resources at the right time.
Graph-based HR Communication Management System
Introducing G-PAS CMS, an HR solution with a predictive analytics engine. G-PAS CMS ultimately improves business performance by analyzing communication between workers within the company. The biggest advantage of graph analytics is known for its convenience in accessing insights via a dashboard. Through G-PAS CMS, the work status of a new employee can be monitored, employees with high workloads can be tracked, silos within the organization can be explored, and insights on an employee with various capabilities can be acquired.
Predicting Power Failure with Graph-based Digital Twin
In order to minimize quantitative damage to power transmission and substation facilities, it was essential for the power company to accurately determine the failure and respond quickly to normalize the damage. Since the graph database is optimized to express the data structure of the real world, it could express complex networks between power transmission and substation facilities and provide fast computational performance that can be applied in real-time.
Analyzing Cross Holding with Graph DB
This was a case of graph DB showing a clearer understanding of ownership shares of certain car manufacturing company. By applying classification and clustering algorithms, we were able to connect the dots of shares owned within the various groups of the company. In a real-life business environment, there would be more complicated information than the ownership shares within the group of companies, but this was just a sample of what graph technology is capable of.
