MIAI: the AI Institute of Grenoble
The artificial intelligence EV Observe boosted by the experts of the AI Institute of Grenoble
Few teams in France are interested in the discovery of causal relationships in time series. Because of their work on this subject, EasyVista and the Grenoble Computer Science Laboratory have acquired a leading expertise in the field of causality.
WHY EV OBSERVE ?
The answer
The LIG research teams are interested in real production data to test and prove their algorithms. EasyVista with its EV Observe SaaS monitoring platform and its real-time streams of millions of data points was therefore the right candidate."
As Eric explains, "The new challenges are focused on causal relationships and not just correlations between objects. This approach is new, difficult, and not well addressed at the LIG. EV Observe's "root cause analysis" project has therefore enabled the LIG to make progress in this vast field of causality with real-life use cases."
The benefits
- Scalability and robustness of the monitoring platform
As a result of these 4 years of research, EasyVista now has a strong competence in Big Data. Few editors on the market have succeeded in switching to these complex technologies.
- Increase the availability of critical applications
By injecting algorithms into this "big engine" that is Big Data, the prediction of alerts in the near future (+2 hours, +15 hours, etc.) or further into the future (+3 months, +15 months, etc.) makes it possible to save uptime and reduce the pressure on operating teams both during working hours and, above all, during on-call periods.
Rachid: "Working on causality allows us to simplify the analysis of complex systems by focusing on the essential alerts of root causes..."
- Improve the RoI of supervision systems through improved operator efficiency and automation
As Rachid explains, "EV Observe is now able to reduce noise, i.e. reduce false positives without generating false negatives. The adjustment of thresholds in automatic is the key to obtain up to 70% noise reduction without changing the current paradigm and organization of operators. Our recommendation system fits the current user processes with little change management.
We are now working on behavioral to automatically detect abnormal system changes on a large data set."
Rachid Mokhtari, R&D Director at EasyVista "EasyVista has been confronted with a large number of data analysis models in order to select the most relevant ones and successfully implement them in EV Observe. EasyVista and the LIG spent a lot of time developing, testing, improving and approving these models. This allows EasyVista to guarantee a greater reliability of their EV Observe solution to the market.
