If you are using our Asset Management module integrated into NetEye, you probably already know about the potential of OCS Inventory and GLPI. However, often users are not aware of all the functionalities available in Life Cycle Asset Management. So let’s highlight some of the most important features to manage the entire life cycle of your assets:
NetEye & EriZone User Group
Challenges and opportunities in the IT Management 4.0
Connectbay, Mantova, October 19, 11:00 – 17:00
We are glad to invite you to attend the NetEye & EriZone User Group. The yearly event for our customers will offer you the possibility to discover the innovations in the IT Service Management field, to identify modern approaches for the Performance Monitoring and to participate in the definition of our solution roadmap.
Machine learning and anomaly detection are being mentioned with increasing frequency in performance monitoring. But what are they and why is interest in them rising so quickly?
From Statistics to Machine Learning
There have been several attempts to explicitly differentiate between machine learning and statistics. It is not so easy to draw a line between them, though.
For instance, different experts have said:
- “There is no difference between Machine Learning and Statistics” (in terms of maths, books, teaching, and so on)
- “Machine Learning is completely different from Statistics.” (and the only future of both)
- “Statistics is the true and only one” (Machine Learning is a different name used for part of statistics by people who do not understand the real concepts of what they are doing)
In short we will not answer this question here. But for monitoring people it is still relevant that the machine learning and statistics communities currently focus on different directions and that it might be convenient to use methods from both fields. The statistics community focuses on inference (they want to infer the process by which data were generated) while the machine learning community puts emphasis on the prediction of what future data are expected to look like. Obviously the two interests are not independent. Knowledge about the generating model could be used for creating an even better predictor or anomaly detection algorithm.
Every so often I get asked whether it is possible to integrate Active Directory Users and Groups with NetEye. Until now my answer has always been that it is possible to use AD via its LDAP functionality as an authentication backend, and that you may manually add each AD user one-by-one to NetEye.
I was never very satisfied with this answer and so I tried to find a solution. Here’s what needs to be done:
- Fixed different SLA values between SLA and SLA Year section (NEREP-190)
- Fixed bug where report title should have a default
- Fixed errors during neteye start (NEVHA-46)