Non mancare al prossimo NetEye & EriZone User Group

Posted by on Aug 31, 2017 in EriZone & OTRS, NetEye, ntop | 0 comments

UserGroup

NetEye & EriZone User Group

Sfide e opportunità per l’IT Management 4.0

Connectbay, Mantova, Giovedì 19 ottobre 11:00 – 17:00

Siamo lieti di invitarvi il 19 ottobre al NetEye & EriZone User Group. L’evento vi offrirà un’occasione unica per scoprire le ultime novità nell’IT System & Service Management, individuare i requisiti necessari per adeguarsi al GDPR (General Data Protection Regulation) e partecipare attivamente alla definizione della fase evolutiva delle nostre soluzioni.

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Upgrading your Windows computers with WSUS? Here’s a guideline.

Posted by on Aug 22, 2017 in NetEye | 0 comments

Upgrade_WSUS
Windows Server Update Services (WSUS) is an application developed by Microsoft that enables administrators to manage the distribution of updates for Microsoft products to computers in a corporate environment.

The first version of WSUS was known as Software Update Services (SUS) and was created in 2005. Only after 2008 it was distributed as an installable server role.

WSUS manages the update catalog for Windows components and other Microsoft products, the approval cycle, as well as the distribution of updates on a local network. However, it has no control over when and how such updates are applied to the target computers: even with this limit, WSUS is the ideal solution because it is free and easier to manage than the System Center Configuration Manager, a product that can both force and centrally control the distribution of updates.

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Life Cycle Asset Management with GLPI and OCS Inventory

Posted by on Aug 9, 2017 in Asset Management, NetEye | 0 comments

AssetManagement

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:

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Save the date! – NetEye & EriZone User Group 2017

Posted by on Aug 3, 2017 in EriZone & OTRS, NetEye | 0 comments

UserGroup

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.

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Next Level Performance Monitoring – Part II: The Role of Machine Learning and Anomaly Detection

Posted by on Aug 2, 2017 in NetEye, Real User Experience Monitoring | 0 comments

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)

The interested reader is also referred to:
Breiman – Statistical Modeling: The Two Cultures and Statistics vs. Machine Learning, fight!

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.

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