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Wednesday, September 16, 2020

To Work from Home or to Work from Work?

Below are my comments to the following post of my former manager:

https://www.linkedin.com/pulse/i-want-work-from-think-kevin-mclaughlin/?trackingId=WMDA4QXhQ9uJ4H%2BsAJImpg%3D%3D 

What do I miss? (working 100% at home)

Whiteboarding. Definitely ! I used to do that a lot: 

- explaining my ideas to developers, so they could implement what want not blindly but with passion. Now working from home I spent twice more time via zoom and still not sure I ignite that passion;

- proving my new and innovative concepts to my boss. Now they have to listen or reading my Ruglish, respectively my ability to convince accepting my ideas is declining...

Serendipit ....Less impactful hallway or over-the-cube-wall conversations can also solve problems and create bonds.....

I feel that too. But in DevOps-Adgile-Slack-zoom environment I am getting used to get what I need. My hobby to be always on-line blogging-presenting (Now it is Virtual Convergence/Seminar) helps a lot.

Rhythms. Yes, life circles are changed. But maybe it is for good. At least for me it is not bad to break current routine and establish new ones. With kids growing up it is anyway unavoidable....  

 


Tuesday, September 15, 2020

My CMG Video presentation "Catching Anomaly and Normality in Cloud by Neural Net and Entropy Calculation"


Saturday, August 22, 2020

CPD - Change Points Detection is planed to be implemented in the free web tool Perfomalist

The next version of the Perfomalist (https://www.perfomalist.com/ ) is coming and will include a new functionality  - Change Point Detection.

How to find a change in the historical time-series data?  

Long ago I have developed a method to do that which is based on EV data (Exception Value - a magnitude of anomalies collected historically). Idea: any change occurred  first would appear as  anomaly and then become a normality (norm), so collecting and analyzing a severity of all anomalies opens possibility to find phases in the history with different patterns. To detect that mathematically one needs just finds all roots of the following equation:   EV(t)=0 , where t is time. But it is too simple as that might give you too many change points. To control the the sensitivity of detecting change points the method  should have some sensitivity tuning parameters,such as following:

Nnormality confidence band in percentiles = UCL-LCL (if it is 100%, that means all observations is normal, 0% means all observations abnormal) 

Imodel insensitivity = EV threshold (if it is 0, that means maximum sensitivity which gives the maximum change points for the given confidence band). 

Respectively the more accurate model would be defined by  the following equation: 

                    |EV(t,N)|=I  or

                 |EV(t,UCL-LCL)|=I 

Where UCL is upper control limit and LCL is lower control limit. Why "||" (absolute value)? To catch two types of changes:  going up- and downwards. 


How the EV(t) function is defined explained in the following white paper - 

This CPD method has been coded by one of the Performalist developer as a python program and here is a test result: 


  
Currently the approach has been testing and  the respectful  micro-service (API) has been developing.  Stay tuned!