Wednesday, April 18, 2018

Performance #AnomalyDetection Online Course Info and #Perfomalist Tool DEMO

Perfomalist.com (Anomaly visualization) tool demo:

See Course Here: Performance Anomaly Detection - Power of Control Chart: How to Read, How to Build, How to Use for Performance Anomaly Detection
Free for CMG Members; $149 for Non-Members.

What is covered:

Machine learning based Anomaly Detection technique
Classical (SPC) and MASF (For system performance data) Control Chartirting
Where is the Control Chart Used?
What are the types of Control Charts?
Reading, building, and interpreting Control Charts
Typical cases of real world issues captured by anomaly detection system (VMs, Mainframes, Middleware, E2E response and more)
How to build free AWS cloud server with R and build there control charts
Performance anomaly (Perfomaly) detection system R implementation example (SEDS-lite - open source based tool)

Previews:

"Perfomaly"=Performance Anomaly:

Control Chart as a Machine Learning Tool to Detect Anomalies

See mode details in another post of this blog:

Performance Anomaly ("Perfomaly") Detection. Parts 1-4: Power of Control Charts

Feedback is welcome.

Информация на русском здесь

Igor Trubin

He started in 1979 as IBM/370 system engineer. In 1986 he got his PhD. in Robotics at St. Petersburg Technical University (Russia) and then worked as a professor teaching CAD/CAM, Robotics for 12 years. He published 30+ papers and made several presentations for conferences related to the Robotics and Artificial Intelligent fields. In 1999 he moved to the US, worked at Capital One bank as a Capacity Planner. His first CMG.org paper was written and presented in 2001. The next one, "Exception Detection System Based on MASF Technique," won a Best Paper award at CMG'02 and was presented at UKCMG'03 in Oxford, England. He made other tech. presentations at IBM z/Series Expo, SPEC.org, Southern and Central Europe CMG and ran several workshops covering his original method of Anomaly and Change Point Detection (Perfomalist.com). Author of “Performance Anomaly Detection” class (at CMG.com). Worked 2 years as the Capacity team lead for IBM, worked for SunTrust Bank for 3 years and then at IBM for 3 years as Sr. IT Architect. Now he works for Capital One bank as IT Manager at the Cloud Engineering and since 2015 he is a member of CMG.org Board of Directors. Runs UT channel iTrubin

Tuesday, April 17, 2018

Perfomalist is a #ChangeDetection system based on Exception Value (EV) data analysis

2022 update:

Now the same method is used for free online tool - Perfomalist https://www.perfomalist.com/

More detailed has been added to the method and published here "

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

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The right term for SETDS methodology is Anomaly and Change (point) detection.

The second part of SETDS - trend detection recently implemented on R and got the "TrendieR" name - is actually a Change Point Detection tool.

See more details how it works in the previous post:

AnomalyDetection vs. NoveltyDetection. SETDS Method Detects and Separates both

But in short the change points are the the following equation roots (solutions)

EV(t)=0

Where t is time and EV is Exception Value (magnitude of exception calculated as the difference between actual data and baseline statistical limits UCL and LCL).

In the "Exception Based Modeling and Forecasting"

CMG.org paper there was the attempt to describe that calculation more formally by the following formulas: