Monday, June 8, 2020

The "Exercise 5. Build your weekly IT-Control Chart by Perfomalist tool" was added to my on-line CMG class "Perfomaly Detection"

As more trainees keep enrolling to my CMG on-line class, 

I have started updating the content.


So far I have added an additional exercise to use recently developed free performance profiler web tool "https://www.perfomalist.com/". So the trainees can build the weekly IT-Control Chart with the provided sample data and with their own data.

The exercise is open for every one here:


Monday, February 3, 2020

#AnomalyDetection Free Web App "PERFOMALIST" (v1.0) is online and ready for beta testing


Welcome to PERFOMALIST v1.0 - date-time-stamped data on-line analyser  https://www.perfomalist.com/


Functionality
1. VISUALIZATION. Weekly (Monthly in v2+) data profiling to visualize patterns, anomalies and short term seasonality via IT-Control Charts. (v1.0)
2. ANALYSIS. Anomalies and Change Points Detection in date-time stamped data. (v2+)
INPUTCSV file with timestamp data (time series observations of a dynamic object). 

Sample input data can be downloaded from from the Download Input Data Sample (https://www.perfomalist.com/sample-upload.csv)

which should look like:

example

Data granularityhourly (v1.0); minutely, daily (v2+)

OUTPUT:
- IT-Control Chart (see example below)  (v1.0)
- Data cube with  summarized data (168 rows/weekhours - v1.0)
- List of anomalies and change points (v2+)

Requirement: 
Input data should consist of at least 3 weeks of history as the method requires comparing the last 7 days of data (actual) with at least 2 weeks long learning/reference data set (baseline). 
The size of the history is limited by about 5 years. Unlimited size of input data will be implemented in v.2.

Additional resources:
How to read IT-Control Chart (on-line article)
- On-line class "Performance Anomaly Detection"

Project contributors:
- Anfisa Trubina 



Thursday, April 19, 2018

My "Performance #AnomalyDetection" Online Course is Launched at CMG.org (#CMGnews)

  • Link to the course is on CMG.org site 
  • $99 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)
See more:

Saturday, November 4, 2017

testing earn.com....

Earn.com for businesses If you’re a sender and want to learn more about how you can use Earn.com to increase your response rates and get more actionable user and customer feedback, contact us: sales@earn.com

#ICPE2026 workshop presentation "Detecting past and future change points in performance data for education and practice"

  Excited to share that we will be presenting at the ICPE 2026 Workshop (WEPPE) on May 4th in Florence, Italy. Our talk, "Detecting pas...