Wednesday, January 11, 2023
Sunday, November 6, 2022
The paper about using #Perfomalist "Change Point Detection for #MongoDB Time Series Performance Regression" was cited...
The paper about using #Perfomalist "Change Point Detection for Time Series Performance Regression" was cited in the following paper: "Estimating Breakpoints in Piecewise Linear Regression Using Methods", where our method was mentioned as " … offer a hybrid change point detection system..."
Tuesday, April 12, 2022
Friday, March 4, 2022
We are participating in the data challenge for icpe2022.spec.org conference.
The challenge dataset is provided by MongoDB.
Initially some small part of the data was used to prove that Perfomalist CPD API can be used.
Data looks like a big data cube with numerous dimensional variables and two factual ones (datetime and value). I took one case with a particular slice of this cube and processed that (datetime-value) by calling the Perfomalist API. The result I have plotted using Excel and can be seen in the following picture.
IDEA: Potentially some program could be developed to call the CPD API (i.e., Perfomalist) for every data cube slice and to collect change points in a separate table like in the 2nd picture below:
That (meta-) data then should be correlated with events happening (or not happening) around any change dates detected, e.g., feature flag tuned on/off (that data is hidden from us so far). The result should help to explain each change. Additionally, to measure the magnitude of the change I would suggest calculating the entropy based imbalance of the data between changes (see my last paper how to do that). For example, that could tell how stable or not stable performance had become after particular change.
After my 1st initial Peorfomalist usage, more rigorous usage was done against MongoDB dataset, based on which the following paper was written and accepted for data challenge track of the conference:
"Change Point Detection for MongoDB Time Series Performance Regression" paper for ACM/SPEC ICPE 2022 Data Challenge Track
Monday, February 28, 2022
Monday, January 10, 2022
- Perfomalist 1.1. has now the Change Point Detection API as described in the previous post:
- Perfomalist 1.2. has additional two columns in the table view of the weekly profile to underline two types of anomalies the tool detects:
Thursday, January 6, 2022
Wednesday, December 22, 2021
Perfomalist (www.Perfomalist.com) is a web based anomaly and change point detection tool. The method used by the tool is SETDS - Statistical Exception and Trend Detection System, which is a variation of the Statistical Process Control method applied to time series data. The key idea of the method is EV (Exception Value) which indicates the severity of anomalies calculated as a difference between control limits and actual anomalous data points. Any change that occurs first would appear as an anomaly and then may become a normality (new norm), so collecting overtime and analyzing the severity of all anomalies opens the possibility to find phases in the data history with different patterns. To detect change points between phases one just needs to find all the roots of the following equation: EV(t)=0 , where t is time. . Using this method the Perfomalist API call returns all change points found in the input CSV data. - Igor Trubin, "Exception Based Modeling and Forecasting" , 34th International Computer Measurement Group Conference, December 7-12, 2008, Las Vegas, Nevada, USA, Proceedings
Thursday, December 2, 2021
Sunday, November 21, 2021
PRODUCT: www.Perfomalist.com www.CMGimpact.com LinkedIn Post ABSTRACT: The MASF/SETDS method of detecting changes and anomalies in performa...
The paper about Perfomalist #ChangeDetection API is accepted for ACM/SPEC ICPE 2022 Data Challenge Track"Change Point Detection (#ChangeDetection) for MongoDB Time Series Performance Regression" paper for ACM/SPEC ICPE 2022 Data Chall...
Link to tool: www.Perfomalist.com Control Points API POST https://api.perfomalist.com/ api/controlpoints.py 'Accept: text/plain' ...