Saturday, April 26, 2025
Test cases for Anomaly/change detection hackathon
Test cases for Anomaly/change detection hackathon.
Test data: https://github.com/elisejiuqizhang/TS-AD-Datasets
Mainpage is here. The dataset contains transactions made by credit cards in September 2013 by European cardholders, yet due to privacy and security reasons, what we see is the result of a PCA transformation.
input:
{
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Friday, November 22, 2024
"Detecting Past and Future Change Points in Performance Data" - research paper preprint
Our research paper was accepted for ORAL PRESENTATION at ICTDsC 2024 in India.
We were not able to go there and plan to publish that later at another place. The abstract is below.
Wednesday, January 11, 2023
Perfomalist team is presenting at www.CMGimpact.com international conference in Orlando.
www.CMGimpact.com
ABSTRACT: The MASF/SETDS method of detecting changes and anomalies in performance data, its recent implementation and the way to use and interpret the results will be presented with real examples against MangoDB testing data.
Since 1995, a time when the CMG conference published a very influential paper about the MASF method of anomaly detection, this topic has been increasingly more popular in the area of Capacity and Performance management. The modification of the MASF – SETDS method – was introduced in the 2002 CMG best paper and then got implemented in several companies and applications. This included the CMG online class “Perfomalies (Performance Anomaly) Detection”. Most recently this method has been turned into the cloud based serverless API microservice which is available for free via “Perfomalist” web app. The Perfomalist change points detection API was used against MongoDB’s testing data and got acceptable results which were published in the SPEC.org 2022 conference. This paper included an additional post-processing algorithm (XGBoost) to eliminate false positives which is planned to be added to Perfomalist service.
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 #MongoDB Time Series Performance Regression" was cited in the following paper: "Estimating Breakpoints in Piecewise Linear Regression Using #MachineLearning Methods", where our method was mentioned as " … offer a hybrid change point detection system..."
Tuesday, April 12, 2022
Friday, March 4, 2022
Perfomalist #ChangeDetection API was used against #MongoDB #perfomanceTesting dataset
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.
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 Release Notes
- Perfomalist 1.1. has now the Change Point Detection API as described in the previous post:
The Change Points Detection Perfomalist API beta version is released.
Filipp Trubin
- 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
Perfomalist is referenced in the following published at Springler paper:
LINK to paper: https://www.trub.in/2022/01/performance-anomaly-and-change-point.html
Intelligent Sustainable Systems pp 403-407| Cite as
Performance Anomaly and Change Point Detection for Large-Scale System Management
Wednesday, December 22, 2021
Perfomalist
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. [1]. Using this method the Perfomalist API call returns all change points found in the input CSV data.
[1] - Igor Trubin, "Exception Based Modeling and Forecasting" , 34th International Computer Measurement Group Conference, December 7-12, 2008, Las Vegas, Nevada, USA, Proceedings
-
Link to tool: www.Perfomalist.com Control Points API POST https://api.perfomalist.com/ api/controlpoints.py 'Accept: text/plain' ... -
"Change Point Detection (#ChangeDetection) for MongoDB Time Series Performance Regression" paper for ACM/SPEC ICPE 2022 Data Chall...
