- [[PAPERS/Software-ecosystem-book-chapter]]
	- Lavorare su sezione presentazione processo
- ![But What is Machine Learning - An ML Guide for Dummies.pdf](../assets/But_What_is_Machine_Learning_-_An_ML_Guide_for_Dummies_1662982150928_0.pdf) #Highlights
	- Machine Learning is a method to find a relationship between different sets of values by utilizing ==statistical models== and ==algorithms==.
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		- [ML courses, videos, and websites](https://todoist.com/showTask?id=6165927353)
		  id:: 631f2025-9cad-413a-8fcf-c1c78bd68635
- type::  [[meeting]]
  external-links::
  people:: #people/AlfonsoPierantonio
  tags:: [[PAPERS/Software-ecosystem-book-chapter]]
  date:: [[12-09-2022]]
  duration-min:: 73
	- **MDE for [[Digital Twin]]** ([Schloss Dagstuhl : Seminar Homepage](https://www.dagstuhl.de/en/program/calendar/semhp/?semnr=22362)) [[Ideas]]
		- Lavoro di Andreas che ha fatto qualche survey
			- ci sono diverse componenti tra cui quella prescrittiva
			- Anomaly detection
			- Predictive maintenance
			- Dataset disponibili?
				- Petrobras ha messo disponibile dei #dataset
					- [UCI Machine Learning Repository: 3W dataset Data Set](https://archive.ics.uci.edu/ml/datasets/3W+dataset)
						- [ricardovvargas/3w_dataset: The first realistic and public dataset with rare undesirable real events in oil wells. (github.com)](https://github.com/ricardovvargas/3w_dataset)
						- [[@A realistic and public dataset with rare undesirable real events in oil wells]]
						- **Da vedere**
							- [Machine learning and transport simulations for groundwater anomaly detection - ScienceDirect (cineca.it)](https://www-sciencedirect-com.univaq.clas.cineca.it/science/article/pii/S0377042720302739?via%3Dihub)
							- [Imminence Monitoring of Critical Events: A Representation Learning Approach | Proceedings of the 2021 International Conference on Management of Data (cineca.it)](https://dl-acm-org.univaq.clas.cineca.it/doi/10.1145/3448016.3452804)
				- Disponibilità di simulatori
					- Ventilatore meccanico
		- Domini consolidati
			- Manufatturiero dove hai una controparte fisica / meccanica
			- Sistemi umani (es. medicina di precisione, agricoltura di precisione)
	- **Aggiornamento book chapter**
		- Condiviso il paper [[@AI-driven streamlined modeling: experiences and lessons learned from multiple domains]] con Alfonso. Ha detto che se lo vedrà per Venerdi 16 Settembre
- type::  [[meeting]]
  external-links::  
  tags::  
  people:: #people/Vittorio_Cortellessa #people/traini #people/riccardo #people/imran 
  tags:: 
  date:: [[12-09-2022]]
  duration-min:: 60
	- Benchmarking process
		- 4 different steps
			- *Identify code segment*
				- Recommender systems for recommending parts of software systems that should be benchmarked
					- Mixing static and dynamic analysis
					- Related work
						- AutoJMH (2016)
						- J23jmh (2022)
			- *Wrap code into payload*
				- remove code smells (of the code we want to benchmark)
					- Smells that affect the performance
						- We need to be cautious on this because we might introduce some bias or in general, affect the behaviour of the original system
					- Dead code identification
			- [...]
			- *Execution large # of times*
				- Reduce time to execute tests
	- **Ideas**
		- Identify features (static or dynamic) of existing projects that can underpin the recommender system
			- The goal is to recommend code locations that need to be benchmarked via [[JMH]]
		- [projects.csv](../assets/projects_1663000066246_0.csv)