Auto saved by Logseq

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+- [[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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+	- ((631f1a34-3f81-4a1b-9fcb-29b8d39c020e))
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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/alfonso
+  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)