62 lines
3.6 KiB
Markdown
62 lines
3.6 KiB
Markdown
- [[PAPERS/Software-ecosystem-book-chapter]]
|
|
- Lavorare su sezione presentazione processo
|
|
-  #Highlights
|
|
- Machine Learning is a method to find a relationship between different sets of values by utilizing ==statistical models== and ==algorithms==.
|
|
- ((631f19cb-c451-4e9b-bb59-1d22d7bac8b2))
|
|
- ((631f1a34-3f81-4a1b-9fcb-29b8d39c020e))
|
|
- ((631f1aa1-6e38-4cb4-a283-150b6fac9e58))
|
|
- [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) |