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datasets/CAIN2024/readme.md

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+# CAIN2024 dataset
+
+- The dataset is collected from the experiments of the paper **Novel Contract-based Runtime Explainability Framework for End-to-End Ensemble Machine Learning Serving** published at The 3rd International Conference on AI Engineering – Software Engineering for AI (CAIN 2024).
+- The dataset is collected from 2 applications: CCTVs and Malware Detection.
+- This repo only contains some sample data. The full dataset can be requested by contacting the authors.
+
+- Cite the dataset as follows:
+```bibtex
+@inproceedings{nguyen2024novel,
+  title={Novel Contract-based Runtime Explainability Framework for End-to-End Ensemble Machine Learning Serving},
+  author={Nguyen, Minh-Tri and Truong, Hong-Linh and Truong-Huu, Tram},
+  booktitle={Proceedings of the IEEE/ACM 3rd International Conference on AI Engineering-Software Engineering for AI},
+  pages={234--244},
+  year={2024}
+}
+```

datasets/UCC2024/readme.md

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+# UCC2024 dataset
+
+- The dataset is collected from the experiments of the paper **Optimizing Multiple Consumer-specific Objectives in End-to-End Ensemble Machine Learning Serving** published at The 17th IEEE/ACM International Conference on Utility and Cloud Computing (UCC 2024).
+- The dataset is collected from the applications Object Classification in Autonomous Driving (OCAD).  
+- This repo only contains some sample data. The full dataset can be requested by contacting the authors.
+
+Authors:
+- Minh-Tri Nguyen
+- Hong-Linh Truong
+- Paolo Arcaini
+- Fuyuki Ishikawa
+
+Contact: Minh-Tri Nguyen (tri.m.nguyen@aalto.fi)

datasets/readme.md

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 - Each dataset folder includes the data collected from specific experiments of the related publication:
   - `SPE2024`: The dataset used in the paper **On Optimizing Resources for Real-time End-to-End Machine Learning in Heterogeneous Edges** published at Software: Practice and Experience (SPE) journal. For more details, please refer to the [readme](SPE2024/readme.md). 
   - `UCC2024`: The dataset used in the paper **Optimizing Multiple Consumer-specific Objectives in End-to-End Ensemble Machine Learning Serving** published at The 17th IEEE/ACM International Conference on Utility and Cloud Computing (UCC 2024). For more details, please refer to the [readme](UCC2024/readme.md).
+  - `CAIN2024`: The dataset used in the paper **Novel Contract-based Runtime Explainability Framework for End-to-End Ensemble Machine Learning Serving** published at The 3rd International Conference on AI Engineering – Software Engineering for AI (CAIN 2024). For more details, please refer to the [readme](CAIN2024/readme.md).
 
 Authors: 
 Minh-Tri Nguyen,