--- title: "alibaba/clusterdata: papers citing this GitHub repository" description: "13 papers in the tracked journals link to the GitHub repository alibaba/clusterdata in their Crossref records, the first in 2021; most of them (7) in 2026." url: https://scholar4ai.com/code/github-alibaba-clusterdata markdown_url: https://scholar4ai.com/code/github-alibaba-clusterdata.md license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/) share_text: "13 papers in the tracked journals link to the GitHub repository alibaba/clusterdata in their Crossref records, the first in 2021; most of them (7) in 2026. (as of 2026-11-03)" data_url: https://scholar4ai.com/data/rankings.json updated: 2026-11-03T14:43:26+09:00 window: 2026-10-05 to 2026-11-03 source: OpenAlex (CC0) --- # alibaba/clusterdata: papers linking to it Research papers in the 480 tracked journals that link to alibaba/clusterdata (https://github.com/alibaba/clusterdata), read from their Crossref records: by year, category and journal, the family tree of papers building on one another, and the linked repositories derived from it or that it derives from. - GitHub - 2,248 stars - 488 forks - Jupyter Notebook - created 2017 - https://github.com/alibaba/clusterdata All repositories: https://scholar4ai.com/code.md ## Key findings - 13 papers in the tracked journals link to the GitHub repository alibaba/clusterdata in their Crossref records, the first in 2021; most of them (7) in 2026. - By category: AI (11), Operations Research (1) and Information Systems (1); the journals with the most are Expert Systems with Applications (4), Applied Intelligence (3) and Neural Computing and Applications (2). ## By year *Papers linking to alibaba/clusterdata by publication year* | Year | Papers | |---|---:| | 2021 | 1 | | 2022 | 2 | | 2023 | 1 | | 2024 | 1 | | 2025 | 1 | | 2026 | 7 | ## Journals *Journals whose papers link to alibaba/clusterdata most* | Journal | Papers | |---|---:| | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 4 | | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | 3 | | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | 2 | | [Computers & Industrial Engineering](https://scholar4ai.com/journals/computers-industrial-engineering.md) | 1 | | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | 1 | ## Papers *Papers linking to alibaba/clusterdata, most cited first* | Published | Title | Journal | Authors | Citations | Link | |---|---|---|---|---:|---| | 2022-02-21 | [Accurate workload prediction for edge data centers: Savitzky-Golay filter, CNN and BiLSTM with attention mechanism](https://doi.org/10.1007/s10489-021-03110-x) | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | Lei Chen, Weiwen Zhang, Haiming Ye | 42 | cites it | | 2022-02-09 | [Workflow performance prediction based on graph structure aware deep attention neural network](https://doi.org/10.1016/j.jii.2022.100337) | [Journal of Industrial Information Integration](https://scholar4ai.com/journals/journal-of-industrial-information-integration.md) | Jixiang Yu, Ming Yu Gao, Yuchan Li et al. | 35 | cites it | | 2023-07-07 | [A multi-population particle swarm optimization-based time series predictive technique](https://doi.org/10.1016/j.eswa.2023.120935) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Cry Kuranga, Tendai Shelton Muwani, Njodzi Ranganai | 26 | cites it | | 2021-10-21 | [Edge artificial intelligence-based affinity task offloading under resource adjustment in a 5G network](https://doi.org/10.1007/s10489-021-02786-5) | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | Jin Wang | 12 | cites it | | 2025-06-26 | [Dynamic task offloading for multiple real-time workflows in a joint MD and MEC system](https://doi.org/10.1016/j.eswa.2025.128751) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Haihong Zhu, Xiaoping Li | 4 | cites it | | 2026-01-18 | [TayMAML: A meta reinforcement learning-based task scheduling method for edge computing](https://doi.org/10.1016/j.eswa.2026.131253) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Tao Ju, Zhiqing Wang, Heting Kang et al. | 4 | cites it | | 2024-09-18 | [DAGCN: hybrid model for efficiently handling joint node and link prediction in cloud workflows](https://doi.org/10.1007/s10489-024-05828-w) | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | Ruimin Ma, Junqi Gao, Li Cheng et al. | 2 | cites it | | 2026-03-01 | [A novel federated swarm learning for increased resilience in latency-aware cloud-native applications](https://doi.org/10.1007/s00521-025-11728-y) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Pooja Nagpal, Sunil Kumar V, Ankit Khare et al. | 1 | cites it | | 2026-01-31 | [Budget-constrained workflow scheduling using task prediction in hybrid environments](https://doi.org/10.1016/j.eswa.2026.131436) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Changhong Tai, Huiying Jin, Qi Wang et al. | 0 | cites it | | 2026-04-23 | [Self-supervised multi-scale cloud workload prediction with time series data augmentation](https://doi.org/10.1016/j.neunet.2026.109034) | [Neural Networks](https://scholar4ai.com/journals/neural-networks.md) | Chunmao Jiang, Wei Wu, Qiaoping Zhong | 0 | cites it | | 2026-05-01 | [Learning to Process Relational Information for Cloud Computing Task Scheduling](https://doi.org/10.1007/s10994-026-07043-9) | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | Seong-Hyun Hong, Myunsoo Kim, Byung-Jun Lee | 0 | cites it | | 2026-06-01 | [An intelligent scheduling and resource prediction framework using multidimensional data streams and deep reinforcement learning](https://doi.org/10.1007/s00521-026-11917-3) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Wei Deng, Ziliang Qiu, X F Liu et al. | 0 | cites it | | 2026-09-01 | [RAWS: a reinforcement learning-based adaptive weighted scoring framework for task scheduling under multi-resource constraints](https://doi.org/10.1016/j.cie.2026.112354) | [Computers & Industrial Engineering](https://scholar4ai.com/journals/computers-industrial-engineering.md) | Yue Shi, He Luo, Xin Cao et al. | 0 | cites it | ## How this is measured Links are read from each paper's Crossref record as its publisher deposited it: the URLs and repository DOIs in its reference list (the software and data it cites) and its supplementary links (“is-supplemented-by”: its own code or data). A paper that gives its code only in the text, a footnote or a data availability statement is not counted, and some publishers deposit reference lists without their URLs, so every count here is a lower bound. Repository details come from the GitHub REST API (stars, forks, and the repository a fork was made from), the Hugging Face Hub API (likes, downloads, and the base model a model was derived from) and DataCite (Zenodo and other data DOIs, and the GitHub release a Zenodo record archives), read again every month. ## Cite this page Scholar4AI. (2026). alibaba/clusterdata: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/github-alibaba-clusterdata ```bibtex @misc{scholar4ai-code-github-alibaba-clusterdata, author = {{Scholar4AI}}, title = {alibaba/clusterdata: papers linking to it}, year = {2026}, howpublished = {\url{https://scholar4ai.com/code/github-alibaba-clusterdata}}, urldate = {2026-11-03}, note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0} } ``` License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).