alibaba/clusterdata: papers linking to it

GitHub · 2,248 stars · 488 forks · Jupyter Notebook · created 2017 · github.com/alibaba/clusterdata

← Code and data repositories cited in research papers

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.

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#

20212026

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	4
Applied Intelligence	3
Neural Computing and Applications	2
Computers & Industrial Engineering	1
Machine Learning	1

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	Applied Intelligence	Lei Chen, Weiwen Zhang, Haiming Ye	42	cites it
2022-02-09	Workflow performance prediction based on graph structure aware deep attention neural network	Journal of Industrial Information Integration	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	Expert Systems with Applications	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	Applied Intelligence	Jin Wang	12	cites it
2025-06-26	Dynamic task offloading for multiple real-time workflows in a joint MD and MEC system	Expert Systems with Applications	Haihong Zhu, Xiaoping Li	4	cites it
2026-01-18	TayMAML: A meta reinforcement learning-based task scheduling method for edge computing	Expert Systems with Applications	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	Applied Intelligence	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	Neural Computing and Applications	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	Expert Systems with Applications	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	Neural Networks	Chunmao Jiang, Wei Wu, Qiaoping Zhong	0	cites it
2026-05-01	Learning to Process Relational Information for Cloud Computing Task Scheduling	Machine Learning	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	Neural Computing and Applications	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	Computers & Industrial Engineering	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

@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}
}

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