---
title: "Intel-bigdata/HiBench: papers citing it"
description: "4 papers in the tracked journals link to the GitHub repository Intel-bigdata/HiBench in their Crossref records, the first in 2019; most of them (1) in 2022."
url: https://scholar4ai.com/code/github-intel-bigdata-hibench
markdown_url: https://scholar4ai.com/code/github-intel-bigdata-hibench.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "4 papers in the tracked journals link to the GitHub repository Intel-bigdata/HiBench in their Crossref records, the first in 2019; most of them (1) in 2022. (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)
---

# Intel-bigdata/HiBench: papers linking to it

Research papers in the 480 tracked journals that link to Intel-bigdata/HiBench (https://github.com/Intel-bigdata/HiBench), 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
- 1,483 stars
- 763 forks
- Java
- created 2012
- archived
- https://github.com/Intel-bigdata/HiBench

All repositories: https://scholar4ai.com/code.md

## Key findings

- 4 papers in the tracked journals link to the GitHub repository Intel-bigdata/HiBench in their Crossref records, the first in 2019; most of them (1) in 2022.
- By category: AI (4); the journals with the most are Journal Of Big Data (4).
- 3 of the papers build on one another: a later paper cites an earlier one that also links Intel-bigdata/HiBench.

## By year

*Papers linking to Intel-bigdata/HiBench by publication year*

| Year | Papers |
|---|---:|
| 2019 | 1 |
| 2020 | 1 |
| 2021 | 1 |
| 2022 | 1 |

## Journals

*Journals whose papers link to Intel-bigdata/HiBench most*

| Journal | Papers |
|---|---:|
| [Journal Of Big Data](https://scholar4ai.com/journals/journal-of-big-data.md) | 4 |

## Family tree

Each paper sits under the earlier paper it builds on.

- [A comprehensive performance analysis of Apache Hadoop and Apache Spark for large scale data sets using HiBench](https://doi.org/10.1186/s40537-020-00388-5) (Journal Of Big Data, 2020)
  - [A parallelization model for performance characterization of Spark Big Data jobs on Hadoop clusters](https://doi.org/10.1186/s40537-021-00499-7) (Journal Of Big Data, 2021) — cites the paper above
    - [Runtime prediction of big data jobs: performance comparison of machine learning algorithms and analytical models](https://doi.org/10.1186/s40537-022-00623-1) (Journal Of Big Data, 2022) — cites the paper above

## Papers

*Papers linking to Intel-bigdata/HiBench, most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2019-06-06 | [Big data stream analysis: a systematic literature review](https://doi.org/10.1186/s40537-019-0210-7) | [Journal Of Big Data](https://scholar4ai.com/journals/journal-of-big-data.md) | Taiwo Kolajo, Olawande J. Daramola, Ayodele Ariyo Adebiyi | 259 | cites it |
| 2020-12-01 | [A comprehensive performance analysis of Apache Hadoop and Apache Spark for large scale data sets using HiBench](https://doi.org/10.1186/s40537-020-00388-5) | [Journal Of Big Data](https://scholar4ai.com/journals/journal-of-big-data.md) | Nasim Ahmed, Andre L. C. Barczak, Teo Sušnjak et al. | 107 | cites it |
| 2022-05-19 | [Runtime prediction of big data jobs: performance comparison of machine learning algorithms and analytical models](https://doi.org/10.1186/s40537-022-00623-1) | [Journal Of Big Data](https://scholar4ai.com/journals/journal-of-big-data.md) | Nasim Ahmed, Andre L. C. Barczak, Mohammad A. Rashid et al. | 28 | cites it |
| 2021-08-14 | [A parallelization model for performance characterization of Spark Big Data jobs on Hadoop clusters](https://doi.org/10.1186/s40537-021-00499-7) | [Journal Of Big Data](https://scholar4ai.com/journals/journal-of-big-data.md) | Nasim Ahmed, Andre L. C. Barczak, Mohammad A. Rashid et al. | 22 | 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). Intel-bigdata/HiBench: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/github-intel-bigdata-hibench

```bibtex
@misc{scholar4ai-code-github-intel-bigdata-hibench,
  author = {{Scholar4AI}},
  title = {Intel-bigdata/HiBench: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/github-intel-bigdata-hibench}},
  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/).