Evidence map›Paper›PMID 39635735›Full record

ArticleScience progress

Optimization algorithm of association rule mining for heavy-haul railway freight train fault data based on distributed parallel computing.

Yanhui Bai, Honghui Li, Wengang Wang, Shufang Liu, Ning Zhang, Chun Zhang

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PubMed Full text at PMC Publisher
In one paragraph

Article in Science progress. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Yanhui BaiSchool of Computer Science and Technology, Beijing Jiaotong University, Beijing, China.ORCID 0009-0003-7255-4141
Honghui LiSchool of Computer Science and Technology, Beijing Jiaotong University, Beijing, China.ORCID 0000-0002-6831-1919
Wengang WangTechnology and Information Department, China National Energy Co., Ltd, Beijing, China.ORCID 0009-0000-7148-1970
Shufang LiuSchool of Railway Safety Theory and Technology, Southwest Jiaotong University, Sichuan, China.ORCID 0009-0003-0989-6114
Ning ZhangSchool of Computer Science and Technology, Beijing Jiaotong University, Beijing, China.ORCID 0000-0002-8614-4851
Chun ZhangSchool of Computer Science and Technology, Beijing Jiaotong University, Beijing, China.ORCID 0009-0000-8754-7487

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the continuous improvement in the efficiency of the heavy-haul railway freight transportation, the pressure on on-site maintenance is increasing. In-depth research on fault characteristics carries significant importance for fault scientific judgment and fault prevention. This study proposes an efficient association rule mining (ARM) algorithm, HM-RDHP, for analyzing fault data from heavy-haul railway freight trains. The algorithm introduces distributed parallel computing technology, integrating the MapReduce framework and HBase on the Hadoop platform to process large volumes of complex fault data efficiently. Experimental results show that the HM-RDHP algorithm can efficiently uncover hidden patterns and associations within the fault data of heavy-haul railway freight trains. The mined association rules provide a valuable reference model to aid in predictive maintenance and fault prevention strategies for freight train maintenance departments.

Indexed as

association rule miningDHP algorithmdistributed parallel computingHeavy-haul railway freight trainMapReduce

Identifiers

PMID39635735
PMCPMC11618913

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.