A Survey and Taxonomy of Distributed Data Mining Research Studies: A Systematic Literature Review

• URL: http://arxiv.org/abs/2009.10618v1
• Date: Mon, 14 Sep 2020 14:58:42 GMT
• Title: A Survey and Taxonomy of Distributed Data Mining Research Studies: A Systematic Literature Review
• Authors: Fauzi Adi Rafrastara, Qi Deyu
• Abstract summary: Data Mining (DM) method has been evolving year by year and as of today there is also the enhancement of DM technique that can be run several times faster than the traditional one. It is not a new field in data processing actually, but in the recent years many researchers have been paying more attention on this area.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Context: Data Mining (DM) method has been evolving year by year and as of today there is also the enhancement of DM technique that can be run several times faster than the traditional one, called Distributed Data Mining (DDM). It is not a new field in data processing actually, but in the recent years many researchers have been paying more attention on this area. Problems: The number of publication regarding DDM in high reputation journals and conferences has increased significantly. It makes difficult for researchers to gain a comprehensive view of DDM that require further research. Solution: We conducted a systematic literature review to map the previous research in DDM field. Our objective is to provide the motivation for new research by identifying the gap in DDM field as well as the hot area itself. Result: Our analysis came up with some conclusions by answering 7 research questions proposed in this literature review. In addition, the taxonomy of DDM research area is presented in this paper. Finally, this systematic literature review provides the statistic of development of DDM since 2000 to 2015, in which this will help the future researchers to have a comprehensive overview of current situation of DDM.

Related papers

• A Survey of Decomposition-Based Evolutionary Multi-Objective Optimization: Part II -- A Data Science Perspective [4.322038460697958]
  We build a knowledge graph that encapsulates more than 5,400 papers, 10,000 authors, 400 venues, and 1,600 institutions for MOEA/D research. We also explore the collaboration and citation networks of MOEA/D, uncovering hidden patterns in the growth of literature.
  arXiv  Detail & Related papers  (2024-04-22T14:38:58Z)
• A Survey on Interpretable Cross-modal Reasoning [64.37362731950843]
  Cross-modal reasoning (CMR) has emerged as a pivotal area with applications spanning from multimedia analysis to healthcare diagnostics. This survey delves into the realm of interpretable cross-modal reasoning (I-CMR) This survey presents a comprehensive overview of the typical methods with a three-level taxonomy for I-CMR.
  arXiv  Detail & Related papers  (2023-09-05T05:06:48Z)
• Dataset Distillation: A Comprehensive Review [76.26276286545284]
  dataset distillation (DD) aims to derive a much smaller dataset containing synthetic samples, based on which the trained models yield performance comparable with those trained on the original dataset. This paper gives a comprehensive review and summary of recent advances in DD and its application.
  arXiv  Detail & Related papers  (2023-01-17T17:03:28Z)
• Research Trends and Applications of Data Augmentation Algorithms [77.34726150561087]
  We identify the main areas of application of data augmentation algorithms, the types of algorithms used, significant research trends, their progression over time and research gaps in data augmentation literature. We expect readers to understand the potential of data augmentation, as well as identify future research directions and open questions within data augmentation research.
  arXiv  Detail & Related papers  (2022-07-18T11:38:32Z)
• D3: A Massive Dataset of Scholarly Metadata for Analyzing the State of Computer Science Research [27.882505456528243]
  DBLP is the largest open-access repository of scientific articles on computer science. We retrieved more than 6 million publications from DBLP and extracted metadata. D3 can be used to identify trends in research activity, productivity, focus, bias, accessibility, and impact of computer science research.
  arXiv  Detail & Related papers  (2022-04-28T09:59:52Z)
• Domain Generalization: A Survey [146.68420112164577]
  Domain generalization (DG) aims to achieve OOD generalization by only using source domain data for model learning. For the first time, a comprehensive literature review is provided to summarize the ten-year development in DG.
  arXiv  Detail & Related papers  (2021-03-03T16:12:22Z)
• Data Mining with Big Data in Intrusion Detection Systems: A Systematic Literature Review [68.15472610671748]
  Cloud computing has become a powerful and indispensable technology for complex, high performance and scalable computation. The rapid rate and volume of data creation has begun to pose significant challenges for data management and security. The design and deployment of intrusion detection systems (IDS) in the big data setting has, therefore, become a topic of importance.
  arXiv  Detail & Related papers  (2020-05-23T20:57:12Z)
• Multi-source Domain Adaptation in the Deep Learning Era: A Systematic Survey [53.656086832255944]
  Multi-source domain adaptation (MDA) is a powerful extension in which the labeled data may be collected from multiple sources. MDA has attracted increasing attention in both academia and industry.
  arXiv  Detail & Related papers  (2020-02-26T08:07:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.