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Cited by 454Data Quality and ManagementDomain Adaptation and Few-Shot LearningBig Data Technologies and Applications

The DOI matches its Crossref record; 96 of the 101 references it cites were found in a registry or an index.

Methodologies for Cross-Domain Data Fusion: An Overview

Zheng, Yu·2015·IEEE Transactions on Big Data
DOI 10.1109/tbdata.2015.2465959
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Abstract

Traditional data mining usually deals with data from a single domain. In the big data era, we face a diversity of datasets from different sources in different domains. These datasets consist of multiple modalities, each of which has a different representation, distribution, scale, and density. How to unlock the power of knowledge from multiple disparate (but potentially connected) datasets is paramount in big data research, essentially distinguishing big data from traditional data mining tasks. This calls for advanced techniques that can fuse knowledge from various datasets organically in a machine learning and data mining task. This paper summarizes the data fusion methodologies, classifying them into three categories: stage-based, feature level-based, and semantic meaning-based data fusion methods. The last category of data fusion methods is further divided into four groups: multi-view learning-based, similarity-based, probabilistic dependency-based, and transfer learning-based methods. These methods focus on knowledge fusion rather than schema mapping and data merging, significantly distinguishing between cross-domain data fusion and traditional data fusion studied in the database community. This paper does not only introduce high-level principles of each category of methods, but also give examples in which these techniques are used to handle real big data problems. In addition, this paper positions existing works in a framework, exploring the relationship and difference between different data fusion methods. This paper will help a wide range of communities find a solution for data fusion in big data projects.

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Crossref · 10.1109/tbdata.2015.2465959

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Citations

Cites

Show all 101 references

Research works printed but not linked: 6

  1. tipping, 2001, Sparse Bayesian learning and the relevance vector machine, J Mach Learn Res, 1, 211
  2. ho, 0, Random decision forest, Proc 3rd Int Conf Document Anal Recog, 278
  3. sun, 0, Rankclus: Integrating clustering with ranking for heterogeneous information network analysis, Proceedings of the 11th International Conference on Extending Database Technology Advances in Database Technology, 565
  4. lecun, 0, Deep learning tutorial, Proc Int Conf Mach Learn Tut
  5. zheng, 0, U-Air: When urban air quality inference meets big data, Proc 19th SIGKDD Conf Knowl Discovery Data Mining, 1436
  6. bishop, 2006, Graphical models, Pattern Recognition and Machine Learning, 359

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Related

Metadata

Authors
Zheng, Y.
Affiliations
Microsoft Research Asia (China) (CN)
Corresponding author
Zheng, Yu
ORCID
Zheng, Yu
Venue
IEEE Transactions on Big Data
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2015-03-01
Volume
1
Issue
1
Pages
16-34
ISSN
2332-7790 (ISSN-L)
DOI
10.1109/tbdata.2015.2465959
OpenAlex
W1892382968
Cited by
454
References
101
Citations by year
2026: 23 · 2025: 44 · 2024: 38 · 2023: 42 · 2022: 46 · 2021: 46 · 2020: 47 · 2019: 51 · 2018: 40 · 2017: 38 · 2016: 36 · 2015: 3
FWCI
48.70
Type
journal article
Language
en
Open access
closed
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
Locations
IEEE Transactions on Big Data (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Information Systems › Data Quality and Management
  • Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
  • Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
Keywords
knowledge fusion, big data, multi-view learning, transfer learning, feature-level fusion, heterogeneous datasets, schema mapping

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