Data mining with big data
Abstract
Big Data concern large-volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity, Big Data are now rapidly expanding in all science and engineering domains, including physical, biological and biomedical sciences. This paper presents a HACE theorem that characterizes the features of the Big Data revolution, and proposes a Big Data processing model, from the data mining perspective. This data-driven model involves demand-driven aggregation of information sources, mining and analysis, user interest modeling, and security and privacy considerations. We analyze the challenging issues in the data-driven model and also in the Big Data revolution.
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Traditional data management approaches cannot manage large and diversified data or handle its effective growth and maintenance as volume and velocity increase.
“However, traditional data management approaches are capable neither of managing such a large and diversified amount of data nor of handling its effective growth and maintenance as the volume and velocity of relevant data increase [4; 27; 73].”
From A Big Data Analytics Method for Tourist Behaviour Analysis (2017) · cites this paper as [73]
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- Authors
- Wu, X., Zhu, X., Wu, G.-Q., & Ding, W.
- Affiliations
- Hefei University of Technology (CN)
- University of Massachusetts Boston (US)
- ORCID
- Venue
- IEEE Transactions on Knowledge and Data Engineering
- Published
- 2014-01
- Volume
- 26
- Issue
- 1
- Pages
- 97-107
- ISSN
- 1041-4347 (ISSN-L)
- OpenAlex
- W2040263621
- Cited by
- 2,410
- References
- 53
- Citations by year
- 2026: 37 · 2025: 51 · 2024: 66 · 2023: 96 · 2022: 121 · 2021: 224 · 2020: 271 · 2019: 305 · 2018: 321 · 2017: 323 · 2016: 291 · 2015: 231 · 2014: 71 · 2013: 2
- FWCI
- 434.88
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Knowledge and Data Engineering (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Data Mining
- Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
- Keywords
- big data, data mining, data-driven modeling, information sources, user interest modeling, data security and privacy
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