Hashing techniques: A survey and taxonomy
- Publication Type:
- Journal Article
- Citation:
- ACM Computing Surveys, 2017, 50 (1)
- Issue Date:
- 2017-04-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| a11-chi.pdf | Published Version | 1.13 MB |
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© 2017 ACM. With the rapid development of information storage and networking technologies, quintillion bytes of data are generated every day from social networks, business transactions, sensors, and many other domains. The increasing data volumes impose significant challenges to traditional data analysis tools in storing, processing, and analyzing these extremely large-scale data. For decades, hashing has been one of the most effective tools commonly used to compress data for fast access and analysis, as well as information integrity verification. Hashing techniques have also evolved from simple randomization approaches to advanced adaptive methods considering locality, structure, label information, and data security, for effective hashing. This survey reviews and categorizes existing hashing techniques as a taxonomy, in order to provide a comprehensive view of mainstream hashing techniques for different types of data and applications. The taxonomy also studies the uniqueness of each method and therefore can serve as technique references in understanding the niche of different hashing mechanisms for future development.
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