Jump Filter: A Dynamic Sketch for Big Data Governance Article Swipe
Pengtao Fu
,
Lailong Luo
,
Deke Guo
,
Xiang Zhao
,
Shangsen Li
,
Huaimin Wang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.21655/ijsi.1673-7288.00296
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.21655/ijsi.1673-7288.00296
PDF HTML XML Export Cite reminder Jump Filter: A Dynamic Sketch for Big Data Governance DOI: 10.21655/ijsi.1673-7288.00296 Author: Affiliation: Clc Number: Fund Project: Article | Figures | Metrics | Reference | Related | Cited by | Materials | Comments Abstract:With the rapid development of information technology, the volume of data maintains exponential growth, and the value of data is hard to mine. This brings significant challenges to the efficient management and control of each link in the data life cycle, such as data collection, cleaning, storage, and sharing. Sketch uses a hash table/matrix/bit vector to track the core characteristics of data, such as frequency, cardinality, and membership. This mechanism makes the sketch itself metadata, which has been widely used in sharing, transmission, update, and other scenarios. The rapid flow characteristic of big data has spawned dynamic sketches. The existing dynamic sketches have the advantage of expanding or shrinking the capacity with the size of the data stream by dynamically maintaining a list of probabilistic data structures in a chain or tree structure. However, there are problems with the excessive space overhead and time overhead increasing with the increase in the dataset cardinality. This paper designs a dynamic sketch for big data governance on the basis of the advanced jump consistent hash. This method can simultaneously achieve the space overhead that grows linearly with the dataset cardinality and the constant time overhead of data processing and analysis, effectively supporting the demanding big data processing and analysis tasks for big data governance. The validity and efficiency of the proposed method are verified by the comparison with traditional methods on various synthetic and natural datasets. Reference Related Cited by
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.21655/ijsi.1673-7288.00296
- http://www.ijsi.org/ijsi/article/pdf/296
- OA Status
- hybrid
- References
- 27
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4361799276
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https://openalex.org/W4361799276Canonical identifier for this work in OpenAlex
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https://doi.org/10.21655/ijsi.1673-7288.00296Digital Object Identifier
- Title
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Jump Filter: A Dynamic Sketch for Big Data GovernanceWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-01-01Full publication date if available
- Authors
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Pengtao Fu, Lailong Luo, Deke Guo, Xiang Zhao, Shangsen Li, Huaimin WangList of authors in order
- Landing page
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https://doi.org/10.21655/ijsi.1673-7288.00296Publisher landing page
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https://www.ijsi.org/ijsi/article/pdf/296Direct link to full text PDF
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://www.ijsi.org/ijsi/article/pdf/296Direct OA link when available
- Concepts
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Computer science, Big data, Overhead (engineering), Cardinality (data modeling), Sketch, Hash function, Bloom filter, Data mining, Distributed computing, Database, Theoretical computer science, Algorithm, Computer security, Operating systemTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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27Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| primary_location.raw_source_name | International Journal of Software and Informatics |
| primary_location.landing_page_url | https://doi.org/10.21655/ijsi.1673-7288.00296 |
| publication_date | 2023-01-01 |
| publication_year | 2023 |
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