Continuous Outlier Mining of Streaming Data in Flink
- Submitting institution
-
The University of Manchester
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 174126604
- Type
- D - Journal article
- DOI
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10.1016/j.is.2020.101569
- Title of journal
- Information Systems
- Article number
- 101569
- First page
- -
- Volume
- 93
- Issue
- -
- ISSN
- 0306-4379
- Open access status
- Exception within 3 months of publication
- Month of publication
- May
- Year of publication
- 2020
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
4
- Research group(s)
-
A - Computer Science
- Citation count
- 0
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- "Proposes the first approach to combining massive parallelism and continuous outlier mining in data streams, an important challenge as stream-based outlier mining enables fast responses in real-time healthcare applications (e.g. https://doi.org/10.1145/3381028).
The research contributions were later implemented in the PROUD platform, a product of an EU-H2020 project (Grant-871403), demonstrated at SIGMOD’2020, which uniquely offers performance, scalability, and extensibility for data partitioning and outlier mining, supporting a multitude of applications, and enabling fog computing."
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
