Data Driven Approximation with Bounded Resources
- Submitting institution
-
University of Edinburgh
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 84492498
- Type
- D - Journal article
- DOI
-
10.14778/3099622.3099628
- Title of journal
- Proceedings of the VLDB Endowment (PVLDB)
- Article number
- -
- First page
- 973
- Volume
- 10
- Issue
- 9
- ISSN
- 2150-8097
- Open access status
- Compliant
- Month of publication
- May
- Year of publication
- 2017
- 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
-
1
- Research group(s)
-
C - Foundations of Computation
- Citation count
- 6
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The work proposes the first resource-bounded approximation scheme "for answering most general queries with a deterministic accuracy bound'' and "has been implemented on many industry systems'' (cf. https://link.springer.com/article/10.1007/s41019-018-0074-4). It was published in VLDB, the leading international all-round database conference (average acceptance rate: 16.7%). It provides the overall framework and key components of the system BEAS, which was proven effective in practice and very successful in attracting industrial investments (e.g., the Huawei-Edinburgh joint lab). This work, together with other results in this line of research, constitutes a keynote talk in Workshop on Data Science Theory and Practice at LSE in 2018.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -