Asymptotically Optimal Encodings of Range Data Structures for Selection and Top-k Queries
- Submitting institution
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The University of Leicester
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1406
- Type
- D - Journal article
- DOI
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10.1145/3012939
- Title of journal
- ACM Transactions on Algorithms
- Article number
- 28
- First page
- 28
- Volume
- 13
- Issue
- 2
- ISSN
- 1549-6325
- Open access status
- Compliant
- Month of publication
- March
- Year of publication
- 2017
- URL
-
-
- Supplementary information
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https://doi.org/10.1145/3012939
- 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
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4
- Research group(s)
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-
- Citation count
- 2
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Encoding data structures (DS) aim to extract from a dataset the absolute minimum information needed to answer queries correctly. Arguably the first non-trivial encoding DS, this work has inspired several papers — some citing conference versions in ESA'13 and FSTTCS'14 — on related encoding problems (e.g. Jo et al. ISAAC'16, Navarro&Thankachan Algorithmica'17) and privacy-preserving database querying (Bernadini et al. ALENEX'20). Direct follow-ups include: Gawrychowski&Nicholson (ICALP'15) improved our space usage, Gagie et al. (TCS'18), Navarro&Thankachan (TCS'14) and el-Zein et al. (ISAAC'19) applied our results to obtain novel space-efficient DS for e.g. indexing textual data.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -