Data structures for categorical path counting queries
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Publication:2089697
DOI10.1016/j.tcs.2022.10.011OpenAlexW3183422168MaRDI QIDQ2089697
Publication date: 24 October 2022
Published in: Theoretical Computer Science (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.tcs.2022.10.011
data structuresweighted treespath queriescategorical path countingcategorical path range countingcategorical queriescolored queries
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- Linear-space data structures for range frequency queries on arrays and trees
- A framework for succinct labeled ordinal trees over large alphabets
- Approximate colored range and point enclosure queries
- Surpassing the information theoretic bound with fusion trees
- Trans-dichotomous algorithms for minimum spanning trees and shortest paths
- An improved combinatorial algorithm for Boolean matrix multiplication
- Optimal deterministic shallow cuttings for 3-d dominance ranges
- An external memory data structure for shortest path queries
- Succinct indices for path minimum, with applications
- Succinct representations of weighted trees supporting path queries
- Efficient range searching for categorical and plain data
- Counting Colours in Compressed Strings
- Colored Range Searching in Linear Space
- Succinct ordinal trees based on tree covering
- Efficient Colored Orthogonal Range Counting
- Approximate range counting revisited
- Further Results on Generalized Intersection Searching Problems: Counting, Reporting, and Dynamization
- Data Structures for Path Queries
- Succinct Color Searching in One Dimension
- Better Data Structures for Colored Orthogonal Range Reporting
- Speeding up the Four Russians Algorithm by About One More Logarithmic Factor
- Orthogonal range searching on the RAM, revisited
- Multiplying matrices faster than coppersmith-winograd
- Space-Efficient and Fast Algorithms for Multidimensional Dominance Reporting and Counting
- Near-Optimal Range Reporting Structures for Categorical Data
