Storing a Sparse Table with 0(1) Worst Case Access Time
Journal of the ACM (JACM)
Average-case analysis of algorithms and data structures
Handbook of theoretical computer science (vol. A)
Managing gigabytes (2nd ed.): compressing and indexing documents and images
Managing gigabytes (2nd ed.): compressing and indexing documents and images
Membership in Constant Time and Almost-Minimum Space
SIAM Journal on Computing
Low Redundancy in Static Dictionaries with Constant Query Time
SIAM Journal on Computing
Space Efficient Hash Tables with Worst Case Constant Access Time
STACS '03 Proceedings of the 20th Annual Symposium on Theoretical Aspects of Computer Science
Membership in Constant Time and Minimum Space
ESA '94 Proceedings of the Second Annual European Symposium on Algorithms
ESA '01 Proceedings of the 9th Annual European Symposium on Algorithms
Succinct indexable dictionaries with applications to encoding k-ary trees, prefix sums and multisets
ACM Transactions on Algorithms (TALG)
Compact Hash Tables Using Bidirectional Linear Probing
IEEE Transactions on Computers
Optimality in External Memory Hashing
Algorithmica
FOCS '08 Proceedings of the 2008 49th Annual IEEE Symposium on Foundations of Computer Science
Algorithms and Data Structures for External Memory
Algorithms and Data Structures for External Memory
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We present a new static dictionary that is very fast and compact, while also extremely easy to implement. A combination of properties make this algorithm very attractive for applications requiring large static dictionaries: 1 High performance, with membership queries taking O(1)-time with a near-optimal constant. 1 Continued high performance in external memory, with queries requiring only 1-2 disk seeks. If the dictionary has n items in $\left\{ 0, ..., m\!-\!1 \right\}$ and d is the number of bytes retrieved from disk on each read, then the average number of seeks is $\min\left(1.63, 1 + O\left( \frac{\sqrt{n} \log m}{d} \right)\right)$. 1 Efficient use of space, storing n items from a universe of size m in $n \log m - \frac{1}{2} n \log n + O\left(n + \log \log m\right)$ bits. We prove this space bound with a novel application of the Kolmogorov-Smirnov distribution. 1 Simplicity, with a 20-line pseudo-code construction algorithm and 4-line query algorithm.