Succinct Data Structures for Retrieval and Approximate Membership

@inproceedings{Dietzfelbinger2008SuccinctDS,
  title={Succinct Data Structures for Retrieval and Approximate Membership},
  author={Martin Dietzfelbinger and R. Pagh},
  booktitle={ICALP},
  year={2008}
}
The retrieval problemis the problem of associatingdata with keys in a set. Formally, the data structure must store afunction $f\colon U\to \{0,1\}^r$ that has specified values on theelements of a given set S⊆ U, |S|= n, but may have any value on elements outsideS. All known methods (e. g. those based on perfect hashfunctions), induce a space overhead of θ(n)bits over the optimum, regardless of the evaluation time. We showthat for any k, query time O(k) can beachieved using space that is within… 
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77 Citations
Highly Influential Citations
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Background Citations
25
Methods Citations
19
Results Citations
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77 Citations

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Citation Type

Fast Succinct Retrieval and Approximate Membership using Ribbon
TLDR
B bumped ribbon retrieval (BuRR) is presented, the first practical succinct retrieval data structure, which achieves space overheads well below 1 % while being faster than most previously used retrieval data structures (typically with spaceOverheads at least an order of magnitude larger) and faster than classical Bloom filters (with space overhead ≥ 44 %).
Conjunctive Filter: Breaking the Entropy Barrier
TLDR
The objective is to break this entropy bound and construct more space-efficient data structures and show that many problems can be solved by using a conjunctive filter such as full-text search and database join queries.
A Space Lower Bound for Dynamic Approximate Membership Data Structures
An approximate membership data structure is a randomized data structure representing a set which supports membership queries. It allows for a small false positive error rate but has no false negative…
An Optimal Bloom Filter Replacement Based on Matrix Solving
TLDR
This work suggests a method for holding a dictionary data structure, which maps keys to values, in the spirit of Bloom Filters, and suggests a data structure that requires only nk bits space, has O (n) preprocessing time, and has a O (logn ) query time.
A Lower Bound for Dynamic Approximate Membership Data Structures
  • Shachar Lovett, E. Porat
  • Computer Science
    2010 IEEE 51st Annual Symposium on Foundations of Computer Science
  • 2010
TLDR
A new lower bound for the memory requirements of any dynamic approximate membership data structure is shown, which shows that the entropy lower bound cannot be achieved by dynamic data structures for any constant error rate.
Constant-Time Retrieval with O(log m) Extra Bits
TLDR
This paper presents a method for treating the retrieval problem with overhead ε = O((logm)/m), which corresponds to O(1) extra memory words (O(logm) bits), and an extremely simple, constant-time query operation.
Random hypergraphs for hashing-based data structures
TLDR
This thesis examines how hyperedge distribution and load affects the probabilities with which these properties hold and derive corresponding thresholds, and identifies a hashing scheme that leads to a particularly high threshold value in this regard.
Experimental Variations of a Theoretically Good Retrieval Data Structure
TLDR
The practicability of one such theoretically very good proposal that has linear construction time, constant evaluation time and space consumption O(nr) bits is explored, bridging a gap between theory and real data structures.
Tight Bounds for Sliding Bloom Filters
TLDR
This work considers a Sliding Bloom Filter: a data structure that, given a stream of elements, supports membership queries of the set of the last n elements (a sliding window), while allowing a small error probability and a slackness parameter.
How to Approximate a Set without Knowing Its Size in Advance
TLDR
A data structure that uses (1+o(1)n log(1/ε)+O(n log log n) bits of space for approximating any set of any size n, without having to know n in advance is presented.
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