Succinct Data Structures for Retrieval and Approximate Membership

TL;DR

This paper introduces space-efficient data structures for retrieval and approximate membership that eliminate previous overheads, achieve near-optimal space usage, and have simple evaluation procedures, with theoretical guarantees on construction and query times.

Contribution

It presents novel succinct data structures for retrieval and approximate membership that remove space overheads and approach theoretical lower bounds, improving efficiency and simplicity.

Findings

Achieves retrieval with space within a factor 1+e^{-k} of optimal for query time O(k).

Eliminates space overhead in approximate membership structures, approaching lower bounds.

Provides expected O(n) construction time and simple evaluation procedures.

Abstract

The retrieval problem is the problem of associating data with keys in a set. Formally, the data structure must store a function f: U ->{0,1}^r that has specified values on the elements of a given set S, a subset of U, |S|=n, but may have any value on elements outside S. Minimal perfect hashing makes it possible to avoid storing the set S, but this induces a space overhead of Theta(n) bits in addition to the nr bits needed for function values. In this paper we show how to eliminate this overhead. Moreover, we show that for any k query time O(k) can be achieved using space that is within a factor 1+e^{-k} of optimal, asymptotically for large n. If we allow logarithmic evaluation time, the additive overhead can be reduced to O(log log n) bits whp. The time to construct the data structure is O(n), expected. A main technical ingredient is to utilize existing tight bounds on the probability of almost square random matrices with rows of low weight to have full row rank. In addition to direct constructions, we point out a close connection between retrieval structures and hash tables where keys are stored in an array and some kind of probing scheme is used. Further, we propose a general reduction that transfers the results on retrieval into analogous results on approximate membership, a problem traditionally addressed using Bloom filters. Again, we show how to eliminate the space overhead present in previously known methods, and get arbitrarily close to the lower bound. The evaluation procedures of our data structures are extremely simple (similar to a Bloom filter). For the results stated above we assume free access to fully random hash functions. However, we show how to justify this assumption using extra space o(n) to simulate full randomness on a RAM.