# A Framework for Building Data Structures from Communication Protocols

arXiv: https://arxiv.org/abs/2506.20761 · HTML: https://mlsanity.com/paper/2506.20761/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jun 2025.

## Built on by (most cited first, 1 of 1)

- [Learning Partition Trees for Nearest Neighbor Search](https://mlsanity.com/paper/2607.09909/) (2026)

## Abstract (arXiv)

We present a general framework for designing efficient data structures for high-dimensional pattern-matching problems (exists ;? i∈[n], f(xi,y)=1) through communication models in which f(x,y) admits sublinear communication protocols with exponentially-small error. Specifically, we reduce the data structure problem to the Unambiguous Arthur-Merlin (UAM) communication complexity of f(x,y) under product distributions. We apply our framework to the Partial Match problem (a.k.a, matching with wildcards), whose underlying communication problem is sparse set-disjointness. When the database consists of n points in dimension d, and the number of star's in the query is at most w = clog n ;(ll d), the fastest known linear-space data structure (Cole, Gottlieb and Lewenstein, STOC'04) had query time t ≈ 2w = nc, which is nontrivial only when c<1. By contrast, our framework produces a data structure with query time n1-1/(c log2 c) and space close to linear. To achieve this, we develop a one-sided ε-error communication protocol for Set-Disjointness under product distributions with tildeTheta(√dlog(1/ε)) complexity, improving on the classical result of Babai, Frankl and Simon (FOCS'86). Building on this protocol, we show that the Unambiguous AM communication complexity of w-Sparse Set-Disjointness with ε-error under product distributions is tildeO(√w log(1/ε)), independent of the ambient dimension d, which is crucial for the Partial Match result. Our framework sheds further light on the power of data-dependent data structures, which is instrumental for reducing to the (much easier) case of product distributions.