# A resource-frugal probabilistic dictionary and applications in bioinformatics

## TL;DR

This paper introduces a resource-efficient probabilistic dictionary using minimal perfect hash functions for large-scale data indexing, demonstrating significant improvements in bioinformatics applications like sequence similarity computation.

## Contribution

It presents a novel probabilistic data structure that outperforms traditional hash tables in construction, query speed, and memory for static large datasets, with practical bioinformatics applications.

## Key findings

- Outperforms hash tables in speed and memory for static datasets
- Enables scalable similarity computations in bioinformatics
- Achieves higher recall in large sequence datasets

## Abstract

Indexing massive data sets is extremely expensive for large scale problems. In many fields, huge amounts of data are currently generated, however extracting meaningful information from voluminous data sets, such as computing similarity between elements, is far from being trivial. It remains nonetheless a fundamental need. This work proposes a probabilistic data structure based on a minimal perfect hash function for indexing large sets of keys. Our structure out-compete the hash table for construction, query times and for memory usage, in the case of the indexation of a static set. To illustrate the impact of algorithms performances, we provide two applications based on similarity computation between collections of sequences, and for which this calculation is an expensive but required operation. In particular, we show a practical case in which other bioinformatics tools fail to scale up the tested data set or provide lower recall quality results.