Section: New Results

Data Structure

Quasi-dictionary data structure

Participants : Camille Marchet, Lolita Lecompte, Pierre Peterlongo.

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.

The quasi-dictionary, is freely available at https://github.com/pierrepeterlongo/quasi_dictionary. The associated paper has been published in Discrete Applied Mathematics [17].