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This research concerns Learned Data Structures, a recent area that hasemerged at the crossroad of Machine Learning and Classic Data Structures. It ismethodologically important and with a high practical impact. We focus onLearned Indexes, i.e., Learned Sorted Set Dictionaries. The proposals availableso far are specific in the sense that they can boost, indeed impressively, thetime performance of Table Search Procedures with a sorted layout only, e.g.,Binary Search. We propose a novel paradigm that, complementing knownspecialized ones, can produce Learned versions of any Sorted Set Dictionary,for instance, Balanced Binary Search Trees or Binary Search on layouts otherthat sorted, i.e., Eytzinger. Theoretically, based on it, we obtain severalresults of interest, such as (a) the first Learned Optimum Binary SearchForest, with mean access time bounded by the Entropy of the probabilitydistribution of the accesses to the Dictionary; (b) the first Learned SortedSet Dictionary that, in the Dynamic Case and in an amortized analysis setting,matches the same time bounds known for Classic Dictionaries. This latter underwidely accepted assumptions regarding the size of the Universe. Theexperimental part, somewhat complex in terms of software development, clearlyindicates the nonobvious finding that the generalization we propose can yieldeffective and competitive Learned Data Structural Booster, even with respect tospecific benchmark models.

From Specific to Generic Learned Sorted Set Dictionaries: A Theoretically Sound Paradigm Yelding Competitive Data Structural Boosters in Practice