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Michele Miccinesi

Publications and source records attributed to Michele Miccinesi.

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Certificates for short extending words in a finite automaton

Let $\mathcal A$ be a complete deterministic finite automaton on a state set $Q$ of size $n$ with $k$ letters, and for a proper nonempty subset $S$ of $Q$ let $\mathrm{minext}(S)$ be the length of a shortest word $u$ with $|Su^{-1}|>|S|$, where $Su^{-1}=\{q: q\cdot u\in S\}$. To each state $q$ attach the integer $β^{\ast}_q=\sum_{t=1}^{n-1}k^{\,n-1-t}(\mathrm{indeg}_t(q)-k^{t})$, where $\mathrm{indeg}_t(q)$ counts the pairs $(p,u)$ with $|u|=t$ and $p\cdot u=q$, and let $B(S)=\sum_{q\in S}β^{\ast}_q$. On every synchronizing automaton, $B(S)\ge0$ implies $\mathrm{minext}(S)\le n-1$, so, as $B(Q)=0$, one of $S$ and $Q\setminus S$ extends within $n-1$; when $B(S)>0$ no hypothesis is needed. Kari's Eulerian extension lemma is the case $β^{\ast}=0$, and $β^{\ast}$, like every member of the family $\sum_{t=1}^{n-1}c_tσ_t$, $c_t>0$, vanishes identically if and only if the automaton is Eulerian, where $σ_t(S)=\sum_{q\in S}(\mathrm{indeg}_t(q)-k^{t})$. On strongly connected automata $σ_t(S)/k^{t}$ has Cesàro limit $n\,e(S)/e(Q)-|S|$ for Friedman's weight $e$; that limit certifies singletons but no larger subset in general. The hypothesis $B(S)\ge0$ cannot be relaxed by one integer unit, nor can the constant $n-1$ be improved. A second-moment test on the sizes $|Su^{-1}|$ certifies 60 to 95 percent of the subsets with $B(S)<0$ at $n\le7$. Along non-Eulerian automata whose words of length $n-1$ merge a fraction of the state pairs bounded below, with $\max_q\mathrm{indeg}_{n-1}(q)=o(nk^{n-1})$, it certifies all but a vanishing share of them. The functional $B$ certifies half of the subsets outside $\{B=0\}$. At each subset size coprime to $n$ ($n\ge4$) some synchronizing Eulerian binary automaton attains the constant $n-1$; whether only there is open. No reset bound follows: Černý's automata have subsets not extending within $n-1$.

cs.FL

Superseding traditional indexes by orchestrating learning and geometry

We design the first learned index that solves the dictionary problem with time and space complexity provably better than classic data structures for hierarchical memories, such as B-trees, and modern learned indexes. We call our solution the Piecewise Geometric Model index (PGM-index) because it turns the indexing of a sequence of keys into the coverage of a sequence of 2D-points via linear models (i.e. segments) suitably learned to trade query time vs space efficiency. This idea comes from some known heuristic results which we strengthen by showing that the minimal number of such segments can be computed via known and optimal streaming algorithms. Our index is then obtained by recursively applying this geometric idea that guarantees a smoothed adaptation to the "geometric complexity" of the input data. Finally, we propose a variant of the index that adapts not only to the distribution of the dictionary keys but also to their access frequencies, thus obtaining the first distribution-aware learned index. The second main contribution of this paper is the proposal and study of the concept of Multicriteria Data Structure, namely one that asks a data structure to adapt in an automatic way to the constraints imposed by the application of use. We show that our index is a multicriteria data structure because its significant flexibility in storage and query time can be exploited by a properly designed optimisation algorithm that efficiently finds its best design setting in order to match the input constraints. A thorough experimental analysis shows that our index and its multicriteria variant improve uniformly, over both time and space, classic and learned indexes up to several orders of magnitude.

cs.DS