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B. Riva Shalom

Publications and source records attributed to B. Riva Shalom.

3 recordsLinked to original sources

A Guide to Similarity Measures

Similarity measures play a central role in various data science application domains for a wide assortment of tasks. This guide describes a comprehensive set of prevalent similarity measures to serve both non-experts and professional. Non-experts that wish to understand the motivation for a measure as well as how to use it may find a friendly and detailed exposition of the formulas of the measures, whereas experts may find a glance to the principles of designing similarity measures and ideas for a better way to measure similarity for their desired task in a given application domain.

cs.IR↗

Mind the Gap

We examine the complexity of the online Dictionary Matching with One Gap Problem (DMOG) which is the following. Preprocess a dictionary $D$ of $d$ patterns, where each pattern contains a special gap symbol that can match any string, so that given a text that arrives online, a character at a time, we can report all of the patterns from $D$ that are suffixes of the text that has arrived so far, before the next character arrives. In more general versions the gap symbols are associated with bounds determining the possible lengths of matching strings. Finding efficient algorithmic solutions for (online) DMOG has proven to be a difficult algorithmic challenge. We demonstrate that the difficulty in obtaining efficient solutions for the DMOG problem even, in the offline setting, can be traced back to the infamous 3SUM conjecture. Interestingly, our reduction deviates from the known reduction paths that follow from 3SUM. In particular, most reductions from 3SUM go through the set-disjointness problem, which corresponds to the problem of preprocessing a graph to answer edge-triangles queries. We use a new path of reductions by considering the complementary, although structurally very different, vertex-triangles queries. Using this new path we show a conditional lower bound of $Ω(δ(G_D)+op)$ time per text character, where $G_D$ is a bipartite graph that captures the structure of $D$, $δ(G_D)$ is the degeneracy of this graph, and $op$ is the output size. We also provide matching upper-bounds (up to sub-polynomial factors) for the vertex-triangles problem, and then extend these techniques to the online DMOG problem. In particular, we introduce algorithms whose time cost depends linearly on $δ(G_D)$. Our algorithms make use of graph orientations, together with some additional techniques. Finally, when $δ(G_D)$ is large we are able to obtain even more efficient solutions.

cs.DS↗

Dictionary Matching with One Gap

The dictionary matching with gaps problem is to preprocess a dictionary $D$ of $d$ gapped patterns $P_1,\ldots,P_d$ over alphabet $Σ$, where each gapped pattern $P_i$ is a sequence of subpatterns separated by bounded sequences of don't cares. Then, given a query text $T$ of length $n$ over alphabet $Σ$, the goal is to output all locations in $T$ in which a pattern $P_i\in D$, $1\leq i\leq d$, ends. There is a renewed current interest in the gapped matching problem stemming from cyber security. In this paper we solve the problem where all patterns in the dictionary have one gap with at least $α$ and at most $β$ don't cares, where $α$ and $β$ are given parameters. Specifically, we show that the dictionary matching with a single gap problem can be solved in either $O(d\log d + |D|)$ time and $O(d\log^{\varepsilon} d + |D|)$ space, and query time $O(n(β-α)\log\log d \log ^2 \min \{ d, \log |D| \} + occ)$, where $occ$ is the number of patterns found, or preprocessing time and space: $O(d^2 + |D|)$, and query time $O(n(β-α) + occ)$, where $occ$ is the number of patterns found. As far as we know, this is the best solution for this setting of the problem, where many overlaps may exist in the dictionary.

cs.DS↗