Papers for

database architects

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Approximate distance labels in sparse graphs need large sizes

Hardness Results for Approximate Distance Labeling in Sparse Graphs

Abstract: Distance labeling schemes assign short labels to the vertices of a graph so that distances between pairs of vertices can be recovered, exactly or approximately, from the two corresponding labels alone. For sparse graphs, Kosowski, Uznański, and Viennot [PODC'2019] showed that, assuming the hardness of the Sum-Index communication problem, exact distance labeling for sparse unweighted graphs requires near-linear label size. Their construction also gives lower bounds for stretch $1+o(1)$, but does not rule out substantially smaller labels for stretch $1+\varepsilon$, where $\varepsilon>0$ is fixed. We prove lower bounds for this $(1+\varepsilon)$-stretch regime through a density/stretch tradeoff. For every constant integer $k\ge 1$, we show that any approximate distance labeling scheme with stretch strictly smaller than $1+1/k$ for unweighted graphs with $m=O(n^{1+1/k})$ edges requires maximum label size at least $\mathrm{SumIndex}(n)/2^{O_k(\sqrt{\log n})}$ for infinitely many values of $n$. Thus, under the conjecture that $\mathrm{SumIndex}(n)=n^{1-o(1)}$, the maximum label size must be $n^{1-o(1)}$. The main new ingredient is an unweighted graph construction based on $3$-AP-free sets, which replaces the weighted uniqueness mechanism of the previous Sum-Index reduction by an additive uniqueness mechanism and keeps the relevant critical paths short enough to obtain a constant multiplicative distance gap. The same construction also yields near-tight lower bounds for related $2$-hop structures, including hub labeling schemes, shortcut sets, and approximate distance hopsets.

Mon 2 NovData Structures and Algorithms
The gist
Graphs are structures made of points connected by lines, and sometimes we want to quickly estimate the distance between two points using labels. This paper shows that for certain kinds of sparse graphs, if you want a very accurate estimate of distances (close to exact), you still need fairly large labels. The authors prove this by building special graphs that make it hard to keep labels small while preserving accurate distances. Their work improves on previous results and also applies to related graph tools like hub labeling and shortcut sets.
Open → 2611.02821v1

Random linear hashing behaves like fully random hashing in bin loads

Linear Hashing Is Poissonesque

Abstract: The Poisson approximation theorem states that when $m$ balls are tossed uniformly and randomly into $n$ bins, the distribution of the bin loads can sometimes be approximated by $n$ independent Poisson random variables of mean $m/n$. We show that when the balls are tossed via a random $\mathbb{F}_2$-linear map, the load distribution is still governed by this Poisson distribution, albeit in a (necessarily) weaker manner. This allows us to show that a random linear map behaves like a fully random map in the following ways: 1. $\textbf{Expected loads.}$ We show that the expected value of the $r$th largest bin is governed by the $r$th largest value among $n$ independent Poisson random variables of mean $m/n$. Specializing to the canonical setting of $m=n$ and $r=1$, our bounds show that the expected maximum load when $n$ balls are hashed into $n$ bins by a random $\mathbb{F}_2$-linear map is no more than the corresponding expectation for a fully random map $\textit{plus}$ $\frac{3}2 + o(1)$. Specializing to $m\gg n\log n$, we show that the expected max load is $(1+o(1))m/n$ in this regime. 2. $\textbf{Concentration.}$ When $m\gg n\log (1/ρ)$ balls are tossed into $n$ bins randomly, we expect with $0.99$ probability that $\textit{every}$ density-$ρ$ subset of bins will contain a $(1\pm o(1) )ρ$-fraction of the balls. We show that this remains the case if the $m$ balls are tossed using a random linear map, and our bound matches the $o(1)$-factor produced by the fully random case. This gives a stronger version of the Leftover Hash Lemma for linear maps. An implication of this is that all bins have load $m/n \pm O(\sqrt{(m/n)\log n})$ with probability $0.99$. We obtain these results by showing that the average of a log-potential on the bin loads is upper bounded by the cumulant generating function of a Poisson random variable of mean $m/n$.

Mon 26 OctData Structures and AlgorithmsComputational ComplexityInformation Theory
The gist
When assigning many items into many bins randomly, the way items spread out can often be approximated by a particular mathematical distribution called a Poisson distribution. This paper shows that even when items are assigned using a specific type of random linear function instead of complete randomness, the distribution of how full the bins get still behaves very similarly to the ideal case. The authors prove this by comparing average and maximum bin loads and show that the linear method nearly matches fully random assignments. This finding improves understanding of how certain hashing methods work and how reliable their distribution properties are.
Open → 2610.34750v1

Chordal graphs linked to new coloring conditions for spanning trees

On Hypergraph Colorings and Completely Independent Spanning Trees in Chordal Graphs

Abstract: In this paper, we study the existence problem of completely independent spanning trees (CIST) in chordal graphs through appropriate hypergraph representations and their panchromatic and bipanchromatic colorings. First, we disprove a conjecture stating an exact relationship between the panchromatic number, the bipanchromatic number, and the minimum number of unique colors in an optimal panchromatic coloring of a hypergraph. Then, by relating CIST to panchromatic and bipanchromatic colorings of the associated hypergraphs, we derive structural conditions for their existence in chordal graphs and specifically strictly chordal graphs.

Tue 6 OctDiscrete Mathematics
The gist
The paper looks at how a special kind of network, called chordal graphs, can be better understood using color-coded groups called hypergraphs. The authors show that a previously believed exact relationship about colors doesn’t always hold. They also find new rules that help check when special kinds of spanning trees, called completely independent spanning trees, exist in these networks. This could help understand complex connections more clearly.
Open → 2610.08042v1

KCensus improves geo-replication latency with optimized fast paths

KCensus: Synthesizing Latency-Optimal Consensus Fast Paths (Extended Version)

Abstract: Strongly consistent geo-replication often relies on fast paths to reduce latency in the common case of no failures or contention. Existing fast-path schemes, however, are ad hoc and restrictive: each corresponds to a point in a broad design space shaped by network topology, workload, and latency objective, so no single scheme works best across settings. This paper looks at fast-path schemes from a new perspective, as mechanisms that spread knowledge about proposals. With this view, we identify a fundamental condition on the spread of knowledge for a fast-path scheme to work. We then introduce KCensus, a framework that turns this condition into an optimization problem, synthesizing new fast-path schemes that are optimal for a given setting. We use KCensus to build a geo-replicated key-value store and evaluate it across AWS regions. Our system outperforms competing protocols, with up to 16% lower average latency.

Fri 25 SeptDistributed, Parallel, and Cluster Computing
The gist
Systems that copy data across distant locations need to agree on changes quickly to keep everything consistent. Usually, special shortcuts called fast paths help reduce the waiting time when there are no problems, but existing shortcuts only work well in certain situations. The authors looked deeper at how information about decisions spreads across the network and created KCensus, a tool that finds the best shortcuts for different setups. They built a key-value store using KCensus and showed it runs faster than other methods by up to 16%.
Open → 2609.31302v1

Finite dependence profiles reveal structure in symmetry based data sets

Finite Dependence and Invariance Hierarchies for Finitely Supported Structures

Abstract: Finite support indicates that the dependence is limited, but not how each finite context controls what can be observed. For a data symmetry G \leq Sym(A) and a finitely supported G-set X, we study the finite-dependence profile X^S = {x : Fix_G(S) fixes x}, indexed by finite contexts S \subseteq A. For relations on A^n this yields Boolean algebras B^{(n)}_{G,S} = Inv_n(Fix_G(S)), forming the invariance hierarchy. The hierarchy separates several nominal principles. Its meet law is the relation-algebra form of the Bojanczyk-Klin-Lasota least-support criterion, while level injectivity is fungibility. The independent join law governs composition across unions of contexts; it holds for locally oligomorphic automorphism groups of ultrahomogeneous structures in purely relational languages of arity at most two, but can fail for unary observations over ternary data. Freshness remains valid sort by sort, and its unrestricted some/any form characterizes full equality symmetry among closed groups. With least supports, orbit-finiteness implies finite context levels and a uniform support bound; under local oligomorphicity the converse also holds. Pointwise closure does not change the profile, and standard atomic-site presentations show that suitable ultrahomogeneous structures with the same age have equivalent action topoi, even though their material FSS universes may retain different external cardinal data. Contextual orientability gives a complementary pointed invariant whose threshold is the least support size of choice from unordered pairs. For countable omega-categorical structures with degenerate algebraic closure, the meet law is equivalent to weak elimination of imaginaries.

Mon 14 SeptLogic in Computer Science
The gist
This work looks at how information depends on limited parts of data that has symmetry, like when certain rearrangements do not change what you see. The authors analyze how the context of a small data subset controls what can be observed, forming a hierarchy of ways data can stay the same under these symmetries. They find connections to principles about minimal information needed to support elements in data and how certain symmetries behave in relational structures. Their study helps clarify complex relationships among dependencies, supports, and invariances in structured data.
Open → 2609.15879v1