Papers
Topics
Authors
Recent
Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 73 tok/s
Gemini 2.5 Pro 57 tok/s Pro
GPT-5 Medium 26 tok/s Pro
GPT-5 High 24 tok/s Pro
GPT-4o 94 tok/s Pro
Kimi K2 198 tok/s Pro
GPT OSS 120B 454 tok/s Pro
Claude Sonnet 4.5 33 tok/s Pro
2000 character limit reached

Optimal Combinatorial Batch Codes based on Block Designs (1312.5505v2)

Published 19 Dec 2013 in cs.DM and math.CO

Abstract: Batch codes, introduced by Ishai, Kushilevitz, Ostrovsky and Sahai, represent the distributed storage of an $n$-element data set on $m$ servers in such a way that any batch of $k$ data items can be retrieved by reading at most one (or more generally, $t$) items from each server, while keeping the total storage over $m$ servers equal to $N$. This paper considers a class of batch codes (for $t=1$), called combinatorial batch codes (CBC), where each server stores a subset of a database. A CBC is called optimal if the total storage $N$ is minimal for given $n,m$, and $k$. A $c$-uniform CBC is a combinatorial batch code where each item is stored in exactly $c$ servers. A $c$-uniform CBC is called optimal if its parameter $n$ has maximum value for given $m$ and $k$. Optimal $c$-uniform CBCs have been known only for $c\in {2,k-1,k-2}$. In this paper we present new constructions of optimal CBCs in both the uniform and general settings, for values of the parameters where tight bounds have not been established previously. In the uniform setting, we provide constructions of two new families of optimal uniform codes with $c\sim \sqrt{k}$. Our constructions are based on affine planes and transversal designs.

Citations (35)

Summary

We haven't generated a summary for this paper yet.

Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

Don't miss out on important new AI/ML research

See which papers are being discussed right now on X, Reddit, and more:

“Emergent Mind helps me see which AI papers have caught fire online.”

Philip

Philip

Creator, AI Explained on YouTube