Efficient and Error-Correcting Data Structures for Membership and Polynomial Evaluation - Computer Science > Data Structures and AlgorithmsReportar como inadecuado




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Abstract: We construct efficient data structures that are resilient against a constantfraction of adversarial noise. Our model requires that the decoder answers mostqueries correctly with high probability and for the remaining queries, thedecoder with high probability either answers correctly or declares -don-tknow.- Furthermore, if there is no noise on the data structure, it answers allqueries correctly with high probability. Our model is the common generalizationof a model proposed recently by de Wolf and the notion of -relaxed locallydecodable codes- developed in the PCP literature.We measure the efficiency of a data structure in terms of its length,measured by the number of bits in its representation, and query-answering time,measured by the number of bit-probes to the possibly corruptedrepresentation. In this work, we study two data structure problems: membershipand polynomial evaluation. We show that these two problems have constructionsthat are simultaneously efficient and error-correcting.



Autor: Victor Chen, Elena Grigorescu, Ronald de Wolf

Fuente: https://arxiv.org/



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