Data structures on event graphs

Authors:
Bernard Chazelle;Wolfgang Mulzer
Affiliations:
Department of Computer Science, Princeton University;Institut für Informatik, Freie Universität Berlin, Germany
Venue:
ESA'12 Proceedings of the 20th Annual European conference on Algorithms
Year:
2012

Citing 7
Cited 0

Randomized algorithms

Randomized algorithms
Optimal prefetching via data compression

Journal of the ACM (JACM)
The discrepancy method: randomness and complexity

The discrepancy method: randomness and complexity
Markov Paging

SIAM Journal on Computing
Self-Organizing Data Structures with Dependent Accesses

ICALP '96 Proceedings of the 23rd International Colloquium on Automata, Languages and Programming
A note on predecessor searching in the pointer machine model

Information Processing Letters
Markov Incremental Constructions

Discrete & Computational Geometry - Special Issue: 24th Annual Symposium on Computational Geometry

Quantified Score

Hi-index	0.00

Visualization

Abstract

We investigate the behavior of data structures when the input and operations are generated by an event graph. This model is inspired by the model of Markov chains. We are given a fixed graph G, whose nodes are annotated with operations of the type insert, delete, and query. The algorithm responds to the requests as it encounters them during a (adversarial or random) walk in G. We study the limit behavior of such a walk and give an efficient algorithm for recognizing which structures can be generated. We also give a near-optimal algorithm for successor searching if the event graph is a cycle and the walk is adversarial. For a random walk, the algorithm becomes optimal.