Affiliation:
1. Università di Roma “La Sapienza”, Roma, Italy
Abstract
Data stream processing has recently received increasing attention as a computational paradigm for dealing with massive data sets. Surprisingly, no algorithm with both sublinear space and passes is known for natural graph problems in classical read-only streaming. Motivated by technological factors of modern storage systems, some authors have recently started to investigate the computational power of less restrictive models where writing streams is allowed. In this article, we show that the use of intermediate temporary streams is powerful enough to provide effective space-passes tradeoffs for natural graph problems. In particular, for any space restriction of
s
bits, we show that single-source shortest paths in directed graphs with small positive integer edge weights can be solved in
O
((
n
log
3/2
n
)/√
s
) passes. The result can be generalized to deal with multiple sources within the same bounds. This is the first known streaming algorithm for shortest paths in directed graphs. For undirected connectivity, we devise an
O
((
n
log
n
)/
s
) passes algorithm. Both problems require Ω(
n
/
s
) passes under the restrictions we consider. We also show that the model where intermediate temporary streams are allowed can be strictly more powerful than classical streaming for some problems, while maintaining all of its hardness for others.
Publisher
Association for Computing Machinery (ACM)
Subject
Mathematics (miscellaneous)
Cited by
19 articles.
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1. Graph Stream Sketch: Summarizing Graph Streams With High Speed and Accuracy;IEEE Transactions on Knowledge and Data Engineering;2023-06-01
2. A One Pass Streaming Algorithm for Finding Euler Tours;Theory of Computing Systems;2022-12-12
3. Algorithms for Big Data Problems in de Novo Genome Assembly;Lecture Notes in Computer Science;2022
4. Graph Connectivity in Log Steps Using Label Propagation;Parallel Processing Letters;2021-12
5. Position paper;Proceedings of the 4th ACM SIGMOD Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA);2021-06-20