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Spanner Evaluation over SLP-Compressed Documents

Summary: Evaluates regular spanners directly on SLP-compressed documents (size s) without decompression, achieving O(s) model-checking/non-emptiness and O(s·|J_M(D)|) time to compute all span-tuples. Enumeration with O(s) preprocessing and O(depth(S)) delay (depth=O(log|D|)) gives logarithmic-in-|D| delay and can outperform uncompressed algorithms for highly compressible data. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
1820
Venue
PODS
Year
2021
Pagerank
6.9690833e-05
Overall Rank
3,563 | 75.22%
DOI
10.1145/3452021.3458325

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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
1,938 Split-Correctness in Information Extraction 2019 PODS 0.00010028895
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