Transducing Markov Sequences
Summary: Studies Markov-sequence queries via finite-state transducers: computing answer probabilities and top-k/ordered enumeration. Shows sub-exponential-approximate enumeration is intractable (even for fixed transducers), gives a matching heuristic, and isolates tractable regex-like extractor subclasses. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. Benny Kimelfeld (IBM)
- 2. Christopher Ré (University of Wisconsin)
BibTeX Citation
@inproceedings{kimelfeld_pods10,
address = {New York, NY, USA},
series = {{PODS} '10},
title = {{Transducing Markov Sequences}},
url = {https://dl.acm.org/doi/10.1145/1807085.1807090},
doi = {10.1145/1807085.1807090},
booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
publisher = {Association for Computing Machinery},
author = {Kimelfeld, Benny and Ré, Christopher},
year = {2010}
}
Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,431 | Database Principles in Information Extraction | 2014 | PODS | 6.7088601e-05 |
| 8,013 | Optimizing and Parallelizing Ranked Enumeration | 2011 | VLDB | 5.5073426e-05 |
| 8,585 | Probabilistic Management of OCR Data using an RDBMS | 2012 | VLDB | 5.4075213e-05 |
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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