On generating all maximal independent sets
Information Processing Letters
Probabilistic quantifiers and games
Journal of Computer and System Sciences - Structure in Complexity Theory Conference, June 2-5, 1986
Counting classes are at least as hard as the polynomial-time hierarchy
SIAM Journal on Computing
Fixed-Parameter Tractability and Completeness I: Basic Results
SIAM Journal on Computing
SIAM Journal on Computing
Sequences, datalog, transducers
Journal of Computer and System Sciences - Fourteenth ACM SIGACT-SIGMOD-SIGART symposium on principles of database systems
Counting and random generation of strings in regular languages
Proceedings of the sixth annual ACM-SIAM symposium on Discrete algorithms
On the complexity of database queries
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Querying sequence databases with transducers
Acta Informatica
Off-Line Handwritten Word Recognition Using a Hidden Markov Model Type Stochastic Network
IEEE Transactions on Pattern Analysis and Machine Intelligence
SEQ: A Model for Sequence Databases
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Typechecking Top-Down Uniform Unranked Tree Transducers
ICDT '03 Proceedings of the 9th International Conference on Database Theory
The complexity of relational query languages (Extended Abstract)
STOC '82 Proceedings of the fourteenth annual ACM symposium on Theory of computing
Optimal aggregation algorithms for middleware
Journal of Computer and System Sciences - Special issu on PODS 2001
Evaluating probabilistic queries over imprecise data
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Clique is hard to approximate within n1-
FOCS '96 Proceedings of the 37th Annual Symposium on Foundations of Computer Science
Finite state transducers approximating Hidden Markov Models
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
State complexity of some operations on binary regular languages
Theoretical Computer Science - Descriptional complexity of formal systems
MYSTIQ: a system for finding more answers by using probabilities
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Shallow parsing with conditional random fields
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Finding and approximating top-k answers in keyword proximity search
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The dichotomy of conjunctive queries on probabilistic structures
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Maximally joining probabilistic data
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A transducer-based XML query processor
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Model-driven data acquisition in sensor networks
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Efficient query evaluation on probabilistic databases
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Efficiently enumerating results of keyword search over data graphs
Information Systems
Query efficiency in probabilistic XML models
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Event queries on correlated probabilistic streams
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Approximating predicates and expressive queries on probabilistic databases
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Generating all maximal induced subgraphs for hereditary and connected-hereditary graph properties
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Approximate lineage for probabilistic databases
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A compositional query algebra for second-order logic and uncertain databases
Proceedings of the 12th International Conference on Database Theory
Exploiting Lineage for Confidence Computation in Uncertain and Probabilistic Databases
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Database Support for Probabilistic Attributes and Tuples
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Online Filtering, Smoothing and Probabilistic Modeling of Streaming data
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Access Methods for Markovian Streams
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Probabilistic Inference over RFID Streams in Mobile Environments
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Ef?cient Query Evaluation over Temporally Correlated Probabilistic Streams
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Running tree automata on probabilistic XML
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A Markov sequence is a basic statistical model representing uncertain sequential data, and it is used within a plethora of applications, including speech recognition, image processing, computational biology, radio-frequency identification (RFID), and information extraction. The problem of querying a Markov sequence is studied under the conventional semantics of querying a probabilistic database, where queries are formulated as finite-state transducers. Specifically, the complexity of two main problems is analyzed. The first problem is that of computing the confidence (probability) of an answer. The second is the enumeration of the answers in the order of decreasing confidence (with the generation of the top-k answers as a special case), or in an approximate order thereof. In particular, it is shown that enumeration in any sub-exponential-approximate order is generally intractable (even for some fixed transducers), and a matching upper bound is obtained through a proposed heuristic. Due to this hardness, a special consideration is given to restricted (yet common) classes of transducers that extract matches of a regular expression (subject to prefix and suffix constraints), and it is shown that these classes are, indeed, significantly more tractable.