SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Learning bias and phonological-rule induction
Computational Linguistics
Predicting users' requests on the WWW
UM '99 Proceedings of the seventh international conference on User modeling
A linear space algorithm for computing maximal common subsequences
Communications of the ACM
Learning Search Control Knowledge: An Explanation-Based Approach
Learning Search Control Knowledge: An Explanation-Based Approach
Learning Subsequential Transducers for Pattern Recognition Interpretation Tasks
IEEE Transactions on Pattern Analysis and Machine Intelligence
An XML query engine for network-bound data
The VLDB Journal — The International Journal on Very Large Data Bases
Optimization Techniques for Data-Intensive Decision Flows
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
WhatNext: A Prediction System for Web Requests using N-gram Sequence Models
WISE '00 Proceedings of the First International Conference on Web Information Systems Engineering (WISE'00)-Volume 1 - Volume 1
Finite-state transducers in language and speech processing
Computational Linguistics
Data integration: the teenage years
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
An expressive language and efficient execution system for software agents
Journal of Artificial Intelligence Research
Learning to optimize plan execution in information agents
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
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Speculative execution of information gathering plans can dramatically reduce the effect of source I/O latencies on overall performance. However, the utility of speculation is closely tied to how accurately data values are predicted at runtime. Caching is one approach that can be used to issue future predictions, but it scales poorly with large data sources and is unable to make intelligent predictions given previously unseen input data, even when there is an obvious relationship between past input and the output it generated. In this paper, we describe a novel way to combine classification and transduction for a more efficient and accurate value prediction strategy, one capable of issuing predictions about previously unseen hints. We show how our approach results in significant speedups for plans that query multiple sources or sources that require multi-page navigation.