add referesnce to Anderson
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@ -1087,7 +1087,7 @@ applications:
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Benchmark & 1st & JITI &{\bf ratio}& 1st & JITI &{\bf ratio}\\
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Benchmark & 1st & JITI &{\bf ratio}& 1st & JITI &{\bf ratio}\\
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\hline
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\hline
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\sgCyl & 2,864 & 24 &$119\times$& 2,390 & 28 &$85\times$\\
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\sgCyl & 2,864 & 24 &$119\times$& 2,390 & 28 &$85\times$\\
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\muta & 30,057 &16,782 &$179\%$ &26,314 &21,574 &$122\%$ \\
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\muta & 30,057 &16,782 &$1.79\times$ &26,314 &21,574 &$1.22\times$ \\
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\pta & 5,131 & 188 & $27\times$& 4,442 & 279 &$16\times$\\
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\pta & 5,131 & 188 & $27\times$& 4,442 & 279 &$16\times$\\
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\tea &1,478,813 &54,616 & $27\times$& --- & --- & --- \\
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\tea &1,478,813 &54,616 & $27\times$& --- & --- & --- \\
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\hline
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\hline
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@ -1226,10 +1226,10 @@ from one to more than two orders of magnitude.
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%% \item[\Krki] tries to learn rules from a small database of chess end-games;
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%% \item[\Krki] tries to learn rules from a small database of chess end-games;
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\item[\GeneExpr] learns rules for yeast gene activity given a
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\item[\GeneExpr] learns rules for yeast gene activity given a
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database of genes, their interactions, and micro-array gene
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database of genes, their interactions, and micro-array gene
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expression data~\cite{Regulatory@ILP-06};
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expression data; %~\cite{Regulatory@ILP-06};
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\item[\BreastCancer] processes real-life patient reports towards
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\item[\BreastCancer] processes real-life patient reports towards
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predicting whether an abnormality may be
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predicting whether an abnormality may be
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malignant~\cite{DavisBDPRCS@IJCAI-05};
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malignant% ;~\cite{DavisBDPRCS@IJCAI-05-short};
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\item[\IEProtein] processes information extraction from paper
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\item[\IEProtein] processes information extraction from paper
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abstracts to search proteins;
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abstracts to search proteins;
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\item[\Susi] learns from shopping patterns;
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\item[\Susi] learns from shopping patterns;
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@ -1248,21 +1248,21 @@ from one to more than two orders of magnitude.
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\paragraph*{Space performance.}
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\paragraph*{Space performance.}
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Table~\ref{tab:ilp:memory} shows memory usage when using \JITI. The
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Table~\ref{tab:ilp:memory} shows memory usage when using \JITI. The
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table presents data obtained at a point near the end of execution; a
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table presents data obtained at a point near the end of execution;
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point where memory usage should be at or close to the maximum. These
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memory usage should be at or close to the maximum. These applications
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applications use a mixture of static and dynamic predicates and we
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use a mixture of static and dynamic predicates and we show their
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show their memory usage separately. On static predicates, memory usage
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memory usage separately. On static predicates, memory usage varies
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varies widely, from only 10\% to the worst case, \Carcino, where the
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widely, from only 10\% to the worst case, \Carcino, where the index
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index tree takes more space than the original program. Hash tables
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tree takes more space than the original program. Hash tables dominate
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dominate usage in \IEProtein and \Susi, whereas \TryRetryTrust chains
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usage in \IEProtein and \Susi, whereas \TryRetryTrust chains dominate
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dominate in \BreastCancer. In most other cases no single component
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in \BreastCancer. In most other cases no single component dominates
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dominates memory usage. Memory usage for dynamic data is shown in the
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memory usage. Memory usage for dynamic data is shown in the last two
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last two columns; note that dynamic data is mostly used to store the
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columns; note that dynamic data is mostly used to store the search
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search space. One can observe that there is a much lower overhead in
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space. One can observe that there is a much lower overhead in this
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this case. A more detailed analysis shows that most space is occupied
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case. A more detailed analysis shows that most space is occupied by
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by the hash tables and by internal nodes of the tree, and that
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the hash tables and by internal nodes of the tree, and that relatively
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relatively little space is occupied by \TryRetryTrust chains,
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little space is occupied by \TryRetryTrust chains, suggesting that
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suggesting that \JITI is behaving well in practice.
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\JITI is behaving well in practice.
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\section{Concluding Remarks}
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\section{Concluding Remarks}
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@ -1273,14 +1273,14 @@ access large datasets efficiently, we have described a novel but also
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simple idea: \emph{indexing Prolog clauses on demand during program
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simple idea: \emph{indexing Prolog clauses on demand during program
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execution}.
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execution}.
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%
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%
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Given the impressive speedups this idea can provide for many
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Given the impressive speedups this idea can provide for many realistic
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applications, we are a bit surprised similar techniques have not been
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applications, we are a bit surprised similar techniques have not been
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explored before. In general, Prolog systems have been reluctant to
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explored before. In general, Prolog systems have been reluctant to
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perform code optimizations during runtime and our feeling is that LP
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perform code optimizations during runtime and our feeling is that LP
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implementation has been left a bit behind times. We hold that this
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implementation has been left a bit behind. We hold that this
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should change.
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should change.
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%
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%
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Indeed, we see \JITI as only the first, albeit very important, step
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Indeed, we see the \JITI as only a first, very successful, step
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towards effective runtime optimization of logic programs.
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towards effective runtime optimization of logic programs.
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As presented, \JITI is a hybrid technique: index generation occurs
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As presented, \JITI is a hybrid technique: index generation occurs
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@ -1291,11 +1291,14 @@ fully dynamic (as in YAP), combined with user declarations, or use
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static analysis to be even more selective or go beyond fixed-order
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static analysis to be even more selective or go beyond fixed-order
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indexing.
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indexing.
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%
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%
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Finally, note that \JITI fully respects Prolog semantics. Better
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Last, we observe that the \JITI fully respects Prolog semantics. Better
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performance can be achieved in the context of one solution
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performance can be achieved in the context of one solution
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computations, or in the context of tabling where order of clauses and
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computations, or in the context of tabling where order of clauses and
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solutions does not matter and repeated solutions are discarded.
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solutions does not matter and repeated solutions are discarded.
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%
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Finally, the good results obtained with \JITI have motivated recent
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work on taking advantage of Just-In-Time to better compile
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clauses~\cite{yapc}.
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%==============================================================================
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%==============================================================================
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\bibliographystyle{splncs}
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\bibliographystyle{splncs}
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@ -275,7 +275,7 @@ organization = "State University of New York at Stony Brook",
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author = "Zhou, Neng-Fa and Takagi, Toshihisa and Kazuo, Ushijima",
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author = "Zhou, Neng-Fa and Takagi, Toshihisa and Kazuo, Ushijima",
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title = "A Matching Tree Oriented Abstract Machine for {P}rolog",
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title = "A Matching Tree Oriented Abstract Machine for {P}rolog",
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pages = "158--173",
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pages = "158--173",
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booktitle = ICLP90,
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booktitle = "ICLP90",
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editor = "Warren, David H. D. and Szeredi, P\'eter",
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editor = "Warren, David H. D. and Szeredi, P\'eter",
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year = 1990,
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year = 1990,
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publisher = "MIT Press"
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publisher = "MIT Press"
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@ -449,7 +449,7 @@ institution = "Department of Artificial Intelligence, University of Edinburgh"
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author = "Srinivasan, A. and King, R.D. and Muggleton, S. and
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author = "Srinivasan, A. and King, R.D. and Muggleton, S. and
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Sternberg, M.J.E.",
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Sternberg, M.J.E.",
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title = "Carcinogenesis Predictions Using {ILP}",
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title = "Carcinogenesis Predictions Using {ILP}",
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booktitle = ILP97,
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booktitle = "ILP97",
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series = LNAI,
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series = LNAI,
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volume = 1297,
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volume = 1297,
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editor = "D\v{z}eroski, S. and Lavra\v{c}, N.",
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editor = "D\v{z}eroski, S. and Lavra\v{c}, N.",
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@ -576,8 +576,7 @@ institution = "Department of Artificial Intelligence, University of Edinburgh"
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Santos Costa, V\{\i}tor},
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Santos Costa, V\{\i}tor},
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title = {Inferring Regulatory Networks from Time Series Expression Data
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title = {Inferring Regulatory Networks from Time Series Expression Data
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and Relational Data via Inductive Logic Programming},
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and Relational Data via Inductive Logic Programming},
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booktitle = "Proceedings of the Sixteenth International Conference
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booktitle = "ILP07",
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on Inductive Logic Programming",
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location = "Santiago de Compostela, Spain",
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location = "Santiago de Compostela, Spain",
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year = "2007"
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year = "2007"
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}
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}
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@ -603,3 +602,27 @@ institution = "Department of Artificial Intelligence, University of Edinburgh"
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year = {2005}
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year = {2005}
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}
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}
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@inproceedings{DavisBDPRCS@IJCAI-05-short,
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author = {Jesse Davis and
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Elizabeth S. Burnside and
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In{\^e}s Dutra and
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David Page and
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Raghu Ramakrishnan and
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Santos Costa, V\'{\i}tor and
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Jude W. Shavlik},
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title = {View Learning for Statistical Relational Learning: With
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an Application to Mammography},
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pages = "677--683",
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booktitle = "IJCAI05",
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location = "Edinburgh, Scotland, UK",
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isbn = {0938075934},
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year = {2005}
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}
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@inproceedings{yapc,
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author = {Anderson Silva and Santos Costa, V\{\i}tor},
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title = {Design, Implementation, and Evaluation of an Dynamic Compilation Framework for the YAP System.},
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year = {2007},
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booktitle = {Submitted for publication}
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}
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