Commented out the Krki benchmarks.
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@ -53,8 +53,8 @@
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\newcommand{\Choline}{\bench{Choline}\xspace}
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\newcommand{\GeneExpr}{\bench{GeneExpression}\xspace}
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\newcommand{\IEProtein}{\bench{IE-Protein\_Extraction}\xspace}
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\newcommand{\Krki}{\bench{Krki}\xspace}
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\newcommand{\KrkiII}{\bench{Krki~II}\xspace}
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%\newcommand{\Krki}{\bench{Krki}\xspace}
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%\newcommand{\KrkiII}{\bench{Krki~II}\xspace}
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\newcommand{\Mesh}{\bench{Mesh}\xspace}
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\newcommand{\Pyrimidines}{\bench{Pyrimidines}\xspace}
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\newcommand{\Susi}{\bench{Susi}\xspace}
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@ -1129,9 +1129,8 @@ data, so memory consumption is a reasonable concern. We evaluate
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JITI's time and space performance on some learning tasks using the
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ALEPH system~\cite{ALEPH}. We use the following datasets:
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%
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% Table~\ref{tab:ilp:time} shows JITI performance.
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The dataset \Krki tries to learn rules from a
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small database of chess end-games; \GeneExpr learns rules for
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%% \Krki which tries to learn rules from a small database of chess end-games;
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\GeneExpr which learns rules for
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yeast gene activity given a database of genes, their interactions, and
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micro-array gene expression data; \BreastCancer processes real-life
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patient reports towards predicting whether an abnormality may be
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@ -1160,17 +1159,17 @@ queries in an extensional database.
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\cline{2-4}
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Benchmark & 1st & JITI &{\bf ratio} \\
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\hline
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\BreastCancer & 1,450 & 88 & 16 \\
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\Carcino & 17,705 & 192 & 92 \\
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\Choline & 14,766 & 1,397 & 11 \\
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\GeneExpr & 193,283 & 7,483 & 26 \\
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\IEProtein & 1,677,146 & 2,909 & 577 \\
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\Krki & 0.3 & 0.3 & 1 \\
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\KrkiII & 1.3 & 1.3 & 1 \\
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\Mesh & 4 & 3 & 1.3 \\
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\Pyrimidines & 487,545 & 253,235 & 1.9 \\
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\Susi & 105,091 & 307 & 342 \\
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\Thermolysin & 50,279 & 5,213 & 10 \\
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\BreastCancer & 1,450 & 88 & $16$ \\
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\Carcino & 17,705 & 192 & $92$ \\
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\Choline & 14,766 & 1,397 & $11$ \\
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\GeneExpr & 193,283 & 7,483 & $26$ \\
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\IEProtein & 1,677,146 & 2,909 & $577$ \\
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%% \Krki & 0.3 & 0.3 & $1$ \\
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%% \KrkiII & 1.3 & 1.3 & $1$ \\
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\Mesh & 4 & 3 & $1.3$ \\
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\Pyrimidines & 487,545 & 253,235 & $1.9$ \\
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\Susi & 105,091 & 307 & $342$ \\
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\Thermolysin & 50,279 & 5,213 & $10$ \\
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\hline
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\end{tabular}
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}
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@ -1187,8 +1186,8 @@ queries in an extensional database.
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666 & 174 & 3,172 & 174 \\
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46,726 & 22,629 & 116,463 & 9,015 \\
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146,033 & 129,333 & 53,423 & 1,531 \\
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678 & 117 & 2,047 & 24 \\
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1,866 & 715 & 2,055 & 26 \\
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%% 678 & 117 & 2,047 & 24 \\
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%% 1,866 & 715 & 2,055 & 26 \\
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802 & 161 & 2,149 & 109 \\
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774 & 218 & 25,840 & 12,291 \\
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5,007 & 2,509 & 4,497 & 759 \\
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@ -1200,12 +1199,14 @@ queries in an extensional database.
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%------------------------------------------------------------------------------
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We compare times for 10 runs of the saturation/refinement cycle of the
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ILP system. Table~\ref{tab:ilp:time} shows time results. The \Krki
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datasets have small search spaces and small databases, so they achieve
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the same performance under both versions: there is no slowdown. The
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\Mesh and \Pyrimidines applications do not benefit much from indexing
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in the database, but they do benefit from indexing in the dynamic
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representation of the search space, as their running times halve.
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ILP system. Table~\ref{tab:ilp:time} shows time results.
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%% The \Krki datasets have small search spaces and small databases, so
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%% they achieve the same performance under both versions: there is no
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%% slowdown.
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The \Mesh and \Pyrimidines applications do not benefit much from
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indexing in the database, but they do benefit from indexing in the
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dynamic representation of the search space, as their running times
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halve.
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The \BreastCancer and \GeneExpr applications use data in
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1NF (that is, unstructured data). The benefit here is mostly from
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@ -1234,7 +1235,7 @@ Because dynamic memory expands and contracts, we chose a point where
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memory usage should be at a maximum. The first two numbers show data
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usage on \emph{static} predicates. Static data-base sizes range from
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146MB (\bench{IE-Protein\_Extraction} to less than a MB
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(\bench{Choline}, \bench{Krki}, \bench{Mesh}). Indexing code can grow
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(\bench{Choline} and \bench{Mesh}). Indexing code can grow
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to be as large as than the original code, as in \Carcino, or
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almost as much, e.g., \bench{IE-Protein\_Extraction}. In most cases
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the YAP \JITI adds at least a third and often a half to the original
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@ -1250,7 +1251,7 @@ usage, but is never dominant.
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This version of ALEPH uses the internal data-base to store the IDB.
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The size of reflects the search space, and is to some extent
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independent of the program's static data, although small applications
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such as \bench{Krki} tend to have a small search space. ALEPH's
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such as \Mesh tend to have a small search space. ALEPH's
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author very carefully designed the system to work around overheads in
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accessing the database, so indexing should not be as critical. The
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low overheads suggest that \JITI is working well, as confirmed in
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