typos
git-svn-id: https://yap.svn.sf.net/svnroot/yap/trunk@1826 b08c6af1-5177-4d33-ba66-4b1c6b8b522a
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@ -1189,13 +1189,13 @@ Table~\ref{tab:ilpmem} discusses the memory cost paid in using
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execution. Because dynamic memory expands and contracts, we chose a
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execution. Because dynamic memory expands and contracts, we chose a
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point where memory usage should be at a maximum. The first two numbers
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point where memory usage should be at a maximum. The first two numbers
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show data usage on \emph{static} predicates. Static data-base sizes
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show data usage on \emph{static} predicates. Static data-base sizes
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range from 146MB (\texttt{IE-Protein|_Extraction} to less than a MB
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range from 146MB (\texttt{IE-Protein\_Extraction} to less than a MB
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(\texttt{Choline}, \texttt{Krki}, \texttt{Mesh}). Indexing code can be
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(\texttt{Choline}, \texttt{Krki}, \texttt{Mesh}). Indexing code can be
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more than the original code, as in \texttt{Mutagenesis}, or almost as
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more than the original code, as in \texttt{Mutagenesis}, or almost as
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much, eg, \texttt{IE-Protein\_Extraction}. In most cases the YAP \JITI
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much, eg, \texttt{IE-Protein\_Extraction}. In most cases the YAP \JITI
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adds at least a third and often a half to the original data-base. A
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adds at least a third and often a half to the original data-base. A
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more detailed analysis shows the source of overhead to be very
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more detailed analysis shows the source of overhead to be very
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different from dataset to dataset. In \texttt{IE-Protein|_Extraction}
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different from dataset to dataset. In \texttt{IE-Protein\_Extraction}
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the problem is that hash tables are very large. Hash tables are also
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the problem is that hash tables are very large. Hash tables are also
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where most space is spent in \texttt{Susi}. In \texttt{BreastCancer}
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where most space is spent in \texttt{Susi}. In \texttt{BreastCancer}
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hash tables are actually small, so most space is spent in
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hash tables are actually small, so most space is spent in
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@ -1209,10 +1209,9 @@ 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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independent of the program's static data, although small applications
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such as \texttt{Krki} do tend to have a small search space. ALEPH's
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such as \texttt{Krki} do 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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author very carefully designed the system to work around overheads in
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accessing the data-base, so indexing should not be as important. In
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accessing the data-base, so indexing should not be as critical. The
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fact, indexing has a much lower space overhead in this case,
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low overheads suggest that the \JITI is working well, as confirmed in
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suggesting it is not so critical. A more detailed analysis shows tha
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a more detailed analysis: most space is spent on hashes tables and on
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indexing is working well: most space is spent on hashes tables and on
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internal nodes of tree, and relatively little space is spent on
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internal nodes of tree, and relatively little space is spent on
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\TryRetryTrust chains.
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\TryRetryTrust chains.
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