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Figure 2c shows an example of bitmaps for pattern occurrence lists.
The join operation becomes inefficient when the size of pattern occurrence lists is large.
It defines also a quadratic join operation over pattern occurrence lists to compute support for candidates.
In contrast, HomTreeMiner avoids storing pattern occurrences by storing only bitmaps of occurrence lists which are usually of insignificant size.
As verified by our experimental evaluation, storing the occurrence lists of multiple patterns as bitmaps results in important space savings.
Then, the occurrence list set of a pattern is the set of bitmaps of the occurrence lists of its nodes.
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Thus, we only need to store with each frequent pattern its occurrence list set.
Each node of the patterns (P_1) and (P_2) is associated with its occurrence list together with the corresponding bitmap vector.
The occurrence list (L_X) of X on T is a sublist of the occurrence list (L_{X'}) of (X') on T. Sublist (L_X) is the inverted list of data tree nodes that participate in the occurrences of Q to T. By Proposition 2, X can be computed using (L_{X'}) instead of using the corresponding label inverted list.
Recall that the support of a pattern P in the input data tree T is defined as the size of the occurrence list (L_R) of the root R of P on T (Sect. 2).
The bitmap output associated with each pattern node indicates the occurrence list of that node on T. Note that pattern (Q_2) is refined by (Q_1) and thus will not be further expanded.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com