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commit ff93de305b4d321f8a627e3595bdd57762aa3eb3
Author: buildbot <[email protected]>
AuthorDate: Wed Dec 3 01:38:23 2025 +0000
Automatic Site Publish by Buildbot
---
output/docs/Community/Research.html | 3 ++-
output/docs/Frequency/FrequentDistinctTuplesSketch.html | 4 ++--
2 files changed, 4 insertions(+), 3 deletions(-)
diff --git a/output/docs/Community/Research.html
b/output/docs/Community/Research.html
index 226f15f3..cfb75b4f 100644
--- a/output/docs/Community/Research.html
+++ b/output/docs/Community/Research.html
@@ -409,7 +409,8 @@ All algorithms in the library produce mergeable summaries,
and come with formal
<h2 id="references">References</h2>
-<p><strong>[ABL+17]</strong> Daniel Anderson, Pryce Bevan, Kevin J. Lang, Edo
Liberty, Lee Rhodes, and Justin Thaler. A high-performance algorithm for
identifying frequent items in data streams. In <em>ACM IMC 2017 (To
Appear)</em>, 2017. <a href="https://arxiv.org/abs/1705.07001";>Preliminary
paper</a>.</p>
+<p><strong>[ABL+17]</strong> Daniel Anderson, Pryce Bevan, Kevin J. Lang, Edo
Liberty, Lee Rhodes, and Justin Thaler. A high-performance algorithm for
identifying frequent items in data streams. In <em>ACM IMC 2017</em>, 2017.
+(dl.acm.org),(arxiv.org/abs/1705.07001).</p>
<p><strong>[AC+13]</strong> Pankaj K. Agarwal, Graham Cormode, Zengfeng Huang,
Jeff M. Phillips, Zhewei Wei, Ke Yi. Mergeable summaries. In <em>ACM Trans.
Database Syst.</em> 38(4): 26:1-26:28, 2013</p>
diff --git a/output/docs/Frequency/FrequentDistinctTuplesSketch.html
b/output/docs/Frequency/FrequentDistinctTuplesSketch.html
index ddda3ce3..82fceb32 100644
--- a/output/docs/Frequency/FrequentDistinctTuplesSketch.html
+++ b/output/docs/Frequency/FrequentDistinctTuplesSketch.html
@@ -503,9 +503,9 @@ while (itr.hasNext()) {
<h3 id="error-behavior">Error Behavior</h3>
<p>Note: the code for the following study can be found in the characterization
repository
-<a
href="https://github.com/DataSketches/characterization/tree/master/src/main/java/org/apache/datasketches/characterization/fdt";>here</a>
and the configuration file can be found <a
href="https://github.com/DataSketches/characterization/tree/master/src/main/resources/fdt";>here</a>.</p>
+<a
href="https://github.com/apache/datasketches-characterization/tree/master/java-base/src/main/java/org/apache/datasketches/characterization/fdt";>here</a>
and the configuration file can be found <a
href="https://github.com/apache/datasketches-characterization/blob/master/java-base/src/main/resources/fdt/FdtAccuracyJob.conf";>here</a>.
A login to GitHub will be required.</p>
-<p>In order to study the error behavior of this sketch a power-law
distribution with a slope of -1 was created. The head of the distribution was a
single item with a cardinality of 16384, and the tail of the distribution was
16384 items each with a cardinality of one. All the points inbetween were items
that have multiplicities and cardinalities that would fall on a straight line
plotted on a Log-X, Log-Y graph. This generated an input stream of about 850K
(Key, value) pairs, which was i [...]
+<p>In order to study the error behavior of this sketch a power-law
distribution with a slope of -1 was created. The head of the distribution was a
single item with a cardinality of 16384, and the tail of the distribution was
16384 items each with a cardinality of one. All the points in between were
items that have multiplicities and cardinalities that would fall on a straight
line plotted on a Log-X, Log-Y graph. This generated an input stream of about
850K (Key, value) pairs, which was [...]
threshold of 1% and a target RSE of 5%.</p>
<p>Twenty such trials were run and the error distribution quantiles of the
results were computed and is shown in the following graph.</p>
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