This is an automated email from the ASF dual-hosted git repository.
git-site-role pushed a commit to branch asf-site
in repository
https://gitbox.apache.org/repos/asf/incubator-datasketches-website.git
The following commit(s) were added to refs/heads/asf-site by this push:
new f80853a Automatic Site Publish by Buildbot
f80853a is described below
commit f80853a54b14b2acf63dfb5ac0e900e092aaf7ba
Author: buildbot <[email protected]>
AuthorDate: Wed Jul 15 20:43:30 2020 +0000
Automatic Site Publish by Buildbot
---
output/docs/Theta/KMVempty.html | 2 +-
output/docs/Theta/ThetaPSampling.html | 2 +-
output/docs/Tuple/TupleOverview.html | 2 --
3 files changed, 2 insertions(+), 4 deletions(-)
diff --git a/output/docs/Theta/KMVempty.html b/output/docs/Theta/KMVempty.html
index b460405..2c972fd 100644
--- a/output/docs/Theta/KMVempty.html
+++ b/output/docs/Theta/KMVempty.html
@@ -507,7 +507,7 @@
<a href="/docs/Theta/KMVfirstEst.html">Next</a></p>
<h2 id="the-kmv-empty-sketch">The KMV Empty Sketch</h2>
-<p>To explain how a simple sketch works, let us start with the well-known <i>k
Minimum Value</i> or <i>KMV</i> sketch in its empty state.</p>
+<p>To explain how a simple Theta sketch works, let us start with the
well-known <i>k Minimum Value</i> or <i>KMV</i> sketch in its empty state.</p>
<p>Our objectives are as follows:</p>
diff --git a/output/docs/Theta/ThetaPSampling.html
b/output/docs/Theta/ThetaPSampling.html
index b7f8887..d92d4d5 100644
--- a/output/docs/Theta/ThetaPSampling.html
+++ b/output/docs/Theta/ThetaPSampling.html
@@ -505,7 +505,7 @@
-->
<h2 id="up-front--p-sampling">Up-Front / p Sampling</h2>
-<p>The up-front / p-sampling option of the Theta Sketches exists to address
the system-level storage allocation challenge when dealing with highly
partitioned/fragmented, massive data that inherently has a long-tail
distribution across all the fragments.</p>
+<p>The up-front / p-sampling option of the Theta Sketches exists to address
the system-level storage allocation challenge when dealing with highly
partitioned/fragmented massive data that inherently has a long-tail
distribution across all the fragments.</p>
<p>Partitioning of Big Data into a large number of fragments will often reveal
that the incoming data has a long tail (or, more precisely, a power-law
distribution).</p>
diff --git a/output/docs/Tuple/TupleOverview.html
b/output/docs/Tuple/TupleOverview.html
index 0826016..dd4e097 100644
--- a/output/docs/Tuple/TupleOverview.html
+++ b/output/docs/Tuple/TupleOverview.html
@@ -519,8 +519,6 @@
<p>Summary Objects are class extensions of the generic base classes in the
library. It is up to the developer of the extension how the summary fields are
defined and how they should be combined during updates or during set
operations.</p>
-<p>Because the distribution of the attribute values is not known, it is not
possible to provide meaningful error bounds on the projections of the attribute
mean or variance onto the raw population.</p>
-
<p>Keep in mind that all of these operations are stream-based. The raw data
from which these sketches are built only needs to be touched once.</p>
<p>The Tuple Sketches also provide sufficient methods so that user could
develop a wrapper class that could facilitate approximate joins or other common
database operations. This concept is illustrated in this next diagram.</p>
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]