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Posted to commits@datasketches.apache.org by le...@apache.org on 2020/07/15 20:43:13 UTC
[incubator-datasketches-website] branch master updated: Fix typos.
This is an automated email from the ASF dual-hosted git repository.
leerho pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-datasketches-website.git
The following commit(s) were added to refs/heads/master by this push:
new 35f07c7 Fix typos.
35f07c7 is described below
commit 35f07c7f4472550d83b72c53691a5af9fa7975fe
Author: Lee Rhodes <le...@users.noreply.github.com>
AuthorDate: Wed Jul 15 13:42:47 2020 -0700
Fix typos.
---
docs/Theta/KMVempty.md | 2 +-
docs/Theta/ThetaPSampling.md | 2 +-
docs/Tuple/TupleOverview.md | 2 --
3 files changed, 2 insertions(+), 4 deletions(-)
diff --git a/docs/Theta/KMVempty.md b/docs/Theta/KMVempty.md
index 30a0148..e8436da 100644
--- a/docs/Theta/KMVempty.md
+++ b/docs/Theta/KMVempty.md
@@ -23,7 +23,7 @@ layout: doc_page
[Next]({{site.docs_dir}}/Theta/KMVfirstEst.html)
## The KMV Empty Sketch
-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.
+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.
Our objectives are as follows:
diff --git a/docs/Theta/ThetaPSampling.md b/docs/Theta/ThetaPSampling.md
index ce204d7..78ee61f 100644
--- a/docs/Theta/ThetaPSampling.md
+++ b/docs/Theta/ThetaPSampling.md
@@ -21,7 +21,7 @@ layout: doc_page
-->
## Up-Front / p Sampling
-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.
+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.
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).
diff --git a/docs/Tuple/TupleOverview.md b/docs/Tuple/TupleOverview.md
index d102336..a3bc531 100644
--- a/docs/Tuple/TupleOverview.md
+++ b/docs/Tuple/TupleOverview.md
@@ -35,8 +35,6 @@ Tuple Sketches are ideal for summarizing attributes such as impressions or click
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.
-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.
-
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.
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.
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