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lcwik pushed a commit to branch asf-site
in repository https://gitbox.apache.org/repos/asf/beam-site.git

commit b25b1522e5214e31aaac1b88b3fe2c90e1b41c79
Author: Yueyang Qiu <robiny...@gmail.com>
AuthorDate: Tue Jul 10 13:01:28 2018 -0700

    Fix typos in mobile gaming example
---
 src/get-started/mobile-gaming-example.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/get-started/mobile-gaming-example.md 
b/src/get-started/mobile-gaming-example.md
index f6c6c46..4a289b4 100644
--- a/src/get-started/mobile-gaming-example.md
+++ b/src/get-started/mobile-gaming-example.md
@@ -344,7 +344,7 @@ Below, we'll look at these features in more detail.
 
 Let's suppose scoring in our game depends on the speed at which a user can 
"click" on their phone. `GameStats`'s abuse detection analyzes each user's 
score data to detect if a user has an abnormally high "click rate" and thus an 
abnormally high score. This might indicate that the game is being played by a 
bot that operates significantly faster than a human could play.
 
-To determine whether or not a score is "abnormally" high, `GameStats` 
calculates the average of every score in that fixed-time window, and then 
checks each score individual score against the average score multiplied by an 
arbitrary weight factor (in our case, 2.5). Thus, any score more than 2.5 times 
the average is deemed to be the product of spam. The `GameStats` pipeline 
tracks a list of "spam" users and filters those users out of the team score 
calculations for the team leader board.
+To determine whether or not a score is "abnormally" high, `GameStats` 
calculates the average of every score in that fixed-time window, and then 
checks each individual score against the average score multiplied by an 
arbitrary weight factor (in our case, 2.5). Thus, any score more than 2.5 times 
the average is deemed to be the product of spam. The `GameStats` pipeline 
tracks a list of "spam" users and filters those users out of the team score 
calculations for the team leader board.
 
 Since the average depends on the pipeline data, we need to calculate it, and 
then use that calculated data in a subsequent `ParDo` transform that filters 
scores that exceed the weighted value. To do this, we can pass the calculated 
average to as a [side input]({{ site.baseurl 
}}/documentation/programming-guide/#side-inputs) to the filtering `ParDo`.
 

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