Databases Reference
In-Depth Information
FROM (SELECT
re-tweeted_status.user.screen_name as re-tweeted_screen_name,
re-tweeted_status.text,
max(re-tweet_count) as re-tweets
FROM tweets
GROUP BY re-tweeted_status.user.screen_name,
re-tweeted_status.text) t
GROUP BY t.re-tweeted_screen_name
ORDER BY total_re-tweets DESC
LIMIT 10;
The result of this query could be similar to Table
7-2
.
Table 7-2.
Results of tweets count
Re-tweeted_screen_name
Total_re-tweets
Tweet_count
MJ
421
5
SS
324
7
SK
213
12
SM
199
23
JM
287
21
AB
263
15
KA
195
18
DW
86
4
AR
67
6
SP
372
29
From these results, we can see whose tweets are getting seen by the widest audience
and also determine whether these people are communicating on a regular basis or not.
We can use this information to carefully target our messaging.
Measure and Monitor
Having the ability to analyze big data is of limited value if users cannot understand
the analysis. Ultimately, a decision maker, provided with the result of analysis, has
to interpret these results. This interpretation cannot happen in a vacuum. Usually, it
involves examining all the assumptions made and retracing the analysis. Furthermore,
there are many possible sources of error: computer systems can have bugs, models
almost always have assumptions, and results can be based on erroneous data. For all of
these reasons, no responsible user will cede authority to the computer system. Rather the
analyst will try to understand, and verify, the results produced by the computer.
