Calculation of how many users is recorded in a system throughout the day

Hello

We have a relationship with a sizeable Decode statement including the bands up after connection of users for a period of the day. Does so at intervals of 15 minutes.

This is really useful for us because we can see volumes of people by logging into our system during the day. Here's the bohemoth that is our decode the statement:

DECODE(Login Time,( LEAST(GREATEST(Login Time,'00:01'),'00:15') ),'00:15',( LEAST(GREATEST(Login Time,'00:16'),'00:30') ),'00:30',( LEAST(GREATEST(Login Time,'00:31'),'00:45') ),'00:45',( LEAST(GREATEST(Login Time,'00:46'),'01:00') ),'01:00',( LEAST(GREATEST(Login Time,'01:01'),'01:15') ),'01:15',( LEAST(GREATEST(Login Time,'01:16'),'01:30') ),'01:30',( LEAST(GREATEST(Login Time,'01:31'),'01:45') ),'01:45',( LEAST(GREATEST(Login Time,'01:46'),'02:00') ),'02:00',( LEAST(GREATEST(Login Time ,'02:01'),'02:15') ),'02:15',( LEAST(GREATEST(Login Time,'02:16'),'02:30') ),'02:30',( LEAST(GREATEST(Login Time,'02:31'),'02:45') ),'02:45',( LEAST(GREATEST(Login Time,'02:46'),'03:00') ),'03:00',( LEAST(GREATEST(Login Time,'03:01'),'03:15') ),'03:15',( LEAST(GREATEST(Login Time,'03:16'),'03:30') ),'03:30',( LEAST(GREATEST(Login Time,'03:31'),'03:45') ),'03:45',( LEAST(GREATEST(Login Time,'03:46'),'04:00') ),'04:00',( LEAST(GREATEST(Login Time,'04:01'),'04:15') ),'04:15',( LEAST(GREATEST(Login Time ,'04:16'),'04:30') ),'04:30',( LEAST(GREATEST(Login Time,'04:31'),'04:45') ),'04:45',( LEAST(GREATEST(Login Time,'04:46'),'05:00') ),'05:00',( LEAST(GREATEST(Login Time,'05:01'),'05:15') ),'05:15',( LEAST(GREATEST(Login Time,'05:16'),'05:30') ),'05:30',( LEAST(GREATEST(Login Time,'05:31'),'05:45') ),'05:45',( LEAST(GREATEST(Login Time,'05:46'),'06:00') ),'06:00',( LEAST(GREATEST(Login Time,'06:01'),'06:15') ),'06:15',( LEAST(GREATEST(Login Time,'06:16'),'06:30') ),'06:30',( LEAST(GREATEST(Login Time ,'06:31'),'06:45') ),'06:45',( LEAST(GREATEST(Login Time,'06:46'),'07:00') ),'07:00',( LEAST(GREATEST(Login Time,'07:01'),'07:15') ),'07:15',( LEAST(GREATEST(Login Time,'07:16'),'07:30') ),'07:30',( LEAST(GREATEST(Login Time,'07:31'),'07:45') ),'07:45',( LEAST(GREATEST(Login Time,'07:46'),'08:00') ),'08:00',( LEAST(GREATEST(Login Time,'08:01'),'08:15') ),'08:15',( LEAST(GREATEST(Login Time,'08:16'),'08:30') ),'08:30',( LEAST(GREATEST(Login Time,'08:31'),'08:45') ),'08:45',( LEAST(GREATEST(Login Time ,'08:46'),'09:00') ),'09:00',( LEAST(GREATEST(Login Time,'09:01'),'09:15') ),'09:15',( LEAST(GREATEST(Login Time,'09:16'),'09:30') ),'09:30',( LEAST(GREATEST(Login Time,'09:31'),'09:45') ),'09:45',( LEAST(GREATEST(Login Time,'09:46'),'10:00') ),'10:00',( LEAST(GREATEST(Login Time,'10:01'),'10:15') ),'10:15',( LEAST(GREATEST(Login Time,'10:16'),'10:30') ),'10:30',( LEAST(GREATEST(Login Time,'10:31'),'10:45') ),'10:45',( LEAST(GREATEST(Login Time,'10:46'),'11:00') ),'11:00',( LEAST(GREATEST(Login Time ,'11:01'),'11:15') ),'11:15',( LEAST(GREATEST(Login Time,'11:16'),'11:30') ),'11:30',( LEAST(GREATEST(Login Time,'11:31'),'11:45') ),'11:45',( LEAST(GREATEST(Login Time,'11:46'),'12:00') ),'12:00',( LEAST(GREATEST(Login Time,'12:01'),'12:15') ),'12:15',( LEAST(GREATEST(Login Time,'12:16'),'12:30') ),'12:30',( LEAST(GREATEST(Login Time,'12:31'),'12:45') ),'12:45',( LEAST(GREATEST(Login Time,'12:46'),'13:00') ),'13:00',( LEAST(GREATEST(Login Time,'13:01'),'13:15') ),'13:15',( LEAST(GREATEST(Login Time ,'13:16'),'13:30') ),'13:30',( LEAST(GREATEST(Login Time,'13:31'),'13:45') ),'13:45',( LEAST(GREATEST(Login Time,'13:46'),'14:00') ),'14:00',( LEAST(GREATEST(Login Time,'14:01'),'14:15') ),'14:15',( LEAST(GREATEST(Login Time,'14:16'),'14:30') ),'14:30',( LEAST(GREATEST(Login Time,'14:31'),'14:45') ),'14:45',( LEAST(GREATEST(Login Time,'14:46'),'15:00') ),'15:00',( LEAST(GREATEST(Login Time,'15:01'),'15:15') ),'15:15',( LEAST(GREATEST(Login Time,'15:16'),'15:30') ),'15:30',( LEAST(GREATEST(Login Time ,'15:31'),'15:45') ),'15:45',( LEAST(GREATEST(Login Time,'15:46'),'16:00') ),'16:00',( LEAST(GREATEST(Login Time,'16:01'),'16:15') ),'16:15',( LEAST(GREATEST(Login Time,'16:16'),'16:30') ),'16:30',( LEAST(GREATEST(Login Time,'16:31'),'16:45') ),'16:45',( LEAST(GREATEST(Login Time,'16:46'),'17:00') ),'17:00',( LEAST(GREATEST(Login Time,'17:01'),'17:15') ),'17:15',( LEAST(GREATEST(Login Time,'17:16'),'17:30') ),'17:30',( LEAST(GREATEST(Login Time,'17:31'),'17:45') ),'17:45',( LEAST(GREATEST(Login Time 17:46 ') ((, 18:00 ')), 18:00 ', (LESS (LARGER (connection time, 18:01 '), 18:15 '))
),'18:15',( LEAST(GREATEST(Login Time,'18:16'),'18:30') ),'18:30',( LEAST(GREATEST(Login Time,'18:31'),'18:45') ),'18:45',( LEAST(GREATEST(Login Time,'18:46'),'19:00') ),'19:00',( LEAST(GREATEST(Login Time,'19:01'),'19:15') ),'19:15',( LEAST(GREATEST(Login Time,'19:16'),'19:30') ),'19:30',( LEAST(GREATEST(Login Time,'19:31'),'19:45') ),'19:45',( LEAST(GREATEST(Login Time,'19:46'),'20:00') ),'20:00',( LEAST(GREATEST(Login Time,'20:01'),'20:15') ),'20:15',( LEAST(GREATEST(Login Time ,'20:16'),'20:30') ),'20:30',( LEAST(GREATEST(Login Time,'20:31'),'20:45') ),'20:45',( LEAST(GREATEST(Login Time,'20:46'),'21:00') ),'21:00',( LEAST(GREATEST(Login Time,'21:01'),'21:15') ),'21:15',( LEAST(GREATEST(Login Time,'21:16'),'21:30') ),'21:30',( LEAST(GREATEST(Login Time,'21:31'),'21:45') ),'21:45',( LEAST(GREATEST(Login Time,'21:46'),'22:00') ),'22:00',( LEAST(GREATEST(Login Time,'22:01'),'22:15') ),'22:15',( LEAST(GREATEST(Login Time,'22:16'),'22:30') ),'22:30',( LEAST(GREATEST(Login Time ((((,' 22:31 '),' 22:45 ')),' 22:45 ', (LEAST (GREATEST(Login Time,'22:46'),' 23:00 ')),' 23:00 ', (LEAST (GREATEST(Login Time,'23:01'),' 23:15 ')),' 23:15 ', (LEAST (GREATEST(Login Time,'23:16'),' 23:30 ')),' 23:30 ', (LEAST (GREATEST(Login Time,'23:31'),' 23:45 ')),' 23:45 ', (LEAST (GREATEST(Login Time,'23:46'),' 00:00 ')))


Isn't beautiful! I just wanted to share this.

Now, what would be really great, is if we could run off statistics on how many people is connected at some point in the day. We are able to report on the user login time and their time of disconnection in any logon and the band upwards in this way. However, if John D connects to 09:00 and 10:00 out newspapers, we would expect to be counted at 09:00, 09:15, 09:30, 09:45 & 10:00 when he was for that entire period.

We do not know if this is possible, but any help is really appreciated.

Thank you
Lloyd

Hello

I guess you have the logon_time and the logoff_time in your folder and you want to add up the number of users when connected on every quarter of an hour during the day.

To do this, you must use a generator of line because there is a line for once connected to an hour, but it takes five times for each quarter of an hour. You can use a custom line generator folder. It's just a folder that will always return a fixed number of lines. You will need to create a Dumpy field in the custom folder of row_gen to join your existing folder. In this example, I created a dummy scope recurring X. You can then create a calculated item in your folder which also always returns X and join the two files together using this fake field.

The line generator must create a line for each quarter during the day. For example,.

SELECT 'X' model, TO_CHAR (hrs, 'fm00') |': ' | To_char (MN * 15, 'fm00') hhmm
FROM (SELECT rownum-1 hrs OF DOUBLE CONNECTION BY ROWNUM<=24)>
(SELECT rownum-1 mn DOUBLE CONNECTION BY ROWNUM<=4)>

You can reach this custom in your timings folder folder using the dummy field, but also include extra in your workbook join conditions so that there is a line that is generated for each quarter that the user is connected. There is some complexity if the user logs on another day. The join conditions must pick up all the lines power supply line for the duration of the user session. So you need to get conditions like this:

(logon_time<= logoff_time="" --="" logged="" on="" and="" off="" on="" the="" same="">
AND (row_gen.hhmm BETWEEN logon_time AND logoff_time))
OR
(logon_time > logoff_time - logged on and off the different days)
AND (row_gen.hhmm logon_time TO 23:45 ')
GOLD row_gen.hhmm BETWEEN ' 00:00 ' AND logoff_time))

You can then include the row_gen.hhmm report and count the number of users. This will give you the number of users connected to each quarter throughout the day.

By the way you do not have a big decode statement. There are a number of other techniques that you could use as the beginning of the quarter and end of time stored in a separate folder.

Rod West

Tags: Business Intelligence

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