gaps in the planning
I guess it's something for a nice piece of sql analytic. Is this possible in 1 sql Rob?I have a table that contains a list of items to be discussed in a meeting place, while in the same room. For some meetings that a speaker is invited, these meetings have a fixed start time. All meetings have a fixed duration in minutes. Days start with the opening and ending with close. How to generate the start times of the elements that have no defined fixed start time?
input data are as follows:
create table sschedule)
Article varchar2 (10)
, varchar2 (30) days
, fixed_start_time varchar2 (5)
number of minutes
);
insert into sschedule (agenda, minutes, days, fixed_start_time) values ('lunch', 'Mon, Mar, sea, game, Fri', 12:00 ', 60);
insert into sschedule (agenda, minutes, days, fixed_start_time) values ('a', "ma, fr", 10:00 ', 60);
insert into sschedule (agenda, minutes, days, fixed_start_time) values ('b', "Mon, sea", null, 120);
insert into sschedule (agenda, minutes, days, fixed_start_time) values ('opening', 'Mon, Mar, sea, game, Fri',' 08:55 ', 5);
insert into sschedule (agenda, minutes, days, fixed_start_time) values ("close', 'Mon, Mar, sea, game" 20:00 ', 5);
insert into sschedule (agenda, minutes, days, fixed_start_time) values ('close', 'Fri', 16:00 ', 5);
insert into sschedule (agenda, day, fixed_start_time, minutes) values ('c', 'Sun, sea, Fri", null, 20);
insert into sschedule (agenda, day, fixed_start_time, minutes) values (', 'Sun, sea, Fri", null, 20);
insert into sschedule (agenda, minutes, days, fixed_start_time) values ('dinner', "Mon, Mar, sea, game," 18:00 ', 60);
insert into sschedule (agenda, minutes, days, fixed_start_time) values ('e', 'Mar, game, Fri", null, 40);
insert into sschedule (agenda, minutes, days, fixed_start_time) values ('speech', 'Mon',' 09:00 ', 120);
insert into sschedule (agenda, minutes, days, fixed_start_time) values ("bye", "Fri", 14:00 ', 120);
insert into sschedule (agenda, minutes, days, fixed_start_time) values ('f', 'Mar, game, Fri", null, 40);
insert into sschedule (agenda, minutes, days, fixed_start_time) values ('g', "Mon, sea", null, 120);
on Friday, this gives:
MINUTES OF FIXED POINT
---------- ----- ----------
opening at 08:55 5
at 10:00 60
lunch from 12:00 60
to goodbye 14:00 120
close 16:00 5
c 20
d 20
e 40
f 40
e may start at 09:00 the gap between opening and a.
c can be started at 09:40 the gap between e and a.
f may start at 11:00 the gap between an and the lunch.
d may start at 11:40 the gap between the f and the lunch.
most combinations are very legal, as long as the fixed_start_times are in the spotlight.
How can this list be generated in 1 sql?
When one day becomes overloaded elements that fall must get something like "does not fit" to signal that it is too busy that day.
Thank you
Ronald.
Ronald Rood wrote:
Is this possible in 1 sql Rob?
Of course, Ronald ;-)
We didn't describe what algorithm to use, so I used a very simple. It justs awards points at the beginning of the time slots open, the largest that those assigned first of all, for the larger gaps first.
SQL> with schedule_normalized as
2 ( select item
3 , cast(days as varchar2(3)) day
4 , to_date(fixed_start_time,'hh24:mi') start_time
5 , minutes
6 , to_date(fixed_start_time,'hh24:mi')
7 + numtodsinterval(minutes,'minute') end_time
8 from sschedule
9 model
10 return updated rows
11 partition by (item,fixed_start_time,minutes)
12 dimension by (0 i)
13 measures (days)
14 ( days [for i from 1 to regexp_count(days[0],',') + 1 increment 1]
15 = regexp_substr(days[0],'[^,]+',1,cv(i))
16 )
17 )
18 select item
19 , day
20 , to_char(st,'hh24:mi') start_time
21 , to_char(et,'hh24:mi') end_time
22 , minutes
23 , fit
24 from schedule_normalized
25 model
26 partition by (day)
27 dimension by
28 ( row_number() over
29 (partition by day order by start_time nulls first, minutes desc) rn
30 )
31 measures
32 ( item
33 , start_time st
34 , minutes
35 , end_time et
36 , lead(start_time) over
37 (partition by day order by start_time) next_start_time
38 , count(nvl2(start_time,null,1)) over (partition by day) cnt
39 , 0 rn_with_largest_gap
40 , row_number() over
41 (partition by day order by start_time nulls first, minutes desc) rnm
42 , cast(null as varchar2(11)) fit
43 )
44 rules iterate(1000) until (iteration_number + 1 = cnt[1])
45 ( rn_with_largest_gap[1]
46 = max(rnm) keep
47 (dense_rank last order by next_start_time - et nulls first)[any]
48 , fit[iteration_number+1]
49 = case
50 when max(next_start_time - et)[any]
51 < minutes[iteration_number+1]/24/60
52 then 'doesn''t fit'
53 end
54 , st[iteration_number+1]
55 = case
56 when fit[iteration_number+1] is null
57 then et[rn_with_largest_gap[1]]
58 end
59 , et[iteration_number+1]
60 = case
61 when fit[iteration_number+1] is null
62 then et[rn_with_largest_gap[1]]
63 + numtodsinterval(minutes[iteration_number+1],'minute')
64 end
65 , next_start_time[iteration_number+1]
66 = case
67 when fit[iteration_number+1] is null
68 then next_start_time[rn_with_largest_gap[1]]
69 end
70 , next_start_time[rn_with_largest_gap[1]]
71 = case
72 when fit[iteration_number+1] is not null
73 then next_start_time[rn_with_largest_gap[1]]
74 end
75 )
76 order by decode(day,'mon',1,'tue',2,'wed',3,'thu',4,'fri',5)
77 , start_time
78 /
ITEM DAY START END_T MINUTES FIT
---------- --- ----- ----- ---------- -----------
opening mon 08:55 09:00 5
keynote mon 09:00 11:00 120
d mon 11:00 11:20 20
lunch mon 12:00 13:00 60
g mon 13:00 15:00 120
b mon 15:00 17:00 120
diner mon 18:00 19:00 60
c mon 19:00 19:20 20
close mon 20:00 20:05 5
opening tue 08:55 09:00 5
a tue 10:00 11:00 60
lunch tue 12:00 13:00 60
f tue 13:00 13:40 40
e tue 13:40 14:20 40
diner tue 18:00 19:00 60
close tue 20:00 20:05 5
opening wed 08:55 09:00 5
g wed 09:00 11:00 120
lunch wed 12:00 13:00 60
b wed 13:00 15:00 120
d wed 15:00 15:20 20
c wed 15:20 15:40 20
diner wed 18:00 19:00 60
close wed 20:00 20:05 5
opening thu 08:55 09:00 5
lunch thu 12:00 13:00 60
e thu 13:00 13:40 40
f thu 13:40 14:20 40
diner thu 18:00 19:00 60
close thu 20:00 20:05 5
opening fri 08:55 09:00 5
d fri 09:00 09:20 20
c fri 09:20 09:40 20
a fri 10:00 11:00 60
f fri 11:00 11:40 40
lunch fri 12:00 13:00 60
e fri 13:00 13:40 40
bye fri 14:00 16:00 120
close fri 16:00 16:05 5
39 rijen zijn geselecteerd.
And an example with elements that do not match:
SQL> insert into sschedule (item,days, fixed_start_time, minutes) values ('z', 'mon',null, 240);
1 rij is aangemaakt.
SQL> with schedule_normalized as
2 ( select item
3 , cast(days as varchar2(3)) day
4 , to_date(fixed_start_time,'hh24:mi') start_time
5 , minutes
6 , to_date(fixed_start_time,'hh24:mi')
7 + numtodsinterval(minutes,'minute') end_time
8 from sschedule
9 model
10 return updated rows
11 partition by (item,fixed_start_time,minutes)
12 dimension by (0 i)
13 measures (days)
14 ( days [for i from 1 to regexp_count(days[0],',') + 1 increment 1]
15 = regexp_substr(days[0],'[^,]+',1,cv(i))
16 )
17 )
18 select item
19 , day
20 , to_char(st,'hh24:mi') start_time
21 , to_char(et,'hh24:mi') end_time
22 , minutes
23 , fit
24 from schedule_normalized
25 model
26 partition by (day)
27 dimension by
28 ( row_number() over
29 (partition by day order by start_time nulls first, minutes desc) rn
30 )
31 measures
32 ( item
33 , start_time st
34 , minutes
35 , end_time et
36 , lead(start_time) over
37 (partition by day order by start_time) next_start_time
38 , count(nvl2(start_time,null,1)) over (partition by day) cnt
39 , 0 rn_with_largest_gap
40 , row_number() over
41 (partition by day order by start_time nulls first, minutes desc) rnm
42 , cast(null as varchar2(11)) fit
43 )
44 rules iterate(1000) until (iteration_number + 1 = cnt[1])
45 ( rn_with_largest_gap[1]
46 = max(rnm) keep
47 (dense_rank last order by next_start_time - et nulls first)[any]
48 , fit[iteration_number+1]
49 = case
50 when max(next_start_time - et)[any]
51 < minutes[iteration_number+1]/24/60
52 then 'doesn''t fit'
53 end
54 , st[iteration_number+1]
55 = case
56 when fit[iteration_number+1] is null
57 then et[rn_with_largest_gap[1]]
58 end
59 , et[iteration_number+1]
60 = case
61 when fit[iteration_number+1] is null
62 then et[rn_with_largest_gap[1]]
63 + numtodsinterval(minutes[iteration_number+1],'minute')
64 end
65 , next_start_time[iteration_number+1]
66 = case
67 when fit[iteration_number+1] is null
68 then next_start_time[rn_with_largest_gap[1]]
69 end
70 , next_start_time[rn_with_largest_gap[1]]
71 = case
72 when fit[iteration_number+1] is not null
73 then next_start_time[rn_with_largest_gap[1]]
74 end
75 )
76 order by decode(day,'mon',1,'tue',2,'wed',3,'thu',4,'fri',5)
77 , start_time
78 /
ITEM DAY START END_T MINUTES FIT
---------- --- ----- ----- ---------- -----------
opening mon 08:55 09:00 5
keynote mon 09:00 11:00 120
c mon 11:00 11:20 20
lunch mon 12:00 13:00 60
z mon 13:00 17:00 240
diner mon 18:00 19:00 60
d mon 19:00 19:20 20
close mon 20:00 20:05 5
b mon 120 doesn't fit
g mon 120 doesn't fit
opening tue 08:55 09:00 5
a tue 10:00 11:00 60
lunch tue 12:00 13:00 60
e tue 13:00 13:40 40
f tue 13:40 14:20 40
diner tue 18:00 19:00 60
close tue 20:00 20:05 5
opening wed 08:55 09:00 5
b wed 09:00 11:00 120
lunch wed 12:00 13:00 60
g wed 13:00 15:00 120
c wed 15:00 15:20 20
d wed 15:20 15:40 20
diner wed 18:00 19:00 60
close wed 20:00 20:05 5
opening thu 08:55 09:00 5
lunch thu 12:00 13:00 60
e thu 13:00 13:40 40
f thu 13:40 14:20 40
diner thu 18:00 19:00 60
close thu 20:00 20:05 5
opening fri 08:55 09:00 5
c fri 09:00 09:20 20
d fri 09:20 09:40 20
a fri 10:00 11:00 60
f fri 11:00 11:40 40
lunch fri 12:00 13:00 60
e fri 13:00 13:40 40
bye fri 14:00 16:00 120
close fri 16:00 16:05 5
40 rijen zijn geselecteerd.
Groet,
Rob.
Published by: Rob van Wijk on 17-dec-2008 23:59
Removed a useless measure x
Tags: Database
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http://Windows.Microsoft.com/en-us/Windows-8/all-drivers
Method 2:
Try to reinstall the game once more and check if the problem persists. This is to check if the problem caused due to not install the game properly or not.
Step 1: uninstall the game.
See the following Microsoft Help article to uninstall or change a program.
http://Windows.Microsoft.com/en-us/Windows-8/uninstall-change-program
Step 2: install the game using the disc or the installation file you have.
Please reply with the status of the issue so that we can better help you.
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It is advisable to manage the Planning of Essbase studio?
Hello
It is advisable to manage the Planning, Essbase studio or planning 11.1.2.4 web UI?
Now that planning and workspace 11.1.2.4 has the ability to manage and administer the planning, is there a reason to use Essbase Studio?
I guess some may use Essbase Studio to extract of the report, but is - it useful for something else? Thank you.
You would use Essbase Studio to manage the planning, it really has nothing to do with the planning and studio for pure Essbase databases.
If you want to extract in planning, perhaps you should look at FDMEE
See you soon
John
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