TM1 - linking measures to dimensions
I have two cubes in TM1, and I am trying to source data from one cube by linking a calculated 'Age' field in the target cube to an 'Age' dimension in the source cube. However, while I can do this fine by writing code in the rules editor, I cannot work out how to do it using the rules Wizard. Unfortunately, policy in my company is that all TM1 models must be based around wizard-based rules, so I am hoping someone could explain how to do this via the wizard.
Cube 1 (the source) contains data on how quickly a loan balance reduces due to customer attrition, based on the loan's age in months - it looks a bit like this:
Age | Attrition %
-------|--------------
1 | 5%
2 | 6%
3 | 7%
Cube 2 (the target) contains a loan balance, and calculates how the balance reduces over several months, based on the data in Cube 1. It looks up the data in cube 1, based on the age that is calculated in the first row of Cube 2, on the basis of:
current month - start month + 1.
So if we assume the loan started in July, for August the age would be:
8 - 7 + 1 = 2 months old.
For the loan starting in July, Cube 2 would look a bit like this:
| Jul | Aug | Sep |
----------------|-------------------
Age | 1 | 2 | 3 |
Opening Balance | $100| $95 | $89 |
Attrition % | 5% | 6% | 7% | <-- sourced from Cube 1 on basis of Age
Attrition $ | -$5 |-$5.7|-$6.3|
Closing Balance | $95| $89 | $83 |
Creating this link is trivial in Excel, but whenever I try to do it using the TM1 Rules Wizard, I run into the problem that TM1 does not seem to allow the linking of a dimension ('Age' in cube 1) to a field within a dimension ('Age' in cube 2).
Can anyone advise?
Related
I have data that looks like this
+-----------+-------------+----------+------------+------------+
| Date | Time | Initials | Location 1 | Location 2 |
+-----------+-------------+----------+------------+------------+
| 8/26/2019 | 11:00:00 AM | BI | 39 | 40 |
| 8/26/2019 | 1:30:00 PM | Kk | 12 | 2 |
| 8/27/2019 | 2:30:00 PM | BH | 18 | 37 |
| 8/28/2019 | 3:30:00 AM | BH | 23 | 29 |
+-----------+-------------+----------+------------+------------+
The output should be something very similar to the Google Maps "Popular Times" graph.
I would like to be able to visualize
A graph for each location in this style (attendance over time via hour), which is the average attendance per day of the week
I would also like to be able to specify a given date ex: 8/26/2019 and pull up the exact data for that date
So I figure either there can be a different graph for every location, or maybe have the various locations data show as different colored bars on the graph.
Ultimately I have this data in a spreadsheet and I'm not sure what would even be the best tool to use to report this data. I looked into data studio and google analytics and just using charts inside the sheet.
However the issue seems to be:
Since the data can be both various dates and various times. I'm not sure how or which tools to use to group the data by a given day, or average the data for a given day of the week. I tried using pivot tables but I'm not sure how to report based on that.
which tools to use to group the data by a given day, or average the data for a given day of the week
=QUERY(QUERY(A2:E,
"select A,count(A),sum(D),sum(E),sum(D)+sum(E),avg(D),avg(E),avg(D)+avg(E),max(D)+max(E),min(D)+min(E)
where A is not null
group by A", 0),
"offset 1", 0)
=QUERY(A2:E,
"select A,count(A),sum(D),sum(E),sum(D)+sum(E),avg(D),avg(E),avg(D)+avg(E),max(D)+max(E),min(D)+min(E)
where A is not null
group by A
pivot C", 0)
need to figure out how to take this input and arrange by Day of the week
=ARRAYFORMULA(IF(A2:A, TEXT(A2:A, "ddd"), ))
Also by hour instead of just by date
=ARRAYFORMULA(IF(A2:A, TEXT(TIME(HOUR(B2:B), 0, 0), "hh:mm:ss"), ))
I am looking for a way to sample data using 2 different criterias, is there anyone who can assist?
I have this that that I have clean with 2000 records. I would like to sample 100 clients distributed at 80% employed and 20 % self employed, furthermore on I have to apply another criteria. Each of the employed and self_employed sample will have to be further distributed by profession, 20% Lawyers, 10% Doctors, 50% Engineers and 20% Accountants.
this is what the data looks like:
Client ID | self employed | Profession
123456 | yes |lawyer
123457 | no |doctor
123458 | yes |accountant
123459 | yes |accountant
123460 | yes |engineer
123461 | yes |lawyer
123462 | no |engineer
123456 | yes |doctor
123456 | yes |lawyer
123456 | yes |engineer
I can't help with the SQL, but the basic idea is straightforward. You need to cross the categories of employment by the professions, with the desired percentages in the margins. Then fill out the table by multiplying the row and column percentages:
employed | unemployed
-------- | -----------
Lawyer | 16% | 4% | 20%
Doctor | 8% | 2% | 10%
Engineer | 40% | 10% | 50%
Accountant | 16% | 4% | 20%
-------- -----------
80% 20%
The entries in the table are what percentage of each crossed category you want in your sample. Since you want a total sample size of 100, multiply each percentage by 100 to get the desired sample size. Given your stated proportions, you want 16 employed lawyers, 4 unemployed lawyers, 8 employed doctors, etc.
Divide your data into subsets corresponding to the 8 categories, and randomly select the appropriate number from each subset. I don't know if SQL provides a random shuffling capability, but if so that's an easy way to select the sample without replacement. Shuffle the employed lawyers and take the first 16, shuffle the unemployed lawyers and take the first 4, and so on. Note that this presumes that each category has enough elements to supply the desired size sample.
I've a fact table that details individual line amounts for orders placed by my organisation. In this fact, at line level, I've included the total order amount to be used, as it's possible we might need that level of detail at some point.
Here's an example of what I've got:-
+------------+------------+---------------+------------+---------------------+
| BookingKey | Booking_ID | Category_FKey | Line_Value | Total_Booking_Value |
+------------+------------+---------------+------------+---------------------+
| 1 | 12 | 8 | 150 | 700 |
| 2 | 12 | 4 | 150 | 700 |
| 3 | 12 | 5 | 300 | 700 |
| 4 | 12 | 4 | 100 | 700 |
+------------+------------+---------------+------------+---------------------+
As you can see, the Total_Booking_Value here is the sum of the Line_Value for the booking in the example (Booking_ID = 12).
The Category_FKey looks up to a Categories dimension.
Using this structure I've created a simple cube and this works fine, mainly.
The issue I have is that I'd like to be able to view the Total Line_Value amount, and somehow include the Total_Booking_Value alongside it.
So, for example I might add the Categories dimension as a filter and want to filter by say Category_FKey = 4.
If this was the case I'd want the aggregates to tell me that the total Line_Value was 250 (for BookingKeys 2 and 4), and the Total_Booking_Value should be 700. Using normal aggregation (ie SUM) I'm getting the Total_Booking_Value as 1400 (obviously - because it's adding 700 * 2 for the two rows the cube would return).
So, the way I see it I'd like to create an MDX calculation that somehow takes the Total_Booking_Value and gives just the value for the Booking in question.
Should this be done using some kind of average, or division by the Distinct number of items? I can't figure this out. I tried something like this:-
create member currentcube.measures.[Calculated Booking Value]
as
[Measures].[Total_Booking_Value] / count(Measures.Booking_ID);
But this isn't working.
Hopefully this makes sense and you can point me in the right direction.
I find it strange that booking_ID is a measure - intuitively it strikes me as something that would be an attribute and therefore a hierarchy - in which case you'd be able to do the count like this:
[Measures].[Total_Booking_Value]
/
COUNT(EXISTING [Booking].[Booking_ID].[Booking_ID].members)
A straightforward solution would be to have two fact tables: one with granularity booking key and one with granularity booking id. The first would contain all columns except total booking value, and the second would contain columns booking id and total booking value.
Then each of both measures would easily be summable.
The reference type between the second fact table and the category dimension could be configures as many-to-many via the first fact table. Thus, you would see the full values of the involved bookings for each selected category, automatically eliminating double counting.
After digging the net for days with no luck, I hope to find the answer here.
I have a simple ms access 2016 database and I have a table with Period as one field and Serial as a second field.
Period is a number that represents a year.
I would like to make Serial a calculated field with the following formula:
Nz(DMax("[Serial]","[Table1]","[Period] = " & Year(Date())),0) + 1
I am trying to get a result similar to:
Period | Serial
------------------
2016 | 1
2016 | 2
2016 | 3
2017 | 1
2017 | 2
2018 | 1
That is a serial number that should be reset every new year.
However, Access seems not to like DMax and it issues the following error message:
The expression .... cannot be used in a calculated column.
Is there an acceptable way to make it?
I am currently working on a school management system but can't seem to figure out the best way to design my student attendance table.
INFO
School is for 14 weeks and class holds 5 times a week. Students in the school can be up to 2000 per term. Meaning attendance can be up to 14 x 5 x 2000 = 140, 000 per term.
I am developing the application for a desktop using VB.Net and MS Access.
PROGRESS SO FAR
I have so far designed something that I am skeptic about.
table name: attendance
_____________________________________________
| id |std_id | att_week | att_date | status |
''''''''''''''''''''''''''''''''''''''''''''''
| 1 | 0001 | 1 |29/9/2015 | yes |
''''''''''''''''''''''''''''''''''''''''''''''
| 2 | 0002 | 1 |29/9/2015 | yes |
''''''''''''''''''''''''''''''''''''''''''''''
I easily found out that designing it like this can easily yield 140, 000 rows in a term.
I also thought of making the week days as column names, that will easily result in 14 x 5 = 70 columns.
What is the best way to design this said table.
Friend I think you should construct your table like this:
Table would accept only the absentees
id student_id class date
________________________________________
1 11 7a 11/11/2020
2 21 6b 10/12/2020
and so on.....
You could easily retrieve details like
1] total absentees per class
2] total absent of a student in date range
3] Per day report of attendance of student can be easily prepared based on this data
ALSO this would be extremly fast due to less number of record and if you index on class_id and and partition tables in specified date range.
Thank You!