I'm currently designing a website which can help my rowing team plan training times and such. The basic idea is that every rower can set the times they can train. Coaches can then see the availability of all the rowers in a handy table and can use this to plan a training.
My question is, how should I represent availability in the class diagram and database?
The idea that I had was to divide days into time blocks: Block 1 stands for 7:00 - 7:30, block 2 stand for 7:30 - 8:00. Then I will create a table 'timeblocks' with the following attributes:
block_id
user_id
date (day, month and year)
block_number
availability
Is this a efficient way of storing availability data?\
Another way you can normalization this table into two piece. a special block table and availability table.
block :
Block_id
block_range
Time_Block
Time_blockId
Block_ID
user_ID
Date
Availability
Related
my question doesn't regard any particular software, it's more of a broad question that could concern every type of data mining problem.
I have a data set with daily data and a bunch of attributes, like the above. 'Sales' is numeric and represents the revenue of sales on a given day. 'Open' is categorical and retrieves if a store is open (=1) or closed (=0). And 'Promo' is categorical, stating if a type of promo is happening at the given day (it takes the values a, b and c).
day
sales
open
promo
06/12/2022
15
1
a
05/12/2022
0
0
a
04/12/2022
12
1
b
Now, my goal is to develop a model that predicts weekly sales. In order to do this, I will need to aggregate daily data into weekly data.
For the variable sales this is quite straight forward because the value of weekly sales is the sum of daily sales within a certain week.
My question regards the categorical variables (open and promo), what kind of aggregation function should I use? I have tried to convert the variables to numerical and use the weekly mean as an aggregation method for this attributes, but i don't know if this is a common approach.
I would like to know if anyone knows what is the best/usual way to tackle this?
Thanks, anyway!
I am writing a rails app that deals with product inventory. I would like to include the following features, and am struggling with developing an efficient algorithm:
View stock history (how many were in stock on each date)
Quantity removed from warehouse, and quantity added to warehouse over specific periods of time
Amount of time the product was out of stock in any given period
My questions are as follows:
What is the best way of tracking changes? In addition to my Products
table, should I create another table called
HistoricProductQuantities, and insert a new record each time there
is a change in the quantity?
What number should I track? The historic stock quantity (i.e. 50 in
stock on this day, 24 in stock on that day), or the CHANGE in stock
quantity i.e. -5 (5 sold) or 15 (15 added to inventory)? Or do I
track both in separate tables?
Thanks for your help.
First of all I recommend implementing Date Dimensions on your application, as it seems like you will be doing a lot of Time related calculations. Search on Google for date dimensions as it's beyond the scope of your questions. That said, I believe it will be of great benefit for your app to implement and use date dimensions.
As far as your direct questions go:
What is the best way of tracking changes? In addition to my Products table, should I create another table called HistoricProductQuantities, and insert a new record each time there is a change in the quantity?
Yes you could do this, I would probably call it HistoricProductSnapshot and keep track of the product activity in there on daily basis. With this information as well as time dimensions you could do calculations such as "how many of Product X Did we have 5 days ago or a month ago etc etc."
What number should I track? The historic stock quantity (i.e. 50 in stock on this day, 24 in stock on that day), or the CHANGE in stock quantity i.e. -5 (5 sold) or 15 (15 added to inventory)? Or do I track both in separate tables?
I do not have experience writing inventory control software but I believe with the Snapshot table I mentioned on the question above you would only have to keep track of quantities per day. The Change in product counts could then be calculated from your snapshot table. You could for example have a function that will output the product amount in a given time range as an array. Example: From March 1 to March 7 these were the stock amounts for Product Y [45,40,39,27,22,45,44].
Hope that helps. As I said I am not a product inventory guy but I have worked with Point of Sales Systems and the procedure above should give you a could enough start for what you are trying to do.
This gem could be usefull for tracking changes in models https://github.com/collectiveidea/audited
Keep the data raw. I would personally create a new data entry every day, displaying how much items you have in stock per day. Or you can make the interval much shorter, such as every 12 hours.
For our particular use case:
We had a table called Days, which had a many to many relationship with products, and each "relationship" will have a value called quantity (to keep track of quantity of product per day). Additionally per relationship, we had another value for the relationship with transactions (a one to many relationship) that has the entries for the time of transaction and remaining stocks.
I would personally advise you to use the quantity of stock as the raw data, as it will enable you to gather the data such as how much items were removed during a certain transaction, when the item was out of stock and when it became in stock, all through the data. When you have data in which you need to perform statistical calculations on, it's best to store this data as raw values (quantity of the item).
What's the best way - or, indeed, any way - to calculate a running total in Rails?
I have a model, Sale. It has a quantity column and a sales_value column. I need to populate a third column, total_quantity, with the sum of the quantity values of the previous records, when the table is sorted by isbn_id, then channel_id, then invoice_date. This sets all sorts of sensible database management alarm bells ringing, so I'm wondering if it's even possible.
The reason for needing this cumulative sum is to apply a percentage to the sales where the cumulative quantity is within a particular range. I can't use an average sales value across all records, because the margin on sales can vary dramatically over time - so I'd apply an average to a bunch of sales which might over or under pay the royalty payee.
So. Should I do a before_save callback on the Sale model, and update_attribute, somehow? Is there a method to return the value of the previous record when the table is sorted in a particular way? Or should I dump all Sale records into an array and maybe use inject to accumulate the running total?
Any ideas most welcome, thanks in advance.
Update: subsequent question asked here.
Do not use inject (srsly). The best way to do this is to use the SQL group commands and/or the Calculations methods in activerecord (like sum)
http://ar.rubyonrails.org/classes/ActiveRecord/Calculations/ClassMethods.html
I am creating a simple todo app where I have 2 types of tasks.
1) regular tasks - These have a due date
2) recurring tasks -These are poped up as reminders on specified date. They can be created either as weekly or monthly reminders. If created for a week, it will be poped up on each week (on a specified date on the week). Likewise for a month it need to be specified the week and the date.
What will be the best way to model this scenario?
I would have two columns for the reminder object - remind_at (date) and repeat_frequency (something to identify different re-occurrences by). That way, you could index the remind_at column and search by it quite quickly. Each time a reminder is shown to user, it would look at repeat_frequency - if it contains directions for repeating, set remind_at to next date, if not, delete/archive the reminder.
You could model a Task to have a due_date. But if a task is recurring, due_date will be null and you would use the recurrence field to compute the next_due_date. recurrence would be a string field holding a parsable string like "tuesday" (for weekly) or "17" (a day number for monthly).
def next_due_date
if due_date
due_date
else
# compute next due date using the 'recurrence' field and today's date
end
end
This may or may not be the "best way" for you, depending on your requirements, and the future needs of the model.
I've just begun diving into data warehousing and I have one question that I just can't seem to figure out.
I have a business which has ten stores, each with a certain employees. In my data warehouse I have a dimension representing the store. The employee dimension is a SCD, with a column for start/end, and the store at which the employee is working.
My fact table is based on suggestions the employees give (anonymously) to the store managers. This table contains the suggestion type (cleanliness, salary issue, etc), the date it was submitted (foreign keyed to a Time dimension table), and the store at which it was submitted.
What I want to do is create a report showing the ratio of the number of suggestions to the number of employees in a given year. Because the number of employees changes periodically I just can't do a simple query for the total number of employees.
Unfortunately I've searched the web quite a bit trying to find a solution but the majority of the examples are retail based sales, which is different from what I'm trying to do.
Any help would be appreciated. I do have the AdventureWorksDW installed on my machine so I can use that as a point of reference if anyone offers a suggestion using that.
Thanks in advance!
The slowly changing dimension should have a natural key that identifies the source of the row (otherwise how would it know what to compare to detect changes). This should be constant amongst all iterations of the dimension. You can get a count of employees by computing a distinct count of the natural key.
Edit: If your transaction table (suggestion) has a date on it, a distinct count of employees grouped by a computed function of the suggestion date (e.g. datepart (yy, s.SuggestionDate)) and the business unit should do it. You don't need to worry about the date on the employee dimension as the applicable row should join directly to the transaction table.
Add another fact table for number of Employees in each store for each month -- you could use max number for the month. Then average months for the year, use this as "number of employees in a year".
Load your new fact table at the end of each month. The new table would look like:
fact table: EmployeeCount
KeyEmployeeCount int -- surrogate key
KeyDate int -- FK to date dimension, point to last day of a month
KeyStore int -- FK to store dimension
NumberOfEmployes int -- (max) number of employees for the month in a given store
If you need a finer resolution, use "per week" or even "per day". The main idea is to average the NumberOfEmployes measure for a given store over the year.