This may sounds stupid but I am confused with the multiplicity between two classed in UML class diagram.
The classes are admin and payment.
It is very clear that
1 admin can accept 0..* payment,
but I'm not sure about can 0..* payment accepted by 1 admin or 1..* admin?
If were talking about that particular payment at that moment, the payment is only handling by 1 admin(of course) but in a bigger picture many different payments can be accepted by many different admins right?
I already tried to search through Google for many examples but can't relate them to my case. My question is the same as:
1 house can has many rooms, and so
many rooms is having by 1 house, but in bigger picture
many different houses can have many rooms also. (right?)
I expect someone could explain more detail for me, thanks in advance..
The multiplicity refers to the number for the instances of the model. Do in your examples for an insurance of a payment it is handled by 1 admin or a specific window has 1 house. So 1 is the correct value in both cases.
The total number of any of these things is irrelevant as almost always it would be more than 1 and 0 would make no sense.
Related
Management are involved. They want to split the product into multiple streams but it's still one product.
Management have introduced multiple Product Owners and given then a stream each.
The Scrum Masters are recommending 1 product = 1 owner = 1 backlog. Management disagree.
Everyone is pulling their hair out with dependencies, it's a mess.
Who should take action and what?
Depending on the project this is possible. I would recommend looking into LeSS Huge we use this system for larger projects. You keep one Product Backlog, one definition of done, one product & one main PO. You just add extra areas or "sub" projects that are directed from their own areaPO that takes from the main backlog. The main PO just divides it more into manageable chunks and makes sure that parallel parts don't have to wait on other parts of the project to finish
I am working on a case to finish my (not so advanced) data scientist course and I have already been helped a lot by topics here, thanks!
Unfortunately now I am stuck again and cannot find an existing answer.
My data comes from a bike shop and I want to see if products bought during customers' first registered purchase are related to/have impact on how important they will become to the shop in the future. I have grouped customers into 5 clusters (from those who registered and made never any registered purchase again, through these who made 2-3 purchases for little money, those who made a few purchases for a lot of money to those who purchase stuff regularly and really bring a lot of money to this bike shop), I have ordered them into an ordinal dependent variable.
As the independent variables I have prepared 20+ binary variables that identify products/services bought during the first purchase from this shop (first purchase as a registered customer). One row per customer. So I want to check the idea if there are combinations of products (probably "extras" to the bike purchase) that can increase the chance that a customer would register and hopefully stay as a loyal customer for the future.
The dream would be be able to say, for example, if you buy a cheap or middle-cheap bike during this first purchase you probably don't contribute so much to the bike shop in a long term so you have low grade on the dependent variable. But those who bought a middle-cheap bike AND a helmet AND a lock (probably to special price) are more likely to become one of the loyal registered customers bringing money for a longer time.
There might be no relation like that but I want to test that anyways. Implementation of the result could be being able to recommend an extra product during a purchase (with a good price on it).
I am learning R during this course. We went through some techniques and first I was imagining it would be possible to work with the neural networks (just cause it sounded most fun to try), having all these products as input in the sparse matrix and the customers clusters as the output (I hoped it was similar to the examples I read about with sparse matrix with pixels from a picture as the input and numbers 1-9 as the output) but then I was told that this actually is based on pictures and real patterns and in my case I don't even know if there is any.
Then I was thinking I could try with the ordinal forest. But it doesn't predict my clusters well, not at all (2 out of 5 clusters get no predictions). But that is OK, I don't expect the first purchase to be able to predict all the customers future. But I would really want to see if there are combinations of products that might increase the chance that a customer ends up in one of the "higher" clusters on the loyalty scale.
I am not sure if this was clear enough. :) Do you think that there is any way of testing my idea? What could I try to do? Let me know if you need more information.
Im learning dimensional modeling and Im trying to create a model. I was thinking about a social media platform which rates hotels. The platform has following data:
hotel information: name and address
a user can rate hotels (1-5 points)
a user can write comments
platform stores the date of the comments
hotel can answer via comment and it stores the date of it
the platform stores the total number of each rating level (i.e.: all rates with 1 point, all rates with 2 point etc.)
platform stores information of the user: sex, name, total number of votes he/she made and address
First, I tried to define which information belongs to a dimension or fact table
(here I also checked which one is additive/semi additive/non-additive)
I realized my example is kind of difficult, because it’s hard to decide if it belongs to a fact table or dimension.
I would like to hear some advice. Would someone agree with my model?
This is how I would model it:
Hotel information -> hotel dimension
User rating -> additive fact – because I can aggregate them with all dimensions
User comment -> semi additive? – because I can aggregate them with the date dimension (I don’t know if my argument is correct, but I know I would have new comments every day, which is for me a reason to store it in a fact table
Answer as comment -> same handling like with the user comments
Date of comment-> dimension
Total Number of all votes (1/2/3/4/5) -> semi-additive facts – makes no sense to aggregate them, since its already total but I would get the average
User information sex and name, address -> user-dimension
User Information: total number of votes -> could be dimension or fact. It depends how often it changes. If it changes often, I store it in a fact. If its not that often, then dimension
I still have question, hope someone can help me:
My Question: should I create two date dimensions, or can I store both information in one date dimension?
2nd Question: each user and hotel just have one address. Are there arguments, to separate the address dimension in a own hierarchy? Can I create a 1:1 relationship to a user dimension and address dimension?
For your model, it looks well considered, but here are some thoughts:
User comment (and answers to comments): they are an event to be captured (with new ones each day, as you mention) so are factual, with dimensionality of the commenter, type of comment, date, and the measure is at least a 'count' which is additive. But you don't want to store big text in a fact so you would put that in a dimension by itself which is 1:1 with the fact, for situations where you need to query on the comment itself.
Total Number of all votes (1/2/3/4/5) are, as you say, already aggregates, mostly for performance. Totals should be easy from the raw data itself so maybe not worthwhile to store them at all. You might also consider updating the hotel dimension with columns (hotel A has 5 '1' votes and 4 '2' votes) that you'd update as you go on, for easy filtering and categorisation.
User Information: total number of votes: it is factual information about a user (dimension) and it depends on whether you always just want to 'find it out' about a person or whether you are likely to use it to filter other information (i.e. show me all reviews for users who have made 10-20 votes). In that case you might store the total in the user dimension (and/or a banding, like 'number of reviews range' with 10-20, 20-30). You can update dimensions often if you need to, but you're right, it could still just live as a fact only.
As for date dimensions, if the 'grain' is 'day' then you only need one dimension, that you refer to from multiple facts.
As for addresses, you're right that there are arguments on both sides! Many people separate addresses into their own dimension, referred to from the other dimensions that use them. Kimball suggests you can do that behind the scenes if necessary, but prefers for each dimension to have its own set of address columns(but modelled as consistently as possible).
I'm using "Users Points Voting API" module to combine "User Points" module and "Fivestar" module.
It works, but not as expected.
When an user rate a post, the author gains N points.
If an user removes its vote, the author loses the N points.
N is a fixed number (I set it in the settings). It is fixed, and not related to the number of stars, it depends on the number of votes one receives and not how good and how bad are these votes (1,2.. or 5 stars).
Can anybody confirm what I've written ? It seems that the module should work differently because it allows to specify positive and negative votes. Maybe it has not been fully developed for the Fivestar module.
thanks
All votes in the fivestar module is positive in that sense that 1 star is above zero. So the problem is not so much the integration itself, but that the two modules don't mix that well. User points react on positive/negative votes, but since all votes in fivestar are positive, 1 star will gain the same result as 5 star vote.
You could argue this is a bad idea, but this is how it was designed.
I have a client who has suggested laying out a long list of categories in a custom order. The order is to be decided by them based on product items they sell the most etc.
I tend to disagree and feel that people browsing the internet prefer to search lists of categories that are in alphabetical order or sorted by something they can take reference of such as a date.
I would like to know others thoughts on this and it would be appreciated if anyone could point me in the direction of any open source surveys that have been taken in this area.
Thanks
Ben
What a silly stance to take regarding a simple customer request. Allow for both orderings, and other ones too. There is no survey that will demonstrate that the client is wrong as they are - by definition - correct.
Code that allows for different orderings has greater utility anyway, and real user data will be able to show them which - if either - should be the default.