How do I go about writing the relational algebra for this SQL query?
Select patient.name,
patient.ward,
medicine.name,
prescription.quantity,
prescription.frequency
From patient, medicine, prescription
Where prescription.frequency = "3perday"
AND prescription.end-date="08-06-2010"
AND canceled = "Y"
Relations...
prescription
prescription-ref
patient-ref
medicine-ref
quantity
frequency
end-date
cancelled (Y/N))
medicine
medicine-ref
name
patient
Patient-ref
name
ward
I will just point you out the operators you should use
Projection (π)
π(a1,...,an): The result is defined as the set that is obtained when all tuples in R are restricted to the set {a1,...,an}.
For example π(name) on your patient table would be the same as SELECT name FROM patient
Selection (σ)
σ(condition): Selects all those tuples in R for which condition holds.
For example σ(frequency = "1perweek") on your prescription table would be the same as SELECT * FROM prescription WHERE frequency = "1perweek"
Cross product(X)
R X S: The result is the cross product between R and S.
For example patient X prescription would be SELECT * FROM patient,prescription
You can combine these operands to solve your exercise. Try posting your attempt if you have any issues.
Note: I did not include the natural join as there are no joins. The cross product should be enough for this exercise.
An example would be something like the following. This is only if you accidentally left out the joins between patient, medicine, and prescription. If not, you will be looking for cross product (which seems like a bad idea in this case...) as mentioned by Lombo. I gave example joins that may fit your tables marked as "???". If you could include the layout of your tables that would be helpful.
I also assume that canceled comes from prescription since it is not prefixed.
Edit: If you need it in standard RA form, it's pretty easy to get from a diagram.
alt text http://img532.imageshack.us/img532/8589/diagram1b.jpg
Related
I'm working with my DBA to try to figure out a way to roll up all costs associated with a Work Order. Since any work Order can have multiple child work orders (through multiple "generations") as well as related work orders (through the RELATEDRECORDS table), I need to be able to get the total of the ACTLABORCOST and ACTMATERIALCOST fields for all child and related work orders (as well as each of their child and related work orders). I've worked though a hierarchical query (using CONNECT BY PRIOR) to get all the children, grandchildren, etc., but I'm stuck on the related work orders. Since every work order can have a related work order with it's own children and related work orders, I need an Oracle function that drills down through the children and the related work orders and their children and related work orders. Since I would think that this is something that should be fairly common, I'm hoping that there is someone who has done this and can share what they've done.
Another option would be a recursive query, as suggested by Francisco Sitja. Since my Oracle didn't allow 2 UNION ALLs, I had to joint to the WOANCESTOR table in both child queries instead of dedicating a UNION ALL for doing the WO hierarchy. I was then able to use the one permitted UNION ALL for doing the RELATEDRECORD hierarchy. And it seems to run pretty quickly.
with mywos (wonum, parent, taskid, worktype, description, origrecordid, woclass, siteid) as (
-- normal WO hierarchy
select wo.wonum, wo.parent, wo.taskid, wo.worktype, wo.description, wo.origrecordid, wo.woclass, wo.siteid
from woancestor a
join workorder wo
on a.wonum = wo.wonum
and a.siteid = wo.siteid
where a.ancestor = 'MY-STARTING-WONUM'
union all
-- WO hierarchy associated via RELATEDRECORD
select wo.wonum, wo.parent, wo.taskid, wo.worktype, wo.description, wo.origrecordid, wo.woclass, wo.siteid
from mywos
join relatedrecord rr
on mywos.woclass = rr.class
and mywos.siteid = rr.siteid
and mywos.wonum = rr.recordkey
-- prevent cycle / going back up the hierarchy
and rr.relatetype not in ('ORIGINATOR')
join woancestor a
on rr.relatedrecsiteid = a.siteid
and rr.relatedreckey = a.ancestor
join workorder wo
on a.siteid = wo.siteid
and a.wonum = wo.wonum
)
select * from mywos
;
Have you considered the WOGRANDTOTAL object? Its description in MAXOBJECT is "Non-Persistent table to display WO grandtotals". There is a dialog in the Work Order Tracking application that you can get to from the Select Action / More Actions menu. Since you mentioned it repeatedly, I should note that WOGRANDTOTAL values do not include joins across RELATEDRECORDS to other work order hierarchies.
You can also save yourself the complication of CONNECT BY PRIOR by joining to WOANCESTOR, which is effectively a dump from a CONNECT BY PRIOR query. (There are other %ANCESTOR tables for other hierarchies.)
I think a recursive automation script would be the best way to do what you want, if you need the results in Maximo. If you need the total cost outside of Maximo, maybe a recursive function would work.
We finally figured out how to pull this off.
WITH WO(WONUM,
PARENT) AS
((SELECT X.WONUM,
X.PARENT
FROM (SELECT R.RECORDKEY WONUM,
R.RELATEDRECKEY PARENT
FROM MAXIMO.RELATEDRECORD R
WHERE R.RELATEDRECKEY = '382418'
UNION ALL
SELECT W.WONUM,
W.PARENT
FROM MAXIMO.WORKORDER W
START WITH W.PARENT = '382418'
CONNECT BY PRIOR W.WONUM = W.PARENT) X)
UNION ALL
SELECT W.WONUM, W.PARENT FROM MAXIMO.WORKORDER W, WO WHERE W.WONUM = WO.PARENT)
SELECT DISTINCT WONUM FROM WO;
This returns a list of all of the child and related work orders for a given work order.
In a survey of 100 people, I am asking each person to choose between product A and product B. I ask each person this question 3 times, but each time I present a different set of products. Say, first time, Person 1 is asked to choose between 'Phone 1' and 'Phone 2', given certain attributes of each phone. The second time the choice is again 'Phone 1' vs. 'Phone 2', but a different set of attributes for each phone.
A person is presented three attributes associated with the two phone alternatives every time the question is asked. So, each time between Phone 1 and Phone 2, the attributes of the phone such as cost, memory and camera pixels are presented so that user can choose which set of attributes is most attractive, Phone 1's or Phone 2's.
Overall, 3*100 = 300 responses; 3 responses per person. Each time the attributes cost, memory and camera pixels presented and user asked to choose the feature set they prefer.
My goal is to analyze how users value features of a phone vs. cost of the phone.
In this scenario, can I use a MNL - even though each time I asked the person a question, I only presented two choices ? My understanding is that MNL is sued when (a) there are multiple choices and (b) the choice options do not change across observations, i.e. each person is asked to choose between multiple products, say A, B, C and A, B, C do not change across observations.
In the scenario described above, the two choices varied across the three times the same person was asked the question ? If not MNL, should I rather create a binary logit model given that user only had to choose between two options when the question was asked (even though he was asked the question three times)? If I can use binary logit, should I be concerned that the choice set of products change across observations ? or should I let the attributes defined in each of the rows address the differences in product choices across observations.
I have setup the data as follows (thinking I can do MNL but may be I should set it up differently and use another modeling approach?):
I am working on designing and analyzing similar survey but mine is related to transportation. I am at the beginning level and I am still new to the whole concept, however I will give you an advice and reference maybe it is helpful.
First point: I have come cross 3 models as following from a useful video on YouTube:
MNL refers to Multinomial Logit Model. MNL is used with
alternative-invariant regressors (for example salary of participant
in the survey, or his/her gender …).
Conditional logit model is used with alternative-invariant (gender,
salary, education level …) and alternative-variant regressors (cost
of the product, memory, camera pixel …)
Mixed logit model which uses random parameters. It is also used with
alternative-invariant (gender, salary, education level …) and
alternative-variant regressors (cost of the product, memory, camera
pixel …)
Note regarding alternative-invariant and alternative-variant regressors:
The gender of person participating in the survey will NOT vary between Product A or Product P, so it is alternative-invariant regressor. While price of product could vary between Product A and Product B so it is called alternative-variant regressors.
Based on above I assume you need to use conditional logit model or mixed logit model.
For me I couldn’t find a special function in R for the conditional logit model or mixed logit model. The same mlogit function is used, refer to the examples below for the help of mlogit package:
a pure "multinomial model"
summary(mlogit(mode ~ 0 | income, data = Fish))
a pure "conditional" model
summary(mlogit(mode ~ price + catch, data = Fish))
a "mixed" model
m <- mlogit(mode ~ price+ catch | income, data = Fish)
summary(m)
same model with charter as the reference level
m <- mlogit(mode ~ price+ catch | income, data = Fish, reflevel = "charter")
From the examples above, I think (but NOT sure) that in the Manual of mlogit package, they refer to mixed logit when you used both alternative-invariant and alternative-variant regressors. While conditional model when you have only alternative-variant regressors. On the other hand, multinomial model when you have only multinomial alternative-invariant regressors.
Second point: There is something called “panel data” when you are asking the same person to choose one product for each choice-set. Same person here means that in your model you are taking into consideration, the gender, the salary, the education level … which they will stay the same for the same person. Check this: https://en.wikipedia.org/wiki/Panel_data
To use panel techniques please refer to help in mlogit package in R. I am quoting from it the following:
“panel only relevant if rpar is not NULL and if the data are repeated observations of the same unit ; if TRUE, the mixed-logit model is estimated using panel techniques”
So in my understanding, if you want to use the panel techniques you have to use random draws because panel will be true and rpar will not be NULL.
Moreover, for example about using the panel data, please refer to the below example from “Estimation of multinomial logit models in R : The mlogit Packages” by Yves Croissant
data("Train", package = "mlogit")
Tr <- mlogit.data(Train, shape = "wide", varying = 4:11, choice = "choice", sep = "_", opposite = c("price", "time", "change", "comfort"), alt.levels=c("A", "B"), id.var ="id")
Train.ml <- mlogit(choice ~ price + time + change + comfort, Tr)
Train.mxlc <- mlogit(choice ~ price + time + change + comfort, Tr, panel = TRUE, rpar = c(time = "cn", change = "n", comfort = "ln"), correlation = TRUE, R = 100, halton = NA)
Train.mxlu <- update(Train.mxlc, correlation = FALSE)
I hope that is useful to you.
The AdventureWorksDW has the construct of the Financial Reporting Fact table. I have a similar fact table where the fact contains only the FK to the dimension tables and a value. The measure gets it's context from an DimAccount dimension. Are there any code samples that show how to do a simple ratio in a calculated member between two measures of the AdventureWorks Financial Reporting sample?
So basically I would like to see say Total Long term Debt / Total Assets from AdventureWorksDW? What I need is the expression or MDX.
Thanks in advance.
Use a query like this:
with member [Account].[Accounts].[Balance Sheet].[Dept by Assets] as
IIf([Account].[Accounts].[Assets] <> 0,
[Account].[Accounts].[Long Term Liabilities] / [Account].[Accounts].[Assets],
null
)
,format_string = "0.00%"
select {
[Account].[Accounts].[Assets],
[Account].[Accounts].[Long Term Liabilities],
[Account].[Accounts].[Dept by Assets]
}
on columns,
{ [Measures].[Amount] }
on rows
from [Adventure Works]
You can define members in any hierarchy, not only in the measures. In the definition, you should use the parent member before the name of the new member, to tell AS the position in the hierarchy. This is more important for CREATE MEMBER in the cube calculation script than for WITH MEMBER, as it influences the position where the client tool will display it.
Suppose I have the following relations:
Academic(academicID (PK), forename, surname, room)
Contact (contactID (PK), forename, surname, phone, academicNO (FK))
I am using Java & I want to understand the use of the notation.
Π( relation, attr1, ... attrn ) means project the n attributes out of the relation.
σ( relation, condition) means select the rows which match the condition.
⊗(relation1,attr1,relation2,attr2) means join the two relations on the named attributes.
relation1 – relation2 is the difference between two relations.
relation1 ÷ relation2 divides one relation by another.
Examples I have seen use three tables. I want to know the logic when only two tables are involved (academic and contact) as opposed to three (academic, contact, owns).
I am using this structure:
LessNumVac = Π( σ( job, vacancies < 2 ), type )
AllTypes = Π( job, type )
AllTypes – LessNumVac
How do I construct the algebra for:
List the names of all contacts owned by academic "John"
List the names of all contacts who is owned by academic "John".
For that, you would join the Academic and Conctact relations, filter for John, and project the name attributes. For efficiency, select John before joining:
πforename, surename (Contact ⋈academicNO = academicID (πacademicID (σforename = "John" Academic))))
You have to extend your operations set with natural join ⋈, Left outer join ⟕ and/or Right outer join ⟖ to show joins.
There is a great Wikipedia article about Relational Algebra. You should definitely read that one!
The title is very wordy. So I'll explain with an example.
We have a database of 10,000 twitter users with each following up to 2000 users. The algorithm takes as input one random never before seen user (including the people that follow him), and returns the twitter users from the database by order of how many of his followers they follow.
i.e.
We have:
User A follows 1,2,3,4
User B follows 3,4,5,6
User C follows 4,8,9
We enter user X who has users 3,4,5 following him.
The algorithm should return:
B: 3 matches (3,4,5)
A: 2 matches (3,4)
C: 1 match (4)
Store the data as a sparse integer matrix A of size 10^5x10^5 with ones at the appropriate places. Then, given a user i, compute A[i,] * A (matrix multiplication). Then sort.
Assuming you have a table structure similar to this:
Table Users
Id (PK, uniqueidentifier, not null)
Username (nvarchar(50), not null)
Table UserFollowers
UserId (FK, uniqueidentifier, not null)
FollowerId (uniqueidentifier, not null)
You can use the following query to get the common parents of followers of the followers of the user in query
SELECT Users_Inner.Username, COUNT(Users_Inner.Id) AS [Total Common Parents]
FROM Users INNER JOIN
UserFollowers ON Users.Id = UserFollowers.FollowerId INNER JOIN
UserFollowers AS UserFollowers_Inner ON UserFollowers.FollowerId = UserFollowers_Inner.UserId INNER JOIN
Users AS Users_Inner ON UserFollowers_Inner.FollowerId = Users_Computed.Id
WHERE (UserFollowers.UserId = 'BD34A1FF-FCF5-4D35-B8A3-EFFB1587A874')
GROUP BY Users_Inner.Username
ORDER BY COUNT(Users_Inner.Id) DESC
would something like this work?
for f in followers(x)
for ff in followers(f)
count[ff]++ // assume it is initially 0
sort the ff-s by their counts
Unlike the matrix solution, the complexity of this is proportional to the number of people involved rather than the number of users on twitter.