It is a functional dependency question.
I know that when x->yz then x->y and x->z.But is the above dependency possible?
If xy determines z can x determine z and y determine z?
Yes, if xy -> z then it's possible that also x -> z and y -> z.
Suppose z can only have one value; then a given x, y or xy only ever appears with that one value. Or suppose x -> z and y -> z and x must equal y. Or suppose both x and y are unique; then xy is unique. (A case of that is when both x & y are candidate keys.) In fact any time that x -> z and y -> z, xy -> z.
(To show something is possible it's always worth trying some cases, especially very simple ones, in case they are examples, so you don't have to prove the general case.)
Related
I have a mysql table with a large list of coordinates (x, y, z). I want to find the most common spots, but when the same place is logged, it isn't identical. For example, x could be 496.0481 or 496.3904, but that is actually the same place.
When I do the following query I get a list of the absolute exact matches, but those are very few and far between:
SELECT x, y, z, COUNT(*) AS coords
FROM coordinates
GROUP BY x, y, z
ORDER BY coords DESC
LIMIT 10;
How can I adjust this to be grouped by each of x, y, and z to be +/- 1 to catch a larger area? I've tried a mix of IF and BETWEEN statements but can't seem to get anything to work.
If I do GROUP BY round(x), round(y), round(z), that gets a larger range but doesn't capture if the number goes from 496 to 497 even if they are just slightly different.
Thanks in advance for the help.
Very naive way:
select t1.x as x1, t1.y as y1, t1.z as z1, t2.x as x2, t2.y as y2, t2.z as z2
from coordinates t1
join coordinates t2 on sqrt(power(t2.x-t1.x, 2) + power(t2.y-t1.y, 2) + power(t2.z-t1.z, 2)) <= 1
For each coordinates (t1) query finds all other coordinates (t2) that dinstanced less or equal than 1 from each other.
But this query has very bad performance: O(n^2)
I am preparing for my Database final and I would like to understand these two questions. Could you please explain to me which is the correct answer and why it is correct.
Suppose that you have a relation with the schema R(X, Y, Z). Every value of X is unique, but the
other columns could have duplicate values. Assume that a sparse index is created for relation R on attribute X. Which of the following queries would use this index effectively?
(a) SELECT MAX(X)
FROM R
(b) SELECT MAX(Y)
FROM R
GROUP BY X
(c) SELECT *
FROM R
WHERE X <> 30
(d) SELECT MAX(Y)
FROM R
WHERE X = 23
(e) none of the above uses the index effectively
I believe (a) could be the correct answer as we have an index for X and they are all unique values.
Suppose that you have a relation with the schema R(X, Y, Z). Every value of X is unique, but the
other columns could have duplicate values. Assume that a dense index is created for relation R on attributes X and Y. Which of the following queries would use this index effectively?
(a) SELECT *
FROM R
WHERE X < Y
(b) SELECT DISTINCT X, Y
FROM R
WHERE X = 23 AND Y > 39
(c) SELECT X, Y
FROM R
(d) SELECT X
FROM R
WHERE Y = 23
(e) none of the above uses the index effectively
I believe (c) could be the correct answer as we have an indexes for both X and Y.
MySQL does not have "dense" vs "sparse". Here are the optimal indexes:
(a) SELECT MAX(X) FROM R -- INDEX(X)
(b) SELECT MAX(Y) FROM R GROUP BY X -- INDEX(x,y); INDEX(x) is not as good
(c) SELECT * FROM R WHERE X <> 30 -- No index is _likely_ to be useful
(d) SELECT MAX(Y) FROM R WHERE X = 23 -- Does not make sense if X is unique
(e) none of the above uses the index effectively -- Some: a,b,d
and
(a) SELECT * FROM R WHERE X < Y -- no index
(b) SELECT DISTINCT X, Y FROM R WHERE X = 23 AND Y > 39 -- Dumb due to uniqueness
(c) SELECT X, Y FROM R -- no index; just read the table
(d) SELECT X FROM R WHERE Y = 23 -- INDEX(Y,X), or, not as good, INDEX(Y)
(e) none of the above uses the index effectively -- ambiguous; note that (Y,X) is not same as (X,Y)
I have a tabulated data with columns: x, a, b, c, ... x has finite range.
I would like to find/learn a function f(a,b,c, ...) such that, if we plot all points (x, y = f(a,b,c, ...)) then the line fitting all such points has positive slope. That is, as x increases, f(a,b,c, ...) also increases. What is the best and simplest way to find such f. In other words, for any two rows in the table (x1, a1, b1, c1, ...) and (x2, a2, b2, c2, ...) we have, if x1 < x2 then f(a1, b1, c1, ...) < f(a2, b2, c2, ...).
One can see this as a "relaxed" version of predicting x in supervised learning.
So let's say you have n boolean inputs of x1, x2, x3, ..., xn. How do you determine that <= k of your boolean inputs are True using only And/Or/Not logic gates, and doing so in polynomial time?
I'm quite honestly befuddled.
There are many ways to do it. One is to (recursively) make two nets:
one (A) determining that <= k-1 of boolean inputs x1 ... x[n-1] are True.
another (B) determining that <= k of boolean inputs x1 ... x[n-1] are True.
Connect them as (B And Not x[n]) Or A
Each record in my report includes (amongst other information) a set of other data items of the format "ItemName X Y Z" where X, Y & Z are numerical values.
When displayed this set will be grouped (as illustrated below) according to a properties of each item, and displayed in a table or similar.
The values X, Y and Z will be displayed pictorially and formatting will be applied to other items (i.e. background colours applied to different group names) to make the information easier to read.
What would be the best way to display this in a report? Current options seem to be using either Sub Reports or to build a new control using CustomReportItems.
Level 1 Group 1 Name
Level 2 Group 1.1 Name
ItemName X Y Z
ItemName X Y Z
ItemName X Y Z
Level 2 Group 1.2 Name
ItemName X Y Z
ItemName X Y Z
ItemName X Y Z
Level 1 Group 2 Name
Level 2 Group 2.1 Name
ItemName X Y Z
ItemName X Y Z
ItemName X Y Z
Level 2 Group 2.2 Name
ItemName X Y Z
ItemName X Y Z
ItemName X Y Z
From my understanding of the requirements, all of this can be achieved in a single table (or list) object, without requiring subreports:
Set up your data query.
Insert a table object into the report, with the dataset specified as the dataset for the query.
Insert groups into the reports for each of the Level 1 and Level 2 grouping criteria. Include group header sections, but not group footers.
To format a cell's colour based on a data item in the detail section (ie. at ItemName X Y Z level), select that cell in the Report Designer and set the BackgroundColour property (from the Properties window) to be an expression selecting the desired colour, using the Edit Expression dialog. (Immediately below the BackgroundColour property is a BackgroundImage property, which can also be set using an expression, if you want to use specific images rather than colours.)