## To all the pgsql developers..Have a look at the operators proposed by me in my research paper.

From: Tasneem Memon To all the pgsql developers..Have a look at the operators proposed by me in my research paper. 2007-06-01 13:24:33 BAY126-W2573A231F1394490354E28A32C0@phx.gbl (view raw, whole thread or download thread mbox) 2007-06-01 13:24:33 from Tasneem Memon  2007-06-02 00:13:54 from Jim Nasby   2007-06-02 17:33:06 from Josh Berkus pgsql-hackers
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Dear All,
I am explaining here how i have defined the operators NEAR and NOT NEAR in my research paper. I want critics/comments from all of you on the operators.

These operators belong to the category of “Fuzzy Operators” proposed in the paper for ANSI SQL.

NEAR

It deals with the NUMBER and DATE datatypes simulating the human behavior and processing the information contained in NEAR in the same way as we humans take it. This is a binary operator with the syntax:
op1  NEAR  op2
Here, the op1 refers to an attribute, whereas op2 is a fixed value, both of the same datatype.
Suppose we want a list of all the VGAs, price of which should be somewhere around 30\$ .. the query will look like:

SELECT    *
FROM      accessories
WHERE    prod_name = ‘VGA’
AND                prod_price NEAR 30

A query for the datatype DATE will look like:

SELECT    *
FROM      sales
WHERE    item = ’printer’
AND                s_date NEAR 10-7-06

The algorithm for the NEAR operator works as follows:

The margins to the op2, i.e. m1 and m2, are added dynamically on both the sides, considering the value it contains. To keep this margin big is important for a certain reason discussed later.
The NEAR operator is supposed to obtain the values near to the op2, thus the target membership degree(md) is initially set to 0.8.
The algorithm compares the op1(column) values row by row to the elements of the set that NEAR defined, i.e. the values from md 1.0 to 0.8, adding matching tuples to the result set.
4.      It is very much possible that the result set is empty since no values within the range exist in the column. Thus, the algorithm checks for empty result set, and in that case, decreases the target md by 0.2 and jumps to step 3. This is the reason big margins to the op2 are added.
5.      In case there are no values in op1 that are between m1 and m2 (where the membership degree of the values with respect to NEAR becomes 0.1) and the result set is empty, the algorithm fetches the two nearest values (tuples) to op2, one smaller and one larger than the op2, as the result.

The algorithm will give an empty result only if the table referred to in the query is empty.

2. NOT NEAR

This operator is also a binary operator, dealing with the datatype NUMBER and DATE. It has the syntax:
op1  NOT NEAR  op2
The op1 refers to an attribute, whereas op2 is a fixed value, both of the same data type.
A query containing the operator looks like:

SELECT id, name, age, history
FROM    casualties
WHERE  cause = ‘heart attack’
AND      age NOT NEAR 55

Or suppose we need a list of some event that is not clashing with some commitment of ours:

SELECT *
FROM    events
WHERE  e_name= ‘concert’
AND      date NOT NEAR 8/28/2007

The algorithm for NOT NEAR works like this:

First of all it adds the margins to the op2, i.e. m1 and m2, dynamically on both the sides, considering the value op2 contains.
op1 values outside the scope of the op2 (m1, m2) are retrieved and added to the result.
If the result set is empty, the farthest values within the op2 fuzzy set (those possessing the least membership degree) are retrieved. This is done by continuing the search from values with md=0.1 till the md=0.6, where the md for NOT NEAR reaches 0.4.

Regards,

Tasneem Ishaque Memon MS (Computer Science) Institute of Mathmetics and Computer Science University of Sindh, Jamshoro Pakistan http://www.geocities.com/tasneememon/- The process of gaining knowledge is not producing results but clearing away ignorance.
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