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> From: [EMAIL PROTECTED]> To: pgsql-hackers@postgresql.org> Tasneem,> > > >
> 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.> > Are we talking about a mathematical calculation on the
> values, or an algorithm > against the population of the result set? I'm
> presuming the latter or you > could just use a function. If so, is NEAR an
> absolute range or based on > something logarithmic like standard deviation?>
It is based on fuzzy logic.. we take the operand2 (which is a crisp value,
given by the end user) as a fuzzy set.. assign membership degree to its
elements.. and then get the values between 1.0 to 0.8 as the values NEAR to
the operand2, and output those as the result.
I have made the initial membership degree constant, i.e 0.8. But that doesn’t
mean that the size of the set defined by NEAR (md=0.8) remains constant. The
larger the operand2 set, the larger the range of the set defined by NEAR.
> Beyond that, I would think that this mechanism would need some kind of extra
> > heuristics to be at all performant, otherwise you're querying the entire >
> table (or at least the entire index) every time you run a query. Have you >
> given any thought to this?>
Yes u are right.. that’s my main concern. Here I have just put forward an idea
to incorporate fuzziness in current database systems through the ANSI SQL; but
still I have to look into that problem if it is to be functional at all with
large amount of data.
Tasneem Memon
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