I apologize in advance if this goes into esoteric algorithms a bit too
much but I think this will get to an interesting idea to solve your
problem. My background is physics particularly computer simulations of
complex systems. Anyways in cosmology an interesting algorithm is called
an nbody tree code (its been around for at least 20 years so a lot is
available online about it). Since every object with mass (well in
general relativity actually anything with energy but I digress)
interacts with every other object with mass, you end up with the
"nbody" problem. The number of interactions in a system goes as n(n1)
~= n^2 where n is the number of elements. This lead to a nightmare to do
simulations of large systems, say two galaxies colliding. 1 billion X 1
billion minus one is huge and effectively incalculable since you would
have to calculate this each time you wanted to increment the simulation
a tiny bit ahead in time. How do you get a reasonable approximation to
the solution? The answer or at least one of them is nbody "tree codes".
You take advantage of the fact that the the force that one star feels
from another falls off as 1/r^2 and importantly two stars far away from
the first star but close together relatively have roughly the same
magnitude and direction of the "r" vector. So you can simply clump them
together, ie sum there masses, and the force is GM1(M"sum")/r^2. To do
this efficiently numerically you break down the system using binary
search trees. Thinking in 2D just to keep it simple, you divide the
space into top left, top right, bottom left bottom right as a first
approximation. Then continually do that until you end up with each
element in its own box. When you figure out the forces you are going to
apply to the system you just take the distance to the middle of the box
that contains the ones you are going to consider together (the closer to
the star in question the smaller the boxes need to be because the
direction of r changes quicker the closer the boxes are to the star, but
farther away you can use larger and larger boxes (which would contain a
2D tree like structure descending to the point where each of the stars
contained are trapped in there own little box), sum the number of stars
in the box and presto.
How would this help you? Well if you encoded the "box hierachy", say 1
for top left, 2 for top right, 3 for bottom left, 4 for bottom right,
then you could specify the box that someone is in based on a string like
"14234". To find the set of stars/points/whatever that are at least x
away you just would have to do a range search for all the points with
their location "string" larger than or equal to the location sting
corresponding to the closest corner of the biggest box such that its
corner is at least "x" units away. Quite good as a first approximation
and the search algorithm should run as O(nlog(n)) which is a logirithmic
decrease in computation time. Ie the 1 billion times 1 billion 1
problem becomes 1 billion times ~9, much much nicer. Really difficult
thing to explain without looking over a diagram in person I admit but
hopefully it makes sense if you look up the algorithm online.
On 04/09/2010 05:01 PM, malsmith wrote:
>
>
> It's sort of an interesting problem  in RDBMS one relatively simple
> approach would be calculate a rectangle that is X km by Y km with User
> 1's location at the center. So the rectangle is UserX  10KmX ,
> UserY10KmY to UserX+10KmX , UserY+10KmY
>
> Then you could query the database for all other users where that each
> user considered is curUserX > UserX10Km and curUserX < UserX+10KmX
> and curUserY > UserY10KmY and curUserY < UserY+10KmY
> * Not the 10KmX and 10KmY are really a translation from Kilometers to
> degrees of lat and longitude (that you can find on a google search)
>
> With the right indexes this query actually runs pretty well.
>
> Translating that to Cassandra seems a bit complex at first  but you
> could try something like precalculating a grid with the right
> resolution (like a square of 5KM per side) and assign every user to a
> particular grid ID. That way you just calculate with grid ID User1 is
> in then do a direct key lookup to get a list of the users in that same
> grid id.
>
> A second approach would be to have to column families  one that maps
> a Latitude to a list of users who are at that latitude and a second
> that maps users who are at a particular longitude. You could do the
> same rectange calculation above then do a get_slice range lookup to
> get a list of users from range of latitude and a second list from the
> range of longitudes. You would then need to do a inmemory nested
> loop to find the list of users that are in both lists. This second
> approach could cause some trouble depending on where you search and
> how many users you really have  some latitudes and longitudes have
> many many people in them
>
> So, it seems some version of a chunking / grid id thing would be the
> better approach. If you let people zoom in or zoom out  you could
> just have different column families for each level of zoom.
>
>
> I'm stuck on a stopped train so  here is even more code:
>
> static Decimal GetLatitudeMiles(Decimal lat)
> {
> Decimal f = 0.0M;
> lat = Math.Abs(lat);
> f = 68.99M;
> if (lat >= 0.0M && lat < 10.0M) { f = 68.71M; }
> else if (lat >= 10.0M && lat < 20.0M) { f = 68.73M; }
> else if (lat >= 20.0M && lat < 30.0M) { f = 68.79M; }
> else if (lat >= 30.0M && lat < 40.0M) { f = 68.88M; }
> else if (lat >= 40.0M && lat < 50.0M) { f = 68.99M; }
> else if (lat >= 50.0M && lat < 60.0M) { f = 69.12M; }
> else if (lat >= 60.0M && lat < 70.0M) { f = 69.23M; }
> else if (lat >= 70.0M && lat < 80.0M) { f = 69.32M; }
> else if (lat >= 80.0M) { f = 69.38M; }
>
> return f;
> }
>
>
> Decimal MilesPerDegreeLatitude = GetLatitudeMiles(zList[0].Latitude);
> Decimal MilesPerDegreeLongitude = ((Decimal)
> Math.Abs(Math.Cos((Double) zList[0].Latitude))) * 24900.0M / 360.0M;
> dRadius = 10.0M // ten miles
> Decimal deltaLat = dRadius / MilesPerDegreeLatitude;
> Decimal deltaLong = dRadius / MilesPerDegreeLongitude;
>
> ps.TopLatitude = zList[0].Latitude  deltaLat;
> ps.TopLongitude = zList[0].Longitude  deltaLong;
> ps.BottomLatitude = zList[0].Latitude + deltaLat;
> ps.BottomLongitude = zList[0].Longitude + deltaLong;
>
>
>
> On Fri, 20100409 at 16:30 0700, Paul Prescod wrote:
>> 2010/4/9 Onur AKTAS<onur.aktas@live.com <mailto:onur.aktas@live.com>>:
>> > ...
>> > I'm trying to find out how do you perform queries with calculations on the
>> > fly without inserting the data as calculated from the beginning.
>> > Lets say we have latitude and longitude coordinates of all users and we have
>> > Distance(from_lat, from_long, to_lat, to_long) function which
>> > gives distance between lat/longs pairs in kilometers.
>>
>> I'm not an expert, but I think that it boils down to "MapReduce" and "Hadoop".
>>
>> I don't think that there's any topdown tutorial on those two words,
>> you'll have to research yourself starting here:
>>
>> *http://en.wikipedia.org/wiki/MapReduce
>>
>> *http://hadoop.apache.org/
>>
>> *http://wiki.apache.org/cassandra/HadoopSupport
>>
>> I don't think it is all documented in any one place yet...
>>
>> Paul Prescod
>>
>
