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Re: [geomesa-users] KNN-Queries
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Hey Mike,
thanks for the detailed answer. With this it was possible to get my
knn-query working. I tested the KNNQuery.runKNNQuery and the
KNNQuery.runNewKNNQuery method. I decided to take the first option,
because the performance seems to be a little better.
Is there any possibility that I can run my query without a filter? I
dont want to filter on time but when I create something like
new Query("gdelt", null, new String[]{"SQLDATE", "geom"}) (set filter to
null) the program won´t finish.
I´m currently working on my masterthesis with focus on storage and
querying geotemporal data in the hadoop ecosystem. Thats why I examine
some technologies in detail. I dont have a specific use case, so I´m
satisfied working with the GDELT-Dataset (I noticed, that the column
"url" was discarded).
Regards,
Marcel.
Am 14.07.2015 20:18, schrieb Michael Ronquest:
Hi Marcel,
Thanks for writing in, as well as your interest in the KNN
method in GeoMesa. Once things are working for you, I'd be *very*
interested in receiving additional feedback, as well as hearing a bit
about your use case.
In short, the KNN algorithm begins by searching in a geohash that
contains your point of reference, with the spatial scale of the
geohash set in the query process. Once all features in that central
geohash are processed, the algorithm then begins to "spiral" out to
neighboring geohashes as needed to either find k neighbors, or to
ensure the current k "best" neighbors are indeed the k nearest neighbors.
Your instinct regarding the KNNQuery is correct: that is what you want
to use. Apologies for the "magic" parameters: KNNQuery is used by the
KNearestNeighborSearchProcess, and the parameters are better explained
there.
Note: the KNNSearchProcess class is used by GeoServer WPS processes,
with a good deal of related boilerplate, so stay away from that.
The runNewKNNQuery method has these parameters:
source: SimpleFeatureSource ===> where your data reside: note this
really should be a GeoMesa Source as we attempt to exploit its
geospatial index in the algorithm
query: Query ===> your "base" query which would include filters on
attributes, time and space.
numDesired: Int ===> this is simply "k", how many points you seek
searchDistanceInMeters:Double ===> this is the "typical" distance
you'd expect to find k points in your data and serves as a "initial
guess" for the search and defines the spatial scale at which the
iterative query by GeoHash will run.
If I was looking for 1000 tweets in Manhattan over the course of a
day, I'd set this to ~500 meters, while if I'm looking for 1000 tweets
around Nageezi, New Mexico, I'd set this to 100000 meters or more.
The search is iterative here, so err toward smaller distances here (at
the potential cost of a slower process, as more "geohash queries" will
need to be made).
maxDistanceInMeters: Double ===> this is the maximum distance at which
the algorithm will search and acts almost like an additional predicate
on your Query: this prevents runaway queries. For example, imagine in
your case if you ask for k=1000 when you only have 100 Features around
Beijing. The KNN process would then "spiral" out from Beijing, geohash
by geohash, querying GeoMesa each time for additional Features. If
you only have sparse data outside of Beijing, then the KNN algorithm
my churn for a great while, perhaps over the entire planet! So this
parameter prevents that. It is possible to get edge effects here, so
error on much larger distances here.
aFeatureForSearch: SImpleFeature ===> this is the reference point
around which to search.
With the parameters defined, you'd then do something like this:
||
|Query theQuery = new Query("gdelt", timeFilter, new String[] |||{
||"SQLDATE"||, ||"geom"| |})|);
// want 100 points
Int k = 100;
// Beijing is dense....
Double guessedDistance = 1000.0;|
|
// very roughly the "radius" of china
Double maxLimitDistance = 2500000.0
|| NearestNeighbors neighbors = KNNQuery.runKNNQuery(fs,
theQuery, k, guessedDistance, maxLimitDistance, beijingCenter);
|
|||||||||||||||||||||
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where fs and timeFilter are as you've previously defined them and
beijingCenter is a SimpleFeature with your point as its geometry.
Hopefully this will help. Please report back on further issues or
success.
Cheers,
Mike
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