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Geographic Functions
editGeographic Functions
editThe geographic functions detect anomalies in the geographic location of the input data.
The X-Pack machine learning features include the following geographic function: lat_long
.
Lat_long
editThe lat_long
function detects anomalies in the geographic location of the
input data.
This function supports the following properties:
-
field_name
(required) -
by_field_name
(optional) -
over_field_name
(optional) -
partition_field_name
(optional)
For more information about those properties, see Detector Configuration Objects.
Example 1: Analyzing transactions with the lat_long function.
{ "function" : "lat_long", "field_name" : "transactionCoordinates", "by_field_name" : "creditCardNumber" }
If you use this lat_long
function in a detector in your job, it
detects anomalies where the geographic location of a credit card transaction is
unusual for a particular customer’s credit card. An anomaly might indicate fraud.
The field_name
that you supply must be a single string that contains
two comma-separated numbers of the form latitude,longitude
. The latitude
and
longitude
must be in the range -180 to 180 and represent a point on the
surface of the Earth.
For example, JSON data might contain the following transaction coordinates:
{ "time": 1460464275, "transactionCoordinates": "40.7,-74.0", "creditCardNumber": "1234123412341234" }
In Elasticsearch, location data is likely to be stored in geo_point
fields. For more
information, see Geo-point datatype. This data type is not
supported natively in X-Pack machine learning features. You can, however, use Painless scripts
in script_fields
in your datafeed to transform the data into an appropriate
format. For example, the following Painless script transforms
"coords": {"lat" : 41.44, "lon":90.5}
into "lat-lon": "41.44,90.5"
:
{ "script_fields": { "lat-lon": { "script": { "source": "doc['coords'].lat + ',' + doc['coords'].lon", "lang": "painless" } } } }
For more information, see Transforming Data With Script Fields.