@@ -36,8 +36,8 @@ PUT _ml/anomaly_detectors/example1
36
36
"analysis_config": {
37
37
"detectors": [{
38
38
"function" : "lat_long",
39
- "field_name" : "transactionCoordinates ",
40
- "by_field_name" : "creditCardNumber "
39
+ "field_name" : "transaction_coordinates ",
40
+ "by_field_name" : "credit_card_number "
41
41
}]
42
42
},
43
43
"data_description": {
@@ -50,22 +50,23 @@ PUT _ml/anomaly_detectors/example1
50
50
51
51
If you use this `lat_long` function in a detector in your {anomaly-job}, it
52
52
detects anomalies where the geographic location of a credit card transaction is
53
- unusual for a particular customer’s credit card. An anomaly might indicate fraud.
53
+ unusual for a particular customer’s credit card. An anomaly might indicate
54
+ fraud.
54
55
55
- IMPORTANT: The `field_name` that you supply must be a single string that contains
56
- two comma-separated numbers of the form `latitude,longitude`, a `geo_point` field,
57
- a `geo_shape` field that contains point values, or a `geo_centroid` aggregation.
58
- The `latitude` and `longitude` must be in the range -180 to 180 and represent a
59
- point on the surface of the Earth.
56
+ IMPORTANT: The `field_name` that you supply must be a single string that
57
+ contains two comma-separated numbers of the form `latitude,longitude`, a
58
+ `geo_point` field, a `geo_shape` field that contains point values, or a
59
+ `geo_centroid` aggregation. The `latitude` and `longitude` must be in the range
60
+ -180 to 180 and represent a point on the surface of the Earth.
60
61
61
62
For example, JSON data might contain the following transaction coordinates:
62
63
63
64
[source,js]
64
65
--------------------------------------------------
65
66
{
66
67
"time": 1460464275,
67
- "transactionCoordinates ": "40.7,-74.0",
68
- "creditCardNumber ": "1234123412341234"
68
+ "transaction_coordinates ": "40.7,-74.0",
69
+ "credit_card_number ": "1234123412341234"
69
70
}
70
71
--------------------------------------------------
71
72
// NOTCONSOLE
0 commit comments