Anomaly Detection Data File Format

Model your data as a series of process observations or measures that are associated with an outcome of interest. Compose each observation (row) as a common set of features (aka., independent variables or factors). Both features and outcomes are either numerical (blood pressures, HbA1c, ages, etc.), binary (yes/no, true/false, etc.), or categorical (Gender, Service line, Floor unit, Shift, DRG, etc.).

Provide a .csv or .json formatted file with a header row labeling each column.

1. The first column is an alphanumeric index starting with a letter that is used to uniquely identify each data row.
2. Provide additional columns of categorical or numeric data. Categorical data must not begin with a number.
3. The first row is a user-supplied alphanumeric header for each column.

Sample Table

Sample Test Files

Anomaly Detection within Nested Groups File Format

Model your data as a series of rows containing relationships composed as a member and a corresponding group.
The 'member' can either be the name of a group member (user) or the name of a group.

Provide a .csv formatted file with a header row labeling the left column containing the 4 letter sequence 'Name' and the right column containing the 5 letter sequence 'Group'".
Letters are case insensitive.

1. The first column is an end-user or group name.
2. The second column is a group name.

Sample Table