QPR ProcessAnalyzer Objects in Expression Language: Difference between revisions

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# Array of columns to group
# Array of columns to group
# Array of groupping expressions
# Array of grouping expressions
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Creates a new DataFrame based on a DataFrame. The resulting DataFram has rows grouped by given columns and values aggregated using given functions. Thus one row in the end result corresponds with one group.
Creates a new DataFrame based on the current DataFrame. The resulting DataFrame has rows grouped by given columns and values aggregated using given functions. In the resulting DataFrame one row in the end result corresponds with one group.


Parameters:
Parameters:
# columns: Columns to group identified by column indexes or column names.
# '''Columns''': Columns to group identified by column indexes or column names.
# aggregationExpressions: Array containing the name of the column as context object and the value as context value. The value can be either:
# '''Aggregation expressions''': Array containing the column name as a key and the aggregation expression as a value.
#* Expression which is evaluated for each group in the groupping operation.
#* Constant value that is assigned as the value for all the rows.


Examples:
Examples:

Revision as of 08:25, 18 December 2019

DataFrame

DataFrame represents a two dimensional array of data with one-to-many columns and zero-to-many rows, like a relational database table, an Excel sheet or a CSV data file. Each column in the DataFrame has a name, and there must not be more than one column with the same name. DataFrame is the generic data structure used to manage all kinds data in QPR ProcessAnalyzer expression engine that run in-memory. DataFrames as linked to other entities in QPR ProcessAnalyzer as follows:

  • Datatable contents is fetched into the memory as a DataFrame object
  • DataFrame can be stored (persisted) to a Datatable
  • ETL operations, such as joining, unions, filtering and grouping are based on the DataFrames
  • Data extracted from an external data source, e.g. using ODBC, is fetched to the in-memory calculation as a DataFrame.
  • Model can get cases and events data from a DataFrame when using an expression datasource.
DataFrame properties Description
Columns (String*) DataFrame columns names as an array in the order the columns are in the DataFrame.
Rows (Object**) Returns the data content of the DataFrame as a two-dimensional array (matrix). The column names are not part of the data content.

Examples: 

DatatableById(5).DataFrame.Rows[0][0]
Returns: the value in the first row and first column in a datatable with id 5.
<column name> (Object*)

Returns an array of values of given column in the datatable. If the column name contains spaces, the Column function needs to be used to refer to a column.

Examples: 

ToDataFrame([[0, "zero"], [2, "two"], [3, "three"]], ["id", "right"]).right
Returns: [zero, two, three]

ToDataFrame([[0, "zero"], [2, "two"], [3, "three"]], ["id", "right"]).right[2]
Returns: three
DataFrame functions Parameters Description
Append (DataFrame)
  • DataFrame which data to append

Creates a new DataFrame that has the contents of given DataFrame added to the end of this DataFrame. When the data is combined, the order of columns matters, not the names of the columns. The resulting DataFrame gets column names from this DataFrame.

If the number of columns is different between this DataFrame and the other DataFrame, an exception is thrown.

Examples: 

ToDataFrame([[0, "zero"], [2, "two"], [3, "three"]], ["id", "text"]).Append(
  ToDataFrame([[1, "one"], [4, "four"]], ["id", "text"])
);

Returns string:
id;text
0;zero
2;two
3;three
1;one
4;four
ColumnIndexes (Integer*)
  • Column names (String*)

Convert DataFrame column names into column indexes. The indexes are starting from zero. If a column is not found, an exception is given.

Examples: 

ToDataFrame([[0, "zero"], [2, "two"], [3, "three"]], ["id", "right"]).ColumnIndexes(["right", "id"])
Returns: [1, 0]
Column (Object*)
  • Column name

Returns an array of values of given column in the order rows are in the datatable.

Examples: 

ToDataFrame([[0, "zero"], [2, "two"], [3, "three"]], ["id", "right"]).Column("right")
Returns: [zero, two, three]
Columns (DataFrame)
  • Column indexes

Creates a new DataFrame having only the defined columns of the original DataFrame.

Note that Columns function is different than Columns property - difference is that the function has parameters.

Examples: 

ToDataFrame([[0, "zero"], [2, "two"], [3, "three"]], ["id", "right"]).Columns([1]).ToCsv()
Returns string:
right
zero
two
three
Head (DataFrame)
  • Number of rows

Creates a new DataFrame that only contains top number of rows of this DataFrame. If the DataFrame has less than n rows, all its rows are returned.

Examples: 

ToDataFrame([[0, "zero"], [2, "two"], [3, "three"]], ["id", "right"]).Head(2).ToCsv()
Results string:
id;right
0;zero
2;two
Join (DataFrame)
  • Other DataFrame
  • Keys

Performs a joining operation between two DataFrames by given columns. 2. Parameters: 2.1. other: Other DataFrame to join this DataFrame with. 2.2. keys: Identifies columns to be matched with each other. Value can be either: 2.2.1. Single column index (numerical) or name. 2.2.1.1. In this case, the column identified with this value is used in both the DataFrames to match the rows. 2.2.2. An array of any number of: 2.2.2.1. Column identifiers or names 2.2.2.1.1. In this case, the column identified with this value is used in both the DataFrames to match the rows. 2.2.2.2. Hierarchical arrays (#29290#) containing identifiers or names both in their context object and value object. 2.2.2.1.1. In this case, the column identified with the context value is used to identify the column in this DataFrame, whereas the column identified with the value of the context is used to identify the column in the other DataFrame to match the rows. 2.3. joinType: Optional parameter defining the join type. Supported values are: 2.3.1. "Inner": 2.3.1.1. Inner join (row is generated only if both context and other have the key value. 2.3.1.2. This is the default value. 2.3.2. "LeftOuter": 2.3.1.1. Left outer join (at least one row is generated for each context row, even if there is no matching other row, in which case null is given as value for the other columns) 3. Returns a new DataFrame that the result of the join operation. Note that the key-column of the other -DataFrame is not included.

Examples: 

Let("left", ToDataFrame([[0, "zero"], [1, "one"]], ["id", "left"]));
Let("right", ToDataFrame([[0, "zero"], [2, "two"], [3, "three"]], ["id", "right"]));
left.join(right, "id", "inner").ToCsv()
Returns string:
id;left;right
0;zero;zero

Let("left", ToDataFrame([[0, "zero"], [1, "one"]], ["id", "left"]));
Let("right", ToDataFrame([[0, "zero"], [2, "two"], [3, "three"]], ["id", "right"]));
left.join(right, "id", "leftouter").ToCsv()
Returns string:
id;left;right
0;zero;zero
1;one;

Let("left", ToDataFrame([[0, 0, "zerozeroleft"], [0, 1, "zeroleft"], [1, 2, "oneleft"]], ["idleft1", "idleft2", "left"]));
Let("right", ToDataFrame([[0, 0, "zerozeroright"], [0, 1, "zeroright"], [2, 3, "tworight"], [3, 4, "threeright"]], ["idright1", "idright2", "right"]));
left.join(right, ["idleft1": "idright1"], "inner");

Returns string:
idleft1;idleft2;left;idright2;right
0;0;zerozeroleft;0;zerozeroright
0;0;zerozeroleft;1;zeroright
0;1;zeroleft;0;zerozeroright
0;1;zeroleft;1;zeroright

Let(""left"", ToDataFrame([[0, 0, ""zerozeroleft""], [0, 1, ""zeroleft""], [1, 2, ""oneleft""]], [""idleft1"", ""idleft2"", ""left""]));
Let(""right"", ToDataFrame([[0, 0, ""zerozeroright""], [0, 1, ""zeroright""], [2, 3, ""tworight""], [3, 4, ""threeright""]], [""idright1"", ""idright2"", ""right""]));
left.join(right, [""idleft1"": ""idright1"", ""idleft2"": ""idright2""], ""inner"");
Returns string:
idleft1;idleft2;left;right
0;0;zerozeroleft;zerozeroright
0;1;zeroleft;zeroright
GroupBy (DataFrame)
  1. Array of columns to group
  2. Array of grouping expressions

Creates a new DataFrame based on the current DataFrame. The resulting DataFrame has rows grouped by given columns and values aggregated using given functions. In the resulting DataFrame one row in the end result corresponds with one group.

Parameters:

  1. Columns: Columns to group identified by column indexes or column names.
  2. Aggregation expressions: Array containing the column name as a key and the aggregation expression as a value.

Examples: 

ToDataFrame([[0, "zero"], [0, "zero2"], [2, "two"], [2, "two"], [2, "two3"], [3, "three"]], ["id", "text"]).GroupBy(["id"],
[
  "ids": Def("", Sum(id)),
  "texts": Def("", StringJoin(",", text)),
  "constant": 123
]).ToCsv()
Returns string:
ids;texts;constant
0;zero,zero2;123
6;two,two,two3;123
3;three;123

ToDataFrame([[0, "zero"], [0, "zero2"], [2, "two"], [2, "two"], [2, "two3"], [3, "three"]], ["id", "text"]).GroupBy(["id", "text"],
[
  "ids": Def("", Sum(id)),
  "texts": Def("", StringJoin(",", text)),
  "constant": 123
]).ToCsv()
Returns string:
ids;texts;constant
0;zero;123
0;zero2;123
4;two,two;123
2;two3;123
3;three;123

Analysis("OperationLog")
.GroupBy(
  ["User Name"],
  [
    "User Name": Def("", Column("User Name")[0]),
    "Count": Def("Rows", CountTop(Rows)),
    "Avg. Duration": Def("", Average(Duration)),
    "Max. Duration": Def("", Max(Duration))
  ]
).ToCsv()

Returns string (similar to this):
User Name;Count;Avg. Duration;Max. Duration
;207;0.617434782608696;20.556
Administrator;665;16.3750631578947;4225.497
qpr;128;2.158765625;20.346
Merge (DataFrame)

Creates a new DataFrame that has the contents of given (target) DataFrame merged with another (source) DataFrame using method described in parameters. 1.1. Principles follow how MERGE SQL command works in SQL Server. 1.2. In order for the merge to work properly, both the source and target table should have exactly the same columns in exactly the same order. 2. Parameters: 2.1. source: DataFrame to be merged with this (target) DataFrame. 2.2. matchColumns: Identifies columns to be matched with each other. Value can be either: 2.2.1. Single column index (numerical) or name. 2.2.1.1. In this case, the column identified with this value is used in both the DataFrames to match the rows. 2.2.2. An array of any number of: 2.2.2.1. Column identifiers or names 2.2.2.1.1. In this case, the column identified with this value is used in both the DataFrames to match the rows. 2.2.2.2. Hierarchical arrays (#29290#) containing identifiers or names both in their context object and value object. 2.2.2.1.1. In this case, the column identified with the context value is used to identify the column in this DataFrame, whereas the column identified with the value of the context is used to identify the column in the other DataFrame to match the rows. 2.3. copyOnMatch: An array similar to the one in 2.2.2 of column names or indexes of columns to copy from the source to target, if a matching row is found. 2.3.1. If the value is _remove (#27680#), then all the rows for which a match was found will be removed from the resulting table. 2.3.2. If the value is null (#27679#), then the target row will be completely replaced with all the columns in the source row that have identical name in both source and target table. 2.3.3. The default value is null. 2.3. copyIfNotMatchedInTarget: An array similar to the one in 2.3 of column names or indexes of columns to copy from the source to target, if a matching row is not found. 2.4. addIfNotMatchedInTarget: A boolean value that defines whether the source row is added to the result if there is no matching row in the target DataFrame. 2.4.1. The default value is true. 2.5. keepIfNotMatchedBySource: A boolean value that defines whether the target row is added to the result if there is no matching row in the source DataFrame. 2.5.1. The default value is true. 3. Returns a new DataFrame that has the combination of rows from both the DataFrames. 3.1. If the number of columns is different between this DataFrame and the other DataFrame, an exception is thrown.

Examples: 

Let("target", ToDataFrame([[0, "zero", "target"], [1, "", "target"]], ["id", "text", "frame"]));
Let("source", ToDataFrame([[1, "one", "source"], [2, "two", "source"], [3, "three", "source"]], ["id", "text", "frame"]));

target.Merge(source, "id").ToCsv()

Returns string (one key, default parameters, identical dataframe columns):
id;text;frame
0;zero;target
1;one;source
2;two;source
3;three;source

target.Merge(source, "id", ["text"]).ToCsv()

Returns string (one key, default parameters, identical dataframe columns, copy only text column from source):
id;text;frame
0;zero;target
1;one;target
2;two;
3;three;

target.Merge(source, "id", ["text"], _remove).ToCsv()

Returns string (one key, default parameters, identical dataframe columns, copy only text column from source, remove rows found only in source):
id;text;frame
0;zero;target
1;one;target

target.Merge(source, "id", ["text"], _remove, false).ToCsv()

Returns string (one key, identical dataframe columns, copy only text column from source, remove rows found only in source or only in target):
id;text;frame
1;one;target

target.Merge(source, "id", _remove, _remove, false).ToCsv()

Returns string (one key, identical dataframe columns, remove all rows):
id;text;frame

Let("target", ToDataFrame([[0, 0, "zerozeroleft", "target"], [0, 1, "zeroleft", "target"], [1, 2, "left", "target"], [4, 5, "fourleft", "target"]], ["idleft1", "idleft2", "textleft", "frame"]));
Let("source", ToDataFrame([[0, 0, "zerozeroright", "source"], [0, 1, "zeroright", "target"], [1, 2, "oneright", "source"], [2, 3, "tworight", "source"], [3, 4, "threeright", "source"]], ["idright1", "idright2", "textright", "frame"]));
target.Merge(source, ["idleft1": "idright1"]).ToCsv()

Returns string (one key, default parameters, different dataframe columns, copy all matching columns):
idleft1;idleft2;textleft;frame
0;0;zerozeroleft;source
0;0;zerozeroleft;target
0;1;zeroleft;source
0;1;zeroleft;target
1;2;left;source
4;5;fourleft;target
2;;;source
3;;;source

target.Merge(source, ["idleft1": "idright1"], []).ToCsv()

Returns string (one key, default parameters, different dataframe columns, copy only key column):
idleft1;idleft2;textleft;frame
0;0;zerozeroleft;target
0;0;zerozeroleft;target
0;1;zeroleft;target
0;1;zeroleft;target
1;2;left;target
4;5;fourleft;target
2;;;
3;;;

target.Merge(source, ["idleft1": "idright1", "idleft2": "idright2"]).ToCsv()

Returns string (two keys, default parameters, different dataframe columns, copy all matching columns):
idleft1;idleft2;textleft;frame
0;0;zerozeroleft;source
0;1;zeroleft;target
1;2;left;source
4;5;fourleft;target
2;3;;source
3;4;;source

target.Merge(source, ["idleft1": "idright1", "idleft2": "idright2"], ["textleft": "textright"]).ToCsv()

Returns string (two keys, default parameters, different dataframe columns, copy only textright -column):
idleft1;idleft2;textleft;frame
0;0;zerozeroright;target
0;1;zeroright;target
1;2;oneright;target
4;5;fourleft;target
2;3;tworight;
3;4;threeright;

target.Merge(source, ["idleft1": "idright1", "idleft2": "idright2"], ["textleft": "textright", "frame"]).ToCsv()

Returns string (two keys, default parameters, different dataframe columns, copy textright and frame -columns):
idleft1;idleft2;textleft;frame
0;0;zerozeroright;source
0;1;zeroright;target
1;2;oneright;source
4;5;fourleft;target
2;3;tworight;source
3;4;threeright;source

target.Merge(source, ["idleft1": "idright1", "idleft2": "idright2"], _remove).ToCsv()

Returns string (two keys, default parameters, different dataframe columns, remove all matching rows, copy only matching columns):
idleft1;idleft2;textleft;frame
4;5;fourleft;target
2;3;;
3;4;;

target.Merge(source, ["idleft1": "idright1", "idleft2": "idright2"], ["textleft": "textright"], ["idleft1": "idright1", "idleft2": "idright2", "textleft": "textright"]).ToCsv()

Returns string (two keys, default parameters, different dataframe columns, copy only textright-column for matching columns, copy id columns and textright-column for rows not found in target):
idleft1;idleft2;textleft;frame
0;0;zerozeroright;target
0;1;zeroright;target
1;2;oneright;target
4;5;fourleft;target
2;3;tworight;
3;4;threeright;

target.Merge(source, ["idleft1": "idright1", "idleft2": "idright2"], ["textleft": "textright"], ["idleft1": "idright1", "idleft2": "idright2", "textleft": "textright", "frame"]).ToCsv()

Returns string (two keys, default parameters, different dataframe columns, copy only textright-column for matching columns, copy id, frame and textright-column for rows not found in target):
idleft1;idleft2;textleft;frame
0;0;zerozeroright;target
0;1;zeroright;target
1;2;oneright;target
4;5;fourleft;target
2;3;tworight;source
3;4;threeright;source

target.Merge(source, ["idleft1": "idright1", "idleft2": "idright2"], null, ["idleft1": "idright1", "idleft2": "idright2", "textleft": "textright"]).ToCsv()

Returns string (two keys, default parameters, different dataframe columns, don't copy any columns from source for matching columns, copy id columns and textright-column for rows not found in target):
idleft1;idleft2;textleft;frame
0;0;zerozeroleft;source
0;1;zeroleft;target
1;2;left;source
4;5;fourleft;target
2;3;tworight;
3;4;threeright;

target.Merge(source, ["idleft1": "idright1", "idleft2": "idright2"], ["textleft": "textright"], ["idleft1": "idright1", "idleft2": "idright2", "textleft": "textright", "frame"], false).ToCsv()

Returns string (two keys, default parameters, different dataframe columns, copy only textright-column for matching columns, copy id, frame and textright-column for rows not found in target, remove all rows not found in source):
idleft1;idleft2;textleft;frame
0;0;zerozeroright;target
0;1;zeroright;target
1;2;oneright;target
2;3;tworight;source
3;4;threeright;source
OrderBy (DataFrame)
  • ordering expression

Creates a new DataFrame having rows ordered in an ascending order using the given expression evaluated on each row.

Examples: 

ToDataFrame([[0, "zero"], [2, "two"], [3, "three"]], ["id", "text"]).OrderBy(text).ToCsv()
Returns string:
id;right
3;three
2;two
0;zero

Analysis("OperationLog").OrderBy(Duration).Head(1)
Results string:
id;right
0;zero
2;two
3;three
OrderByDescending (DataFrame)
  • ordering expression

Creates a new DataFrame having rows ordered in an descending order using the given expression evaluated on each row. See OrderBy above for examples.

Persist (DataTable)
  • DataTable name
  • Additional parameters

Writes the DataFrame into QPR ProcessAnalyzer database as a DataTable having the given name. If a DataTable with that name does not exist in the project, a new DataTable is created. If a DataTable with that name already exists, the DataFrame will be stored into that DataTable. The function returns a DataTable object which the data was persisted into.

The additional parameters support:

  • Append: Can be used to determine whether to append (true) or overwrite (false) the existing data. Default is overwrite.
  • ProjectName: Name of the project under which the DataTable is to be created.
  • ProjectId: Id of the project under which the DataTable is to be created.

Examples: 

Let("right", ToDataFrame([[0, "zero"], [2, "two"], [3, "three"]], ["id", "right"]));
right.Persist("RightDataTable", ["ProjectName": "TestData"])
Results: Id of the new data table named "RightDataTable" created into project named TestData (which is created if it doesn't already exist). If the table already existed, its contents will be overwritten by the new content.

Let("newData", ToDataFrame([[4, "four"]], ["id", "right"]));
newData.Persist("RightDataTable", ["ProjectName": "TestData", "Append": true])
Results: Id of the new data table named "RightDataTable" created into project named TestData (which is created if it doesn't already exist). If the table already existed, new content will be appended into the end of the table.
SetColumns (DataFrame)
  • new/modified columns as array

Creates a new DataFrame based on the current DataFrame, where new columns have been created and/or existing columns have been modified. New and modified columns are defined using an array, where the column name is as a key and as a value there is the expression to calculate the new or modified column. When specifying a column that already exists, the column values are modified. When specifying a new column name, that column is created as a new column to the resulting DataFrame.

Examples: 

ToDataFrame([[0, "zero"], [2, "two"], [3, "three"]], ["id", "text"]).SetColumns(["both": Def("", text + "=" + id)]).ToCsv()
Result string:
id;text;both
0;zero;zero=0
2;two;two=2
3;three;three=3

ToDataFrame([[0, "zero"], [2, "two"], [3, "three"]], ["id", "text"]).SetColumns(["text": Def("", text + "=" + id)]).ToCsv()
Result string:
id;text
0;zero=0
2;two=2
3;three=3

ToDataFrame([[0, "zero"], [2, "two"], [3, "three"]], ["id", "text"]).SetColumns(["both": Def("", text + "=" + id), "both+1": Def("", both + 1), "text": Def("", "Done: " + Column("both+1")), "constant": 1234]).ToCsv()
Result string:
id;text;both;both+1;constant
0;Done: zero=01;zero=0;zero=01;1234
2;Done: two=21;two=2;two=21;1234
3;Done: three=31;three=3;three=31;1234
Tail (DataFrame)
  • Number of rows

Creates a new DataFrame that has only the bottom number of rows of this DataFrame. If the DataFrame has less than n rows, all its rows are returned.

Example: 

ToDataFrame([[0, "zero"], [2, "two"], [3, "three"]], ["id", "right"]).Tail(2).ToCsv()
Results string:
id;right
2;two
3;three
ToCsv (String) (none)

Converts a DataFrame into a CSV data. The CSV data has the following formatting:

  • Column separator: semicolon
  • Decimal separator in numeric fields: period
  • Quotation character for text fields: double quotes (used when the textual value contains semicolon, double quotes, linebreak or tabulator)
  • Escape character: Double quotes in textual fields are escaped with two double quotes.
  • Date format for date fields: yyyy-MM-dd HH:mm:ss,fff
  • First line: contains column headers

Example: 

ToDataFrame([[0, "zero"], [2, "two"], [3, "three"]], ["id", "right"]).ToCsv()
Returns string:
id;right
0;zero
2;two
3;three
Where (DataFrame)
  • condition expression

Creates a new DataFrame having only rows for which the given condition expression returns true. The condition expression can refer to the columns of the DataFrame (see the example below).

Examples: 

Let("df", ToDataFrame([[0, "zero"], [2, "two", true], [3, "three"]], ["id", "string"]));

All the following expression return the same:
df.Where(id < 3);
df.Where(Column("id") < 3);
df.Where(_[0] < 3);

Returns string:
id;string
0;zero
2;two
Zip (DataFrame)
  • DataFrame

Creates a new DataFrame that has the contents of given DataFrame appended as new columns into the end of this DataFrame. Returns a new DataFrame that has the colums from both the data frames so that the columns from the other DataFrame are appended to the end of the columns of this DataFrame. If the number of rows is different between this DataFrame and the other DataFrame, an exception is thrown. There must not be duplicate column names in the DataFrames - otherwise an exception is thrown.

Examples: 

Let("df1", ToDataFrame([[0, "zero"], [1, "one"], [4, "four"]], ["id", "text"]));
Let("df2", ToDataFrame([[1, "one"], [2, "two"], [3, "three"]], ["id2", "text2"]));
df1.Zip(df2).ToCsv();
Returns string:
id;text;id2;text2
0;zero;1;one
1;one;2;two
4;four;3;three

Datatable

Datatables are used to store data in QPR ProcessAnalyzer persistently. Datatables consist of one-to-many named columns and zero-to-many rows. The Datatable entity in the expression language is used to access Datatables metadata, such as name, description and audit information. The actual data contents can be accessed by using the DataFrame property, which causes the actual data to be loaded from the database into memory for calculations.

Datatables have many usecases:

  • project can load their data from Datatables.
  • Datatables can be used to store data extracted from external systems
  • Intermediate ETL calculation results can be stored to Datatables
  • It's possible to visualize data from Datatables directly in the dashboards without building a QPR ProcessAnalyzer model

Datatables are stored to QPR ProcessAnalyzer database as database tables. Each datatable belongs to a project.

Datatable properties Description
CreatedDate (DateTime) Timestamp when the datatable was created.
DataFrame (DataFrame) Contents of the DataTable as a DataFrame.
Description (String) Datatable description. The datatable description may contain line breaks.
Id (Integer) Datatable Id. Datatable Id is generated by QPR ProcessAnalyzer when the datatable is created.
LastImportDate (DateTime) Timestamp when data was the last time imported to the datatable.
LastModifiedDate (DateTime) Timestamp when the datatable was modified the last time.
Name (String) Name of the datatable.
NColumns (Integer) Number of columns in the datatable.
NRows (Integer) Number of data rows in the datatable.
Project (Project) Project the datatable belongs to.

Model

Model properties Description
CaseAttributes (AttributeType*) CaseAttributes in the model returned in the alphabetical order. Using this property requires that the model is loaded in the memory. If the model is not in the memory, it's loaded when this property is used.
ConfigurationJson (String) Returns an model configuration in JSON format.
CreatedDate (DateTime) Timestamp when the model was created.
DefaultFilterId (Integer) Default filter id of the model.
ConfigurationJson (String) Returns an model configuration in JSON format.
Description (String) Model description. The model description may contain line breaks.
EstimatedMemory (Integer) Returns an estimation of how much memory in bytes the model requires.
EventAttributes (AttributeType*) EventAttributes in the model returned in the alphabetical order. Using this property requires that the model is loaded in the memory. If the model is not in the memory, it's loaded when this property is used.
EventLog (EventLog) Model EventLog containing all events in the model, i.e. no filters have been applied. Using this property requires that the model is loaded in the memory. If the model is not in the memory, it's loaded when this property is used.
Id (Integer) Model Id. Model Id is generated by QPR ProcessAnalyzer when the model is created.
LastImportDate (DateTime) Timestamp when data was the last time imported to the model.
LastModifiedDate (DateTime) Timestamp when the model was modified the last time.
Name (String) Model name.
NBookmarks (Integer) Number of bookmarks in the model.
NCache (Integer) Amount of objects related to the model when the model is loaded into the memory.
NCaseAttributes (Integer) Number of CaseAttributes in model.
NCases (Integer) Number of Cases in the model.
NEventAttributes (Integer) Number of EventAttributes in model.
NEvents (Integer) Number of Events in model.
NEventTypes (Integer) Number of EventTypes in the model.
NFilters (Integer) Number of filters in the model.
NOpens (Integer) Number of times analysis has been requested from the model.
Project (Project) Project the model belongs to.
ProjectId (Integer) Project id the model belongs to.
Status (String)

Memory availability status of the model. There are the following statuses:

  • Loading: The model is currently loading into the memory. When the loading is ready, the status changes to online. If the loading fails, the status changes to offline.
  • Offline: The model is currently not loaded into the memory. The model needs to be loaded into the memory, so that analyses can be calculated from the model (occurs automatically when an analysis is requested).
  • Online: The model is in the memory and ready for analysis calculation. If the model is dropped from the memory, its status changes to offline.

Notes:

  • Regarding properties returning object counts: For models downloaded from a datatable, ODBC or expression datasource, the numbers represent the situation in the previous data load. Null is returned for models downloaded from an ODBC datasource, if the model has never been loaded into the memory. Note also that if Case permissions are used for the model, null is returned for data security reasons.
  • Using CaseAttributes, EventAttributes or Eventlog properties of the Model object requires the model to be loaded into the memory. If the model is not in the memory, it's loaded when these properties is called. Other Model object properties down require the model to be in the memory.

Project

Project properties Description
Id (Integer) Id of the Project. It's generated by QPR ProcessAnalyzer when the model is loaded.
Name (String) Name of the Project.
Models (Model*) Models that are in the Project.

User/Group

User objects represents users and user groups. Note that some properties can only be used for users and some for groups.

User properties Description
Description (String) Description of the user.
Email (String) Email address of the user.
FullName (String) Full name of the user or group name.
GroupMemberNames (String*) Array of names of members of a user group. This property is available for groups.
GroupMembers (User*) Array of members of a user group. This property is available for groups.
GroupNames (String*) Array of names of user groups the user belongs to. This property is available for users.
Groups (User*) Array of user groups the user belongs to. This property is available for users.
Id (Integer) Id of the user, which is unique for every user.
IsActive (Boolean) Returns true only if the user is active (not disabled).
IsGroup (Boolean) Returns true if the user is a user group.
LogonMessage (String) Returns the logon message of the user.
Name (String) Login name of the user or group.
Roles (String Array*) Roles of user as string array.
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