Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that adjust a high-pressure feeder of a feed system used in pulp production based on the fluid leakage through a low-pressure outlet of a high-pressure feeder. Methods can include obtaining multiple flow values, including (1) a make-up liquor flow value, (2) a black liquor flow value, (3) a white liquor flow value, (4) a chip chute circulation flow value, and (5) a high pressure feeder purge flow value. Methods can include determining a chip flow value specifying a flow of chips provided to the high-pressure feeder. Based on the flow values, methods can determine a fluid leakage value specifying an amount of fluid leakage through a gap between a pocket rotor and the housing of the high pressure feeder. Methods can adjust an annular gap based on the fluid leakage value satisfies a threshold leakage value.

Abstract: In accordance with the teachings described herein, systems and methods are provided for estimating or determining quantiles for data stored in a distributed system. In one embodiment, an instruction is received to estimate or determine a specified quantile for a variate in a set of data stored at a plurality of nodes in the distributed system. A plurality of data bins for the variate are defined that are each associated with a different range of data values in the set of data. Lower and upper quantile bounds for each of the plurality of data bins are determined based on the total number of data values that fall within each of the plurality of data bins. The specified quantile is estimated or determined based on an identified one of the plurality of data bins that includes the specified quantile based on the lower and upper quantile bounds.

Abstract: In accordance with the teachings described herein, systems and methods are provided for estimating or determining quantiles for data stored in a distributed system. In one embodiment, an instruction is received to estimate or determine a specified quantile for a variate in a set of data stored at a plurality of nodes in the distributed system. A plurality of data bins for the variate are defined that are each associated with a different range of data values in the set of data. Lower and upper quantile bounds for each of the plurality of data bins are determined based on the total number of data values that fall within each of the plurality of data bins. The specified quantile is estimated or determined based on an identified one of the plurality of data bins that includes the specified quantile based on the lower and upper quantile bounds.

Abstract: In accordance with the teachings described herein, systems and methods are provided for estimating quantiles for data stored in a distributed system. In one embodiment, an instruction is received to estimate a specified quantile for a variate in a set of data stored at a plurality of nodes in the distributed system. A plurality of data bins for the variate are defined that are each associated with a different range of data values in the set of data. Lower and upper quantile bounds for each of the plurality of data bins are determined based on the total number of data values that fall within each of the plurality of data bins. The specified quantile is estimated based on an identified one of the plurality of data bins that includes the specified quantile based on the lower and upper quantile bounds.

Abstract: In accordance with the teachings described herein, systems and methods are provided for estimating quantiles for data stored in a distributed system. In one embodiment, an instruction is received to estimate a specified quantile for a variate in a set of data stored at a plurality of nodes in the distributed system. A plurality of data bins for the variate are defined that are each associated with a different range of data values in the set of data. Lower and upper quantile bounds for each of the plurality of data bins are determined based on the total number of data values that fall within each of the plurality of data bins. The specified quantile is estimated based on an identified one of the plurality of data bins that includes the specified quantile based on the lower and upper quantile bounds.

Abstract: In accordance with the teachings described herein, systems and methods are provided for estimating quantiles for data stored in a distributed system. In one embodiment, an instruction is received to estimate a specified quantile for a variate in a set of data stored at a plurality of nodes in the distributed system. A plurality of data bins for the variate are defined that are each associated with a different range of data values in the set of data. Lower and upper quantile bounds for each of the plurality of data bins are determined based on the total number of data values that fall within each of the plurality of data bins. The specified quantile is estimated based on an identified one of the plurality of data bins that includes the specified quantile based on the lower and upper quantile bounds.

Abstract: In accordance with the teachings described herein, systems and methods are provided for estimating quantiles for data stored in a distributed system. In one embodiment, an instruction is received to estimate a specified quantile for a variate in a set of data stored at a plurality of nodes in the distributed system. A plurality of data bins for the variate are defined that are each associated with a different range of data values in the set of data. Lower and upper quantile bounds for each of the plurality of data bins are determined based on the total number of data values that fall within each of the plurality of data bins. The specified quantile is estimated based on an identified one of the plurality of data bins that includes the specified quantile based on the lower and upper quantile bounds.

Abstract: Systems and methods are provided for identifying data variable roles during initial data exploration. A variable type, unique data value count values, and an overflow count value are determined for a variable. The unique data value count values include a number of occurrences of each of a plurality of unique data values for the variable in a data set. The overflow count value is a number of occurrences of data values other than the plurality of unique data values for the variable in the data set. When a number of the plurality of unique data values is greater than a value for a high cardinality threshold, the variable is determined to be a high cardinality variable. When the variable is not determined to be the high cardinality variable, a class variable role is assigned to the variable. When the variable is determined to be the high cardinality variable, Whether or not the variable is a numeric variable type is determined based on the determined variable type.

Abstract: Systems and methods are provided for identifying data variable roles during initial data exploration. A variable type, unique data value count values, and an overflow count value are determined for a variable. The unique data value count values include a number of occurrences of each of a plurality of unique data values for the variable in a data set. The overflow count value is a number of occurrences of data values other than the plurality of unique data values for the variable in the data set. When a number of the plurality of unique data values is greater than a value for a high cardinality threshold, the variable is determined to be a high cardinality variable. When the variable is not determined to be the high cardinality variable, a class variable role is assigned to the variable. When the variable is determined to be the high cardinality variable, Whether or not the variable is a numeric variable type is determined based on the determined variable type.

Abstract: Systems and methods are provided for identifying data variable rules during initial data exploration. In one example, a computer-implemented method of determining a role for a data variable is disclosed. The method comprises identifying to a plurality of data nodes a set of data records containing data values assigned to each data node, a maximum number of levels to record in a sorted data structure at the data nodes, and the data node responsible for each of a plurality of variables. The method further comprises receiving for each variable from the data node responsible for the variable a plurality of unique data values for the variable, a count for each of the unique data values and an overflow count for the variable, wherein the number of unique data values does not exceed the maximum number of levels. A role for a variable can be determined based upon the unique data values, counts and overflow count for the variable.

Abstract: Systems and methods are provided for identifying data variable rules during initial data exploration. In one example, a computer-implemented method of determining a role for a data variable is disclosed. The method comprises identifying to a plurality of data nodes a set of data records containing data values assigned to each data node, a maximum number of levels to record in a sorted data structure at the data nodes, and the data node responsible for each of a plurality of variables. The method further comprises receiving for each variable from the data node responsible for the variable a plurality of unique data values for the variable, a count for each of the unique data values and an overflow count for the variable, wherein the number of unique data values does not exceed the maximum number of levels. A role for a variable can be determined based upon the unique data values, counts and overflow count for the variable.

Abstract: In accordance with the teachings described herein, systems and methods are provided for estimating quantiles for data stored in a distributed system. In one embodiment, an instruction is received to estimate a specified quantile for a variate in a set of data stored at a plurality of nodes in the distributed system. A plurality of data bins for the variate are defined that are each associated with a different range of data values in the set of data. Lower and upper quantile bounds for each of the plurality of data bins are determined based on the total number of data values that fall within each of the plurality of data bins. The specified quantile is estimated based on an identified one of the plurality of data bins that includes the specified quantile based on the lower and upper quantile bounds.

Abstract: A method and computer controlled apparatus to control fluid leakage in a high pressure feeder and a stationary housing with a chamber in which rotates a pocket rotor. The method includes: monitoring the fluid leakage from the high pressure feeder, wherein the fluid leakage is discharged from a low pressure outlet of the high pressure feeder; determining whether the fluid leakage is within a predefined range of acceptable fluid leakage, and moving the pocket rotor in the chamber to adjust the fluid leakage.