Methods and systems to complete transaction date

• US 7,778,861 B2
• Filed: 08/22/2005
• Issued: 08/17/2010
• Est. Priority Date: 08/22/2005
• Status: Active Grant

• Current Assignee: Alvaria Incorporated
• Original Assignee: Aspect Software Incorporated
• Inventors: Lewis, Donald C.
• Primary Examiner(s): Jeanty; Romain
• Assistant Examiner(s): FEENEY, BRETT A
• Application Number:
  US11/209,568
• Publication Number:
  US 20070043542A1
• Time in Patent Office: 1,821 Days
• Field of Search: None
• US Class Current: 705/7.11
• CPC Class Codes: G06Q 10/063 Operations research, analysis or management
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: H04M 3/523 with call distribution or queueing

Abstract

A method and system to receive transaction data; determine a gap in the transaction data; and use an algorithm to generate data to fill in the gap is described. The algorithm is selected from a group including a first algorithm and a second algorithm. The first algorithm is to determine a dominant pattern in the transaction data; identify a region within the dominant pattern that corresponds to the gap in the transaction data; and adopt data associated with the corresponding region into the gap to minimize impact on the dominant pattern. The second algorithm includes a Moore-Penrose pseudo-inverse algorithm to choose the transaction data to fill in the gap based on a set of substitute data from among a group of substitute data sets and adopts the set of substitute data into the gap.

Claims

1. A method comprising:

receiving incomplete transaction data at an interface to a processing circuit;

determining by the processing circuit a gap in the incomplete transaction data; and

using an algorithm implemented by the processing circuit to generate data to fill in the gap and to generate complete transaction data, wherein the algorithm is selected by the processing circuit from a group including at least a first algorithm and a second algorithm,

wherein the first algorithm is automatically to;

determine a dominant pattern in the transaction data;

identify a region within the dominant pattern that corresponds to the gap in the transaction data; and

adopt data associated with the corresponding region in the gap to minimize impact on the dominant pattern;

wherein the second algorithm includes a Moore-Penrose pseudo-inverse algorithm to choose at least a portion of the transaction data to fill in the gap based on a set of substitute data from among a group of substitute data sets and to adopt the set of substitute data into the gap; and

where the first algorithm includes

(i, j) referring to a j^th day of an i^th week, for n weeks with m days in each week,

wherein x_ij includes valid numerical data, and if the data is not valid on (i,j), x_ij=null,

wherein v_ij includes v_ij=x_ij, unless x_ij=0, in which case, v_ij=null,

wherein w_ij includes w_ij=ln(v_ij) whenever v_ij is not null, and w_ij=null whenever v_ij=null,

wherein a matrix of column differences, c_ij, includes c_ij=w_ij+1−

w_ij whenever both w_ij+1 and w_ij are not null, and c_ij=null, otherwise,

wherein a matrix of row differences, r_ij, includes r_ij=w_i+1j−

w_ij whenever both w_i+1j and w_ij are not null, and r_ij=null, otherwise,

wherein a j^th column of c_ij includes at least one non-null entry, and c_*j includes an average of each non-null entry in the j^th column of c_ij, otherwise,c_*j=0,

wherein an i^th row of r_ij includes at least one non-null entry, and r_i* includes an average of each non-null entry in the i^th row of r_ij, otherwise, r_i*=0,

wherein C_j+1=C_j+c_*j,where C₁=0,

wherein R_i+1, =R_i+r_i*, where R₁=0,

wherein u_ij+R_I+C_j,

wherein K includes an average of w_ij−

u_ij over each (i, j) entry where w_ij is not null,

wherein y_ij=w_ij whenever w_ij is not null and otherwise, y_ij=K+u_ij,

wherein output z_ij=Round(exp(y_ij)), wherein the output z_ij corresponds to filling in the gap.

2. The method of claim 1 wherein the algorithm is selected based upon at least one of amount of the transaction data, forecasting module restrictions, and a ratio of valid data to gap data.

3. The method of claim 1 wherein the algorithm is selected based upon one of maximizing accuracy for filling in the gaps, and minimizing processing time for filling in the gap.

4. The method of claim 1

wherein the second algorithm includes an equation Fh=g, wherein Fh=g includes a plurality of solutions, for h, wherein a solution from the plurality of solutions that is selected to fill in the gap is the solution for h, such that ∥

h∥

is minimized solving for h=F⁺g, wherein a pseudoinverse of F includes F⁺, wherein vectors h and g include;

5. The method of claim 1, including forecasting future transaction activity utilizing the complete transaction data.

6. A machine-readable storage medium storing a sequence of instructions that, when executed by a computer, cause the computer to perform the method comprising:

receiving incomplete transaction data;

determining a gap in the incomplete transaction data; and

using an algorithm to generate data to fill in the gap and to generate complete transaction data, wherein the algorithm is selected from a group including a first algorithm and a second algorithm,

wherein the first algorithm is automatically to;

determine a dominant pattern in the transaction data;

identify a region within the dominant pattern that corresponds to the gap in the transaction data; and

adopt data associated with the corresponding region in the gap to minimize impact on the dominant pattern; and

wherein the second algorithm includes a Moore-Penrose pseudo-inverse algorithm to choose at least a portion of the transaction data to fill in the gap based on a set of substitute data from among a group of substitute data sets and to adopt the set of substitute data into the gap; and

wherein the first algorithm includes

(i, j) referring to a j^th day of an i^th week, for n weeks with m days in each week,

wherein x_ij, includes valid numerical data, and if the data is not valid on (i, j), x_ij=null,

wherein v_ij includes v_ij=x_ij, unless x_ij=0, in which case, v_ij=null,

wherein w_ij includes w_ij=ln(v_ij) whenever v_ij is not null, and w_ij=null whenever v_ij=null,

wherein a matrix of column differences, c_ij, includes c_ij=−

w<sub>ij+1−

wij</sub> whenever both w_ij+1 and w_ij are not null, and c_ij=null, otherwise,

wherein a matrix of row differences, r_ij, includes r_ij=w_i+1j−

w_ijwhenever both w_i+1j and w_ij are not null, and r_ij=null, otherwise,

wherein a j^th column of c_ij includes at least one non-null entry, and c_*j includes an average of each non-null entry in the j^th column of c_ij, otherwise, c_*j=0,

wherein an i^th row of r_ij includes at least one non-null entry, and r_i* includes an average of each non-null entry in the i^th row of r_ij, otherwise, r_i*=0,

wherein C_j+1=C_j+c_*j, where C₁=0,

wherein R_i+1=R_i+r_i*,where R₁=0,

wherein u_ij=R_i+C_j,

wherein K includes an average of w^ij−

u_ij over each (i, j) entry where w^ij is not null,

wherein y_ij=w_ij whenever w_ij is not null and otherwise, y_ij=K+u_ij,

wherein output z_ij=Round(exp(y_ij)), wherein the output z_ij corresponds to filling in the gap.

7. A system comprising:

an interface to receive transaction data; and

a transaction gap processor module configured to;

determine a gap in the transaction data;

determine a dominant pattern in the transaction data;

identify a region within the dominant pattern that corresponds to the gap in the transaction data; and

adopt data associated with the corresponding region into the gap to minimize impact on the dominant pattern wherein the transaction gap processor module incorporates an algorithm that includes a formula for output z_ij=Round(exp(y_ij)), wherein the output z_ij corresponds to filling in the gap, wherein (i, j) refers to a j^th day of an i^th week, for n weeks with m days in each week, wherein y_ij=w_ij whenever w_ij is not null and otherwise, y_ij=K +u_ij, wherein K includes an average of w_ij−

u_ij over each (i, j) entry where w_ij is not null,

wherein C_j+1=C_j+c_*j, where C₁=0, wherein R_i+1=R_i+r_i*, where R₁=0, wherein u_ij=R_i+C_j,

wherein a matrix of column differences, c_ij, includes c_ij=w_ij=1−

w_ij whenever both w_ij+1 and w_ij are not null, and c_ij=null, otherwise,

wherein a matrix of row differences, r_ij, includes r_ij=w_i+1j−

w_ij whenever both w_i+1j and w_ij are not null, and r_ij=null, otherwise,

wherein a j^th column of c_ij includes at least one non-null entry, and c_*j includes an average of each non-null entry in the j^th column of c_ij, otherwise, c_*j=0,

wherein an i^th row of r_ij includes at least one non-null entry, and r_i* includes an average of each non-null entry in the i^th row of r_ij, otherwise, r_i*=0, wherein x_ij includes valid numerical data, and if the data is not valid on (i, j), x_ij=null, wherein v_ij includes v_ij=x_ij, unless x_ij=0, in which case, v_ij=null, wherein w_ij includes w_ij=ln(v_ij) whenever v_ij is not null, and w_ij=null whenever v_ij=null.

8. A system comprising:

an interface to receive transaction data;

a transaction gap processor module configured to;

determine a gap in the transaction data;

use a Moore-Penrose pseudo-inverse algorithm to determine transaction data to fill in the gap based on a set of substitute data from among a group of substitute data sets; and

adopt the set of substitute data into the gap;

wherein the transaction gap module includes

an equation Fh=g, wherein Fh=g includes a plurality of solutions, for h, wherein a solution from the plurality of solutions that is selected to fill in the gap is the solution for h, such that ∥

h∥

is minimized solving for h=F⁺g, wherein a pseudoinverse of F includes F⁺, wherein vectors h and g include;

9. A system of claim 8 wherein the gap includes at least one of a data error and a data omission, and the transaction data comprises data regarding frequency of transactions during periods of time.

10. A system comprising:

means for receiving transaction data;

means for determining a gap in the transaction data;

means for determining a dominant pattern in the transaction data;

means for identifying a region within the dominant pattern that corresponds to the gap in the transaction data; and

means for adopting data associated with the corresponding region into the gap to minimize impact on the dominant pattern and wherein the means for determining a gap incorporates an algorithm that includes a formula for output z_ij=Round(exp(y_ij)), wherein the output z_ij corresponds to filling in the gap, wherein (i, j) refers to a j^th day of an i^th week, for n weeks with m days in each week, wherein y_ij=w_ij whenever w_ij is not null and otherwise, y_ij=K +u_ij, wherein K includes an average of w_ij−

u_ij over each (i, j) entry where w_ij is not null,

wherein C_j+1=C_j+c*j, where C₁=0, wherein R_i+1=R_i+r_i*, where R₁=0, wherein u_ij=R_i+C_j,

wherein a matrix of column differences, c_ij, includes c_ij=w_ij+1−

w_ij whenever both w_ij+1 and w_ij are not null, and c_ij=null, otherwise,

wherein a matrix of row differences, r_ij, includes r_ij=w_i+1j−

w_ij whenever both w_i+1j and w_ij are not null, and r_ij=null, otherwise,

wherein a j^th column of c_ij includes at least one non-null entry, and c_*j includes an average of each non-null entry in the j^th column of c_ij, otherwise, c_*j=0,

wherein an i^th row of r_ij includes at least one non-null entry, and r_i* includes an average of each non-null entry in the i^th row of r_ij, otherwise, r_i*=0, wherein x_ij includes valid numerical data, and if the data is not valid on (i, j), x_ij=null, wherein v_ij includes v_ij=x_ij, unless x_ij=0,in which case, v_ij=null, wherein includes w_ij=ln(v_ij) whenever v_ij is not null, and w_ij=null whenever v_ij=null.