Methods and systems to complete transaction date

• US 20070043542A1
• Filed: 08/22/2005
• Published: 02/22/2007
• Est. Priority Date: 08/22/2005
• Status: Active Grant

• Current Assignee: Alvaria Incorporated
• Original Assignee: Aspect Communications Corporation (Alvaria Incorporated)
• Inventors: Lewis, Donald
• Granted Patent: US 7,778,861 B2
• Time in Patent Office: Days
• US Class Current: 703/2
• 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 computer-implemented 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 into 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.

2. The computer-implemented method of claim 1 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_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.

3. The computer-implemented method of claim 1 wherein the first algorithm is used when processing time for filling in the gap is to be minimized.

4. The computer-implemented method of claim 1 wherein the second algorithm is used when accuracy for filling in the gap is to be maximized.

6. The computer-implemented method of claim 1, including forecasting future transaction activity utilizing the complete transaction data

7. A machine-readable medium storing a sequence of instructions that, when executed by a computer, cause the computer to perform the method of claim 1.

5. The computer-implemented method of claim I 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;

8. A system comprising:

an interface to receive transaction data; and

a transaction gap module 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.

9. The system of claim 8 wherein the transaction data module embodies 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 R₁0, 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=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.

10. A system comprising:

an interface to receive transaction data;

a transaction gap module 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.

11. The system of claim 10 wherein the gap includes at least one of a data error and a data omission.

12. The system of claim 10 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;

13. 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.

14. A system comprising:

means for receiving transaction data;

means for determining a gap in the transaction data;

means for determine transaction data to fill in the gap based on a set of substitute data from among a group of substitute data sets; and

means for adopting the set of substitute data into the gap.

15. The system of claim 14 wherein the means for determining transaction data includes using a Moore-Penrose pseudo-inverse algorithm.