Method and system for predicting user activity levels associated with an application

• US 7,269,599 B2
• Filed: 12/01/2005
• Issued: 09/11/2007
• Est. Priority Date: 12/01/2005
• Status: Expired due to Fees

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Andreev, Dmitry, Grunin, Galina, Vilshansky, Gregory, Greenstein, Paul Gregory
• Primary Examiner(s): Vital; Pierre
• Assistant Examiner(s): Chempakaseril; Ann J
• Application Number:
  US11/292,182
• Publication Number:
  US 20070130097A1
• Time in Patent Office: 649 Days
• Field of Search: 706/45, 706/46, 726/22, 709/224, 707/3, 707/101, 707/102
• US Class Current: 1/1
• CPC Class Codes: G06F 11/3409 for performance assessment
• CPC Class Codes: G06F 11/3447 Performance evaluation by modeling
• CPC Class Codes: G06F 2201/87 Monitoring of transactions
• CPC Class Codes: Y10S 707/99943 Generating database or data structure, e.g. via user interface

Abstract

A method and system for predicting a user activity level associated with an application. An activity level is a number of transactions performed by users utilizing the application per time period or a number of users utilizing the application per time period. Measurements of activity levels are assigned to a user activity metric (UAM) variable, and associated values are assigned to a set of factors. At least one correlation coefficient between each factor and the UAM is calculated. In response to a maximum correlation coefficient associated with a factor being less than a pre-defined threshold, the factor is excluded from the set of factors to facilitate forming a subset of factors associated with correlation coefficients whose absolute values are greater than or equal to the pre-defined threshold. A regression model utilizing the subset is generated to predict an activity level.

Claims

1. A method of predicting a user activity level associated with an application executing on a computing system in a multi-user computing environment, comprising:

defining a user activity metric (UAM) as a variable, said UAM being assigned a plurality of activity levels, wherein each activity level of said plurality of activity levels is a measured number of transactions performed by a plurality of users utilizing said application during a time period of a plurality of time periods, or a measured number of said users utilizing said application during said time period;

defining a plurality of factors associated with said plurality of activity levels, said plurality of factors including a calendar type factor, said calendar type factor capable of being assigned a plurality of values of a cyclic time scale, wherein each value of said plurality of values represents an amount of time elapsed from a specified event;

assigning said plurality of activity levels to said UAM;

assigning said plurality of values to said calendar type factor of said plurality of factors, said plurality of values associated with said plurality of activity levels in a one-to-one correspondence, and with said plurality of time periods in a one-to-one correspondence;

adjusting, subsequent to said assigning said plurality of values to said calendar type factor, said plurality of values to a plurality of adjusted values, said plurality of adjusted values including a pre-defined base value and one or more other adjusted values, wherein an adjusted value of said one or more other adjusted values represents a distance to said pre-defined base value, and wherein said adjusting includes facilitating a representation of a linear dependency between said plurality of adjusted values and said plurality of activity levels;

calculating a plurality of coefficients of correlation, said calculating said plurality of coefficients of correlation including calculating a first coefficient of correlation between said plurality of adjusted values and said plurality of activity levels;

determining that an absolute value of said first coefficient of correlation is less than a pre-defined threshold value;

excluding, in response to said determining, said calendar type factor from said plurality of factors, said excluding including forming a subset of said plurality of factors, said subset including one or more factors, wherein an absolute value of a second coefficient of correlation between a set of values assigned to any factor of said one or more factors and said plurality of activity levels is greater than or equal to said pre-defined threshold value; and

generating a regression model to predict an activity level, said regression model based on said plurality of activity levels and said subset.

2. The method of claim 1, further comprising:

collecting, in real time, one or more sets of one or more values, said one or more sets of one or more values associated with said one or more factors included in said subset in a one-to-one correspondence; and

calculating said activity level via said regression model utilizing said one or more sets of one or more values, wherein said activity level is a number of transactions to be performed by said plurality of users utilizing said application during a future time period or a number of said users to be utilizing said application during said future time period.

3. The method of claim 2, further comprising:

comparing said activity level to a plurality of resource provisioning threshold values;

provisioning, responsive to said comparing, one or more resources of said computing system, said provisioning comprising one of;

reallocating a first resource of said one or more resources to said application, wherein said activity level exceeds a first threshold value of said plurality of resource provisioning threshold values, and

reallocating a second resource of said one or more resources from said application to another portion of said computing system, wherein said activity level is less than a second threshold value of said plurality of resource provisioning threshold values, said second threshold value being different from said first threshold value.

5. The method of claim 1, wherein a value of said plurality of values assigned to said calendar type factor comprises:

a time of a day associated with said activity level and adjusted by a time zone in which a user of said plurality of users is located.

4. A method of predicting a user activity level associated with an application executing on a computing system in a multi-user computing environment, comprising:

defining a user activity metric (UAM) as a variable, said UAM being assigned a plurality of activity levels, wherein each activity level of said plurality of activity levels is a measured number of transactions performed by a plurality of users utilizing said application during a time period of a plurality of time periods, or a measured number of said users utilizing said application during said time period;

defining a plurality of factors associated with said UAM;

assigning said plurality of activity levels to said UAM;

assigning a plurality of values to a factor of said plurality of factors, said plurality of values associated with said plurality of activity levels in a one-to-one correspondence, and with said plurality of time periods in a one-to-one correspondence;

calculating at least one coefficient of correlation between at least two values of said plurality of values and at least two activity levels of said plurality of activity levels;

determining a maximum coefficient of correlation of said at least one coefficient of correlation that is less than a pre-defined threshold value;

excluding, in response to said determining, said factor from said plurality of factors to facilitate forming a subset of said plurality of factors, said subset including one or more factors, wherein an absolute value of a coefficient of correlation between any factor of said one or more factors and said UAM is greater than or equal to said pre-defined threshold value;

generating a regression model to predict an activity level, said regression model based on said plurality of activity levels and said subset,

wherein said defining said plurality of factors comprises defining said factor as a calendar type factor for which each value of said plurality of values represents an amount of time elapsed from a specified event,

wherein said at least one coefficient of correlation comprises a single coefficient of correlation, and

wherein said maximum coefficient of correlation is an absolute value of said single coefficient of correlation;

determining a sample mean, a standard deviation, and a standard error associated with said plurality of activity levels, said plurality of activity levels being historical data;

modifying a value of said plurality of values to a pre-defined base value, wherein said value is associated with an activity level of said plurality of activity level exceeding said sample mean by more than said standard error multiplied by a pre-defined number; and

adjusting other values of said plurality of values to a set of two or more adjusted values, wherein an adjusted value of said two or more adjusted values represents a distance to said base value,

wherein said calculating utilizes said base value and said set of two or more adjusted values to calculate said single coefficient of correlation, wherein a first absolute value of said single coefficient of correlation is greater than a second absolute value of another coefficient of correlation between said plurality of values and said plurality of activity levels.

27. A computing system comprising a processor coupled to a computer-readable memory unit, said memory unit comprising a software application, said software application comprising instructions that when executed by said processor implement the method of claim 4.

28. A computer program product, comprising a computer-usable storage medium having a computer-readable program code embodied therein, said computer-readable program code comprising an algorithm adapted to implement the method of claim 4.

6. A method of predicting a user activity level associated with an application executing on a computing system in a multi-user computing environment, said method comprising:

defining a user activity metric (UAM) as a variable, said UAM being assigned a plurality of activity levels, wherein each activity level of said plurality of activity levels is a measured number of transactions performed by a plurality of users utilizing said application during a time period of a plurality of time periods, or a measured number of said plurality of users utilizing said application during said time period;

defining a plurality of factors associated with said plurality of activity levels, said plurality of factors including a first subset of said plurality of factors, said first subset including at least one factor, wherein each factor of said first subset is a measurable variable on which said UAM depends, wherein said measurable variable is not a calendar type factor capable of being assigned amounts of time elapsed from a specified event;

assigning said plurality of activity levels to said UAM, said assigning said plurality of activity levels including assigning a plurality of sets of at least two activity levels, wherein each set of said at least two activity levels depends upon a factor of said plurality of factors;

assigning a plurality of values to each factor of said plurality of factors, said plurality of factors associated with said plurality of activity levels in a one-to-one correspondence, and with said plurality of time periods in a one-to-one correspondence;

generating a first matrix of X_ij elements, wherein x_ij is a value of an i-th factor of said first subset of said plurality of factors, said value of said i-th factor measured at an end of a j-th time period of said plurality of time periods;

generating a second matrix including said first matrix, wherein said second matrix is a three dimensional matrix including y_ijk elements, wherein y_ijk is an (i,j,k)-th activity level assigned to said UAM, and included in a k-th set of said plurality of sets of at least two activity levels, said (i,j,k)-th activity level associated with said x_ij and measured at an end of a (j+k)-th time period of said plurality of time periods;

calculating a plurality of coefficients of correlation, wherein each coefficient of correlation of said plurality of coefficients of correlation is r_ik and wherein said r_ik is an (i,k)-th coefficient of correlation between said i-th factor of said first subset of said plurality of factors and said k-th set of said plurality of sets of at least two activity levels;

determining that each absolute value of r_jk coefficients of correlation associated with said i-th factor is less than a pre-defined threshold value;

excluding, in response to said determining, said i-th factor from said plurality of factors, said excluding including forming a subset of said plurality of factors, said subset of said plurality of factors including one or more factors, wherein an absolute value of a coefficient of correlation between a set of values assigned to any factor of said one or more factors and said plurality of activity levels is greater than or equal to said pre-defined threshold value; and

generating a regression model to predict an activity level, said regression model based on said plurality of activity levels and said subset of said plurality of factors.

7. A method of predicting a user activity level associated with an application executing on a computing system in a multi-user computing environment, comprising:

defining a user activity metric (UAM) as a variable, said UAM being assigned a plurality of activity levels, wherein each activity level of said plurality of activity levels is a measured number of transactions performed by a plurality of users utilizing said application during a time period of a plurality of time periods, or a measured number of said users utilizing said application during said time period;

defining a plurality of factors associated with said UAM;

assigning a plurality of activity levels to said UAM;

assigning a plurality of values to a factor of said plurality of factors, said plurality of values associated with said plurality of activity levels in a one-to-one correspondence, and with said plurality of time periods in a one-to-one correspondence;

calculating at least one coefficient of correlation between at least two values of said plurality of values and at least two activity levels of said plurality of activity levels;

determining a maximum coefficient of correlation of said at least one coefficient of correlation that is less than a pre-defined threshold value;

excluding, in response to said determining, said factor from said plurality of factors to facilitate forming a subset of said plurality of factors, said subset including one or more factors, wherein an absolute value of a coefficient of correlation between any factor of said one or more factors and said UAM is greater than or equal to said pre-defined threshold value; and

generating a regression model to predict an activity level, said regression model based on said plurality of activity levels and said subset,

wherein said defining said plurality of factors comprises;

defining a first subset of said plurality of factors, said first subset including at least one factor, wherein said factor is included in said first subset, wherein each factor of said first subset is a measurable variable on which said UAM depends, wherein said measurable variable is not a calendar type factor for which each value of said plurality of values represents an amount of time elapsed from a specified event, and wherein said method further comprises;

assigning said plurality of activity levels to said UAM, said plurality of activity levels included in a plurality of sets of at least two activity levels,

wherein each set of said at least two activity levels depends upon a factor of said plurality of factors, and is included in said plurality of activity levels, and

wherein said plurality of activity levels is included in one set of said plurality of sets;

generating a first matrix of x_ij elements, wherein x_ij is a value of an i-th factor of said first subset measured at an end of a j-th time period of said plurality of time periods; and

generating a second matrix including said first matrix, wherein said second matrix is a three dimensional matrix including y_ijk elements,

wherein y_ijk is an (i,j,k)-th activity level assigned to said UAM, and included in a k-th set of said plurality of sets of at least two activity levels, said (i,j,k)-th activity level associated with said x_ij and measured at an end of a (j+k)-th time period of said plurality of time periods, and

wherein said second matrix includes elements r_ik,

wherein r_ik is an (i,k)-th coefficient of correlation between said i-th factor of said first subset and said k-th set,

wherein said maximum coefficient of correlation is an absolute value of an element (r) included in a set of said elements r_ik,

wherein said absolute value of said r is greater than or equal to an absolute value of any other element of said set of said elements r_ik, said r being associated with said factor and with a set of said plurality of sets of at least two activity levels, and

wherein said maximum coefficient of correlation is associated with a time step K, wherein each activity level of said set of said plurality of sets is measured K units of time after an end of a period of time of said plurality of periods of time.

8. The method of claim 7, further comprising:

assigning one or more factor values to said one or more factors of said subset in a one-to-one correspondence, wherein each factor value of said one or more factor values is measured said K units of time before a start of a period of time during which said activity level is to occur; and

calculating said activity level via said regression model, wherein said regression model is a multiple linear regression model utilizing said one or more factor values as input to predict said activity level during said period of time during which said activity level is to occur.

29. A computing system comprising a processor coupled to a computer-readable memory unit, said memory unit comprising a software application, said software application comprising instructions that when executed by said processor implement the method of claim 7.

30. A computer program product, comprising a computer-usable storage medium having a computer-readable program code embodied therein, said computer-readable program code comprising an algorithm adapted to implement the method of claim 7.

9. A system for predicting a user activity level associated with an application executing on a computing system in a multi-user computing environment, comprising:

means for defining a user activity metric (UAM) as a variable, said UAM being assigned a plurality of activity levels, wherein each activity level of said plurality of activity levels is a measured number of transactions performed by a plurality of users utilizing said application during a time period of a plurality of time periods, or a measured number of said users utilizing said application during said time period;

means for defining a plurality of factors associated with said plurality of activity levels, said plurality of factors including a calendar type factor, said calendar type factor capable of being assigned a plurality of values of a cyclic time scale, wherein each value of said plurality of values represents an amount of time elapsed from a specified event;

means for assigning said plurality of activity levels to said UAM;

means for assigning said plurality of values to said calendar type factor of said plurality of factors, said plurality of values associated with said plurality of activity levels in a one-to-one correspondence, and with said plurality of time periods in a one-to-one correspondence;

means for adjusting, subsequent to said assigning said plurality of values to said calendar type factor, said plurality of values to a plurality of adjusted values, said plurality of adjusted values including a pre-defined base value and one or more other adjusted values, wherein an adjusted value of said one or more other adjusted values represents a distance to said pre-defined base value, and wherein said adjusting includes facilitating a representation of a linear dependency between said plurality of adjusted values and said plurality of activity levels;

means for calculating a plurality of coefficients of correlation, said calculating said plurality of coefficients of correlation including calculating a first coefficient of correlation between said plurality of adjusted values and said plurality of activity levels;

means for determining that an absolute value of said first coefficient of correlation is less than a pre-defined threshold value;

means for excluding, in response to said determining, said calendar type factor from said plurality of factors, said excluding including forming a subset of said plurality of factors, said subset including one or more factors, wherein an absolute value of a second coefficient of correlation between a set of values assigned to any factor of said one or more factors and said plurality of activity levels is greater than or equal to said pre-defined threshold value; and

means for generating a regression model to predict an activity level, said regression model based on said plurality of activity levels and said subset.

10. The system of claim 9, further comprising:

means for collecting, in real time, one or more sets of one or more values, said one or more sets of one or more values associated with said one or more factors included in said subset in a one-to-one correspondence; and

means for calculating said activity level via said regression model utilizing said one or more sets of one or more values, wherein said activity level is a number of transactions to be performed by said plurality of users utilizing said application during a future time period or a number of said users to be utilizing said application during said future time period.

11. The system of claim 10, further comprising:

means for comparing said activity level to a plurality of resource provisioning threshold values;

means for provisioning, responsive to said comparing, one or more resources of said computing system, said means for provisioning comprising one of;

means for reallocating a first resource of said one or more resources to said application, wherein said activity level exceeds a first threshold value of said plurality of resource provisioning threshold values, and

means for reallocating a second resource of said one or more resources from said application to another portion of said computing system, wherein said activity level is less than a second threshold value of said plurality of resource provisioning threshold values, said second threshold value being different from said first threshold value.

12. The system of claim 9, wherein a value of said plurality of values assigned to said calendar type factor comprises:

a time of a day associated with said activity level and adjusted by a time zone in which a user of said plurality of users is located.

13. A system of predicting a user activity level associated with an application executing on a computing system in a multi-user computing environment, said system comprising:

means for defining a user activity metric (UAM) as a variable, said UAM being assigned a plurality of activity levels, wherein each activity level of said plurality of activity levels is a measured number of transactions performed by a plurality of users utilizing said application during a time period of a plurality of time periods, or a measured number of said plurality of users utilizing said application during said time period;

means for defining a plurality of factors associated with said plurality of activity levels, said plurality of factors including a first subset of said plurality of factors, said first subset including at least one factor, wherein each factor of said first subset is a measurable variable on which said UAM depends, wherein said measurable variable is not a calendar type factor capable of being assigned amounts of time elapsed from a specified event;

means for assigning said plurality of activity levels to said UAM, said means for assigning said plurality of activity levels including means for assigning a plurality of sets of at least two activity levels, wherein each set of said at least two activity levels depends upon a factor of said plurality of factors;

means for assigning a plurality of values to each factor of said plurality of factors, said plurality of factors associated with said plurality of activity levels in a one-to-one correspondence, and with said plurality of time periods in a one-to-one correspondence;

means for generating a first matrix of x_ij elements, wherein x_ij is a value of an i-th factor of said first subset of said plurality of factors, said value of said i-th factor measured at an end of a j-th time period of said plurality of time periods;

means for generating a second matrix including said first matrix, wherein said second matrix is a three dimensional matrix including y_ijk elements, wherein y_ijk is an (i,j,k)-th activity level assigned to said UAM, and included in a k-th set of said plurality of sets of at least two activity levels, said (i,j,k)-th activity level associated with said x_ij and measured at an end of a (j+k)-th time period of said plurality of time periods;

means for calculating a plurality of coefficients of correlation, wherein each coefficient of correlation of said plurality of coefficients of correlation is r_ik and wherein said r_ik is an (i,k)-th coefficient of correlation between said i-th factor of said first subset of said plurality of factors and said k-th set of said plurality of sets of at least two activity levels;

means for determining that each absolute value of r_ik coefficients of correlation associated with said i-th factor is less than a pre-defined threshold value;

means for excluding, in response to said determining, said i-th factor from said plurality of factors, said means for excluding including means for forming a subset of said plurality of factors, said subset of said plurality of factors including one or more factors, wherein an absolute value of a coefficient of correlation between a set of values assigned to any factor of said one or more factors and said plurality of activity levels is greater than or equal to said pre-defined threshold value; and

means for generating a regression model to predict an activity level, said regression model based on said plurality of activity levels and said subset of said plurality of factors.

14. A computer program product comprising a computer-usable storage medium including computer-usable program code for predicting a user activity level associated with an application executing on a computing system in a multi-user computing environment, said computer program product comprising:

computer-usable code for defining a user activity metric (UAM) as a variable, said UAM being assigned a plurality of activity levels, wherein each activity level of said plurality of activity levels is a measured number of transactions performed by a plurality of users utilizing said application during a time period of a plurality of time periods, or a measured number of said users utilizing said application during said time period;

computer-usable code for defining a plurality of factors associated with said plurality of activity levels, said plurality of factors including a calendar type factor, said calendar type factor capable of being assigned a plurality of values of a cyclic time scale, wherein each value of said plurality of values represents an amount of time elapsed from a specified event;

computer-usable code for assigning said plurality of activity levels to said UAM;

computer-usable code for assigning said plurality of values to said calendar type factor of said plurality of factors, said plurality of values associated with said plurality of activity levels in a one-to-one correspondence, and with said plurality of time periods in a one-to-one correspondence;

computer-usable code for adjusting, subsequent to said assigning said plurality of values to said calendar type factor, said plurality of values to a plurality of adjusted values, said plurality of adjusted values including a pre-defined base value and one or more other adjusted values, wherein an adjusted value of said one or more other adjusted values represents a distance to said pre-defined base value, and wherein said adjusting includes facilitating a representation of a linear dependency between said plurality of adjusted values and said plurality of activity levels;

computer-usable code for calculating a plurality of coefficients of correlation, said calculating said plurality of coefficients of correlation including calculating a first coefficient of correlation between said plurality of adjusted values and said plurality of activity levels;

computer-usable code for determining that an absolute value of said first coefficient of correlation is less than a pre-defined threshold value;

computer-usable code for excluding, in response to said determining, said calendar type factor from said plurality of factors, said excluding including forming a subset of said plurality of factors, said subset including one or more factors, wherein an absolute value of a second coefficient of correlation between a set of values assigned to any factor of said one or more factors and said plurality of activity levels is greater than or equal to said pre-defined threshold value; and

computer-usable code for generating a regression model to predict an activity level, said regression model based on said plurality of active levels and said subset.

15. The program product of claim 14, further comprising:

computer-usable code for collecting, in real time, one or more sets of one or more values, said one or more sets of one or more values associated with said one or more factors included in said subset in a one-to-one correspondence; and

computer-usable code for calculating said activity level via said regression model utilizing said one or more sets of one or more values, wherein said activity level is a number of transactions to be performed by said plurality of users utilizing said application during a future time period or a number of said users to be utilizing said application during said future time period.

16. The program product of claim 15, further comprising:

computer-usable code for comparing said activity level to a plurality of resource provisioning threshold values;

computer-usable code for provisioning, responsive to said comparing, one or more resources of said computing system, said computer-usable code for provisioning comprising one of;

computer-usable code for reallocating a first resource of said one or more resources to said application, wherein said activity level exceeds a first threshold value of said plurality of resource provisioning threshold values, and

computer-usable code for reallocating a second resource of said one or more resources from said application to another portion of said computing system, wherein said activity level is less than a second threshold value of said plurality of resource provisioning threshold values, said second threshold value being different from said first threshold value.

17. The program product of claim 14, wherein a value of said plurality of values assigned to said calendar type factor comprises:

a time of a day associated with said activity level and adjusted by a time zone in which a user of said plurality of users is located.

18. A computer program product comprising a computer-usable storage medium including computer-usable program code for predicting a user activity level associated with an application executing on a computing system in a multi-user computing environment, said computer program product comprising:

computer-usable code for defining a user activity metric (UAM) as a variable, said UAM being assigned a plurality of activity levels, wherein each activity level of said plurality of activity levels is a measured number of transactions performed by a plurality of users utilizing said application during a time period of a plurality of time periods, or a measured number of said plurality of users utilizing said application during said time period;

computer-usable code for defining a pulrality of factors associated with said plurality of activity levels, said plurality of factors including a first subset of said plurality of factors, said first subset including at least one factor, wherein each factor of said first subset is a measurable variable on which said UAM depends, wherein said measurable variable is not a calendar type factor capable of being assigned amounts of time elapsed from a specified event;

computer-usable code for assigning said plurality of activity levels to said UAM, said computer-usable code for assigning said plurality of activity levels including computer-usable code for assigning a plurality of sets of at least two activity levels, wherein each set of said at least two activity levels depends upon a factor of said plurality of factors;

computer-usable code for assigning a plurality of values to each factor of said plurality of factors, said plurality of factors associated with said plurality of activity levels in a one-to-one correspondence, and with said plurality of time periods in a one-to-one correspondence;

computer-usable code for generating a first matrix of X_ij elements, wherein X_ij is a value of an i-th factor of said first subset of said plurality of factors, said value of said i-th factor measured at an end of a j-th time period of said plurality of time periods;

computer-usable code for generating a second matrix including said first matrix, wherein said second matrix is a three dimensional matrix including y_ijk elements, wherein y_ijk is an (i,j,k)-th activity level assigned to said UAM, and included in a k-th set of said plurality of sets of at least two activity levels, said (i,j,k)-th activity level associated with said x_ij and measured at an end of a (j+k)-th time period of said plurality of time periods;

computer-usable code for calculating a plurality of coefficients of correlation, wherein each coefficient of correlation of said plurality of coefficients of correlation is r_ik and wherein said r_ik is an (i,k)-th coefficient of correlation between said i-th factor of said first subset of said plurality of factors and said k-th set of said plurality of sets of at least two activity levels;

computer-usable code for determining that each absolute value of r_ik coefficients of correlation associated with said i-th factor is less than a pre-defined threshold value;

computer-usable code for excluding, in response to said determining, said i-th factor from said plurality of factors, said computer-usable code for excluding including computer-usable code for forming a subset of said plurality of factors, said subset of said plurality of factors including one or more factors, wherein an absolute value of a coefficient of correlation between a set of values assigned to any factor of said one or more factors and said plurality of activity levels is greater than or equal to said pre-defined threshold value; and

computer-usable code for generating a regression model to predict an activity level, said regression model based on said plurality of activity levels and said subset of said plurality of factors.

19. A method for deploying computing infrastructure, comprising integrating computer-readable code into a computing system, wherein the code in combination with the computing system is capable of performing a process of predicting a user activity level associated with an application executing on a computing system in a multi-user computing environment, said process comprising:

defining a user activity metric (UAM) as a variable, said UAM being assigned a plurality of activity levels, wherein each activity level of said plurality of activity levels is a measured number of transactions performed by a plurality of users utilizing said application during a time period of a plurality of time periods, or a measured number of said users utilizing said application during said time period;

defining a plurality of factors associated with said plurality of activity levels, said plurality of factors including a calendar type factor, said calendar type factor capable of being assigned a plurality of values of a cyclic time scale, wherein each value of said plurality of values represents an amount of time elapsed from a specified event;

assigning said plurality of activity levels to said UAM;

assigning said plurality of values to said calendar type factor of said plurality of factors, said plurality of values associated with said plurality of activity levels in a one-to-one correspondence, and with said plurality of time periods in a one-to-one correspondence;

adjusting, subsequent to said assigning said plurality of values to said calendar type factor, said plurality of values to a plurality of adjusted values, said plurality of adjusted values including a pre-defined base value and one or more other adjusted values, wherein an adjusted value of said one or more other adjusted values represents a distance to said pre-defined base value, and wherein said adjusting includes facilitating a representation of a linear dependency between said plurality of adjusted values and said plurality of activity levels;

calculating a plurality of coefficients of correlation, said calculating said plurality of coefficients of correlation including calculating a first coefficient of correlation between said plurality of adjusted values and said plurality of activity levels;

determining that an absolute value of said first coefficient of correlation is less than a pre-defined threshold value;

excluding, in response to said determining, said calendar type factor from said plurality of factors, said excluding including forming a subset of said plurality of factors, said subset including one or more factors, wherein an absolute value of a second coefficient of correlation between a set of values assigned to any factor of said one or more factors and said plurality of activity levels is greater than or equal to said pre-defined threshold value; and

generating a regression model to predict an activity level, said regression model based on said plurality of activity levels and said subset.

20. The method of claim 19, said process further comprising:

collecting, in real time, one or more sets of one or more values, said one or more sets of one or more values associated with said one or more factors included in said subset in a one-to-one correspondence; and

calculating said activity level via said regression model utilizing said one or more sets of one or more values, wherein said activity level is a number of transactions to be performed by said plurality of users utilizing said application during a future time period or a number of said users to be utilizing said application during said future time period.

21. The method of claim 20, said process further comprising:

comparing said activity level to a plurality of resource provisioning threshold values;

provisioning, responsive to said comparing, one or more resources of said computing system, said provisioning comprising one of;

reallocating a first resource of said one or more resources to said application, wherein said activity level exceeds a first threshold value of said plurality of resource provisioning threshold values, and

reallocating a second resource of said one or more resources from said application to another portion of said computing system, wherein said activity level is less than a second threshold value of said plurality of resource provisioning threshold values, said second threshold value being different from said first threshold value.

23. The method of claim 19, wherein a value of said plurality of values assigned to said calendar type factor comprises:

a time of a day associated with said activity level and adjusted by a time zone in which a user of said plurality of users is located.

22. A method for deploying computing infrastructure, comprising integrating computer-readable code into a computing system, wherein the code in combination with the computing system is capable of performing a process of predicting a user activity level associated with an application executing on a computing system in a multi-user computing environment, said process comprising:

defining a user activity metric (UAM) as a variable, said UAM being assigned a plurality of activity levels, wherein each activity level of said plurality of activity levels is a measured number of transactions performed by a plurality of users utilizing said application during a time period of a plurality of time periods, or a measured number of said users utilizing said application during said time period;

defining a plurality of factors associated with said UAM;

assigning a plurality of activity levels to said UAM;

assigning a plurality of values to a factor of said plurality of factors, said plurality of values associated with said plurality of activity levels in a one-to-one correspondence, and with said plurality of time periods in a one-to-one correspondence;

calculating at least one coefficient of correlation between at least two values of said plurality of values and at least two activity levels of said plurality of activity levels;

determining a maximum coefficient of correlation of said at least one coefficient of correlation that is less than a pre-defined threshold value;

excluding, in response to said determining, said factor from said plurality of factors to facilitate forming a subset of said plurality of factors, said subset including one or more factors, wherein an absolute value of a coefficient of correlation between any factor of said one or more factors and said UAM is greater than or equal to said pre-defined threshold value; and

generating a regression model to predict an activity level, said regression model based on said plurality of activity levels and said subset,

wherein said defining said plurality of factors comprises defining said factor as a calendar type factor for which each value of said plurality of values represents an amount of time elapsed from a specified event,

wherein said at least one coefficient of correlation comprises a single coefficient of correlation, and

wherein said maximum coefficient of correlation is an absolute value of said single coefficient of correlation;

determining a sample mean, a standard deviation, and a standard error associated with said plurality of activity levels, said plurality of activity levels being historical data;

modifying a value of said plurality of values to a pre-defined base value, wherein said value is associated with an activity level of said plurality of activity levels exceeding said sample mean by more than said standard error multiplied by a pre-defined number; and

adjusting other values of said plurality of values to a set of two or more adjusted values, wherein an adjusted value of said two or more adjusted values represents a distance to said base value,

wherein said calculating utilizes said base value and said set of two or more adjusted values to calculate said single coefficient of correlation, wherein a first absolute value of said single coefficient of correlation is greater than a second absolute value of another coefficient of correlation between said plurality of values and said plurality of activity levels.

24. A method of predicting a user activity level associated with an application executing on a computing system in a multi-user computing environment, said method comprising:

defining a user activity metric (UAM) as a variable, said UAM being assigned a plurality of activity levels, wherein each activity level of said plurality of activity levels is a measured number of transactions performed by a plurality of users utilizing said application during a time period of a plurality of time periods, or a measured number of said plurality of users utilizing said application during said time period;

defining a plurality of factors associated with said plurality of activity levels, said plurality of factors including a first subset of said plurality of factors, said first subset including at least one factor, wherein each factor of said first subset is a measurable variable on which said UAM depends, wherein said measurable variable is not a calendar type factor capable of being assigned amounts of time elapsed from a specified event;

assigning said plurality of activity levels to said UAM, said assigning said plurality of activity levels including assigning a plurality of sets of at least two activity levels, wherein each set of said at least two activity levels depends upon a factor of said plurality of factors;

assigning a plurality of values to each factor of said plurality of factors, said plurality of factors associated with said plurality of activity levels in a one-to-one correspondence, and with said plurality of time periods in a one-to-one correspondence;

generating a first matrix of x_ij elements, wherein x_ij is a value of an i-th factor of said first subset of said plurality of factors, said value of said i-th factor measured at an end of a j-th time period of said plurality of time periods;

generating a second matrix including said first matrix, wherein said second matrix is a three dimensional matrix including y_ijk elements, wherein y_ijk is an (i,j,k)-th activity level assigned to said UAM, and included in a k-th set of said plurality of sets of at least two activity levels, said (i,j,k)-th activity level associated with said x_ij and measured at an end of a (j+k)-th time period of said plurality of time periods;

calculating a plurality of coefficients of correlation, wherein each coefficient of correlation of said plurality of coefficients of correlation is r_ik and wherein said r_ik is an (i,k)-th coefficient of correlation between said i-th factor of said first subset of said plurality of factors and said k-th set of said plurality of sets of at least two activity levels;

determining that each absolute value of r_ik coefficients of correlation associated with said i-th factor is less than a pre-defined threshold value;

excluding, in response to said determining, said i-th factor from said plurality of factors, said excluding including forming a subset of said plurality of factors, said subset of said plurality of factors including one or more factors, wherein an absolute value of a coefficient of correlation between a set of values assigned to any factor of said one or more factors and said plurality of activity levels is greater than or equal to said pre-defined threshold value; and

generating a regression model to predict an activity level, said regression model based on said plurality of activity levels and said subset of said plurality of factors.

25. A method for deploying computing infrastructure, comprising integrating computer-readable code into a computing system, wherein the code in combination with the computing system is capable of performing a process of predicting a user activity level associated with an application executing on a computing system in a multi-user computing environment, said process comprising:

defining a user activity metric (UAM) as a variable, said UAM being assigned a plurality of activity levels, wherein each activity level of said plurality of activity levels is a measured number of transactions performed by a plurality of users utilizing said application during a time period of a plurality of time periods, or a measured number of said users utilizing said application during said time period;

defining a plurality of factors associated with said UAM;

assigning a plurality of activity levels to said UAM;

assigning a plurality of values to a factor of said plurality of factors, said plurality of values associated with said plurality of activity levels in a one-to-one correspondence, and with said plurality of time periods in a one-to-one correspondence;

calculating at least one coefficient of correlation between at least two values of said plurality of values and at least two activity levels of said plurality of activity levels;

determining a maximum coefficient of correlation of said at least one coefficient of correlation that is less than a pre-defined threshold value;

excluding, in response to said determining, said factor from said plurality of factors to facilitate forming a subset of said plurality of factors, said subset including one or more factors, wherein an absolute value of a coefficient of correlation between any factor of said one or more factors and said UAM is greater than or equal to said pre-defined threshold value; and

generating a regression model to predict an activity level, said regression model based on said plurality of activity levels and said subset,

wherein said defining said plurality of factors comprises;

defining a first subset of said plurality of factors, said first subset including at least one factor, wherein said factor is included in said first subset, wherein each factor of said first subset is a measurable variable on which said UAM depends, wherein said measurable variable is not a calendar type factor for which each value of said plurality of values represents an amount of time elapsed from a specified event, and wherein said process further comprises;

assigning said plurality of activity levels to said UAM, said plurality of activity levels included in a plurality of sets of at least two activity levels,

wherein each set of said at least two activity levels depends upon a factor of said plurality of factors, and is included in said plurality of activity levels, and

wherein said plurality of activity levels is included in one set of said plurality of sets;

generating a first matrix of x_ij elements, wherein x_ij is a value of an i-th factor of said first subset measured at an end of a j-th time period of said plurality of time periods; and

generating a second matrix including said first matrix, wherein said second matrix is a three dimensional matrix including y_ijk elements,

wherein y_ijk is an (i,j,k)-th activity level assigned to said UAM, and included in a k-th set of said plurality of sets of at least two activity levels, said (i,j,k)-th activity level associated with said x_ij and measured at an end of a (j+k)-th time period of said plurality of time periods, and

wherein said second matrix includes elements r_ik,

wherein r_ik is an (i,k)-th coefficient of correlation between said i-th factor of said first subset and said k-th set,

wherein said maximum coefficient of correlation is an absolute value of an element (r) included in a set of said elements r_ik,

wherein said absolute value of said r is greater than or equal to an absolute value of any other element of said set of said elements r_ik, said r being associated with said factor and with a set of said plurality of sets of at least two activity levels, and

wherein said maximum coefficient of correlation is associated with a time step K, wherein each activity level of said set of said plurality of sets is measured K units of time after an end of a period of time of said plurality of periods of time.

26. The method of claim 25, said process further comprising:

assigning one or more factor values to said one or more factors of said subset in a one-to-one correspondence, wherein each factor value of said one or more factor values is measured said K units of time before a start of a period of time during which said activity level is to occur; and

calculating said activity level via said regression model, wherein said regression model is a multiple linear regression model utilizing said one or more factor values as input to predict said activity level during said period of time during which said activity level is to occur.