System and method for applying predictive metric analysis for a business monitoring subsystem

• US 8,108,235 B2
• Filed: 03/21/2008
• Issued: 01/31/2012
• Est. Priority Date: 04/12/2006
• Status: Expired due to Fees

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Chowdhary, Pawan Raghunath, Kapoor, Shubir
• Primary Examiner(s): Boswell, Beth V
• Assistant Examiner(s): JACKSON, ERNEST ADEYEMI
• Application Number:
  US12/053,045
• Publication Number:
  US 20080167923A1
• Time in Patent Office: 1,411 Days
• Field of Search: 705/7, 705/10, 705/7.11, 705/7.29
• US Class Current: 705/7.11
• CPC Class Codes: G06F 2216/03 Data mining
• CPC Class Codes: G06Q 10/063 Operations research, analysis or management
• CPC Class Codes: G06Q 10/0637 Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
• CPC Class Codes: G06Q 10/06375 Prediction of business process outcome or impact based on a proposed change
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities

Abstract

Predictive metric analysis for business management is divided into build time, corresponding to the business owner view of the enterprise, and run time, corresponding to the information technology view of the enterprise. The build time consists of a predictive model and a monitoring model. These models go through transformation processes to the components of the run time. The run time components are a Metric Value Prediction Service (MVPS), which receives as input predictive model transformation and outputs predicted metric values, and a monitoring engine, which receives as input monitoring model transformation, the predicted metric values and business events from the business process. Various analytical engines can be plugged in to provide the predictive capabilities. Input is provided to a framework from various business systems which results in predicting the value of the metrics across the future time horizons.

Claims

1. A method for providing predictive modeling capabilities to allow intelligent business performance management of a business comprising the steps of:

generating, using a computer, a meta model consisting of business metrics relating to performance of the business with its customers organized as a hierarchy across operational, tactical and strategic levels of the business with each metric having the ability to be associated with time as a look-ahead dimension, wherein the computer-generated meta model is a model that describes a language in which models can be expressed, and wherein the computer-generated meta model includes;

an operational level of the business comprising transactional metrics organized in a hierarchy,

a tactical level of the business comprising tactical metrics having a relationship with corresponding lower level transactional metrics, and

a strategic level of the business comprising strategic metrics having a relationship with lower level tactical metrics;

associating time with each business metric as a look-ahead dimension;

transforming, using a computer, the meta model to service interface definition and related artifacts;

transforming, using a computer, the meta model into performance warehouse meta-data schemas without losing persistence of historical data related to the performance of the business with its customers; and

using a computer, receiving requests for metric predictions and servicing the requests in real time by using predictive analysis predicting individual usage of a certain service in a next time period or predicting individual customer ordering behavior for a future time.

2. The method of claim 1, wherein said meta model also comprises business metrics categorized within a predictive metric context to be used as input for predictive analysis.

3. The method of claim 2, wherein said meta model also comprises trigger conditions describing how and when said predictive analysis will be triggered.

10. The method of claim 1, including generating a meta-model in which the operational level is a low level, the tactical level is a middle level and the strategic level is a high level.

11. The method of claim 10, including generating a meta-model in which the operational level comprises On Time Delivery, Shipment Dispatch and Shipment Arrive.

12. The method of claim 10, including generating a meta-model in which the tactical level comprises Efficiency.

13. The method of claim 10, including generating a meta-model in which the strategic level comprises Customer Satisfaction.

14. The method of claim 1, including performing a look-ahead by computing a function of time based on an initial time, a time step, and a final time.

15. The method of claim 1, including receiving insurance claims data as input and predicting individual usage of healthcare services over a next insured period.

4. A system for providing predictive modeling capabilities to allow intelligent business performance management of a business comprising:

a computer generating a meta model consisting of business metrics relating to performance of the business with its customers organized as a hierarchy across operational, tactical and strategic levels of the business with each metric having the ability to be associated with time as a look-ahead dimension, wherein the meta model is a model that describes a language in which models can be expressed and wherein the computer-generated meta model includes;

an operational level of the business comprising transactional metrics organized in a hierarchy,

a tactical level of the business comprising tactical metrics having a relationship with corresponding lower level transactional metrics, and

a strategic level of the business comprising strategic metrics having a relationship with lower level tactical metrics;

a computer transforming the meta model to service interface definition and related artifacts;

a computer transforming the meta model into performance warehouse meta-data schemas without losing persistence of historical data relating to performance of the business with its customers; and

a computer receiving requests for metric predictions and using analytical techniques to service the requests in real time by using predictive analysis predict individual usage of a certain service in a next time period or predict individual customer ordering behavior for a future time.

5. The system of claim 4, wherein said meta model also comprises business metrics categorized within a predictive metric context to be used as input for predictive analysis.

6. The method of claim 5, wherein said meta model also comprises trigger conditions describing how and when said predictive analysis will be triggered.

7. A non-transitory computer-readable medium for providing predictive modeling capabilities to allow intelligent business performance management of a business, on which is provided:

instructions for using a computer to generate a meta model consisting of business metrics relating to performance of the business with its customers organized as a hierarchy across operational, tactical and strategic levels of the business with each metric having the ability to be associated with time as a look-ahead dimension,

wherein the meta model is a model that describes a language in which models can be expressed and wherein the computer-generated meta model includes;

an operational level of the business comprising transactional metrics organized in a hierarchy, a tactical level of the business comprising tactical metrics having a relationship with corresponding lower level transactional metrics, and a strategic level of the business comprising strategic metrics having a relationship with lower level tactical metrics;

instructions for using a computer to transform the meta model to service interface definition and related artifacts;

instructions for using a computer to transform the meta model into performance warehouse meta-data schemas without losing persistence of historical data relating to performance of the business with its customers; and

instructions for using a computer to receive requests for metric predictions and to use analytical techniques to service the requests in real time by using predictive analysis predict individual usage of a certain service in a next time period or predict individual customer ordering behavior for a future time.

8. The non-transitory computer readable medium of claim 7, wherein said meta model also comprises business metrics categorized within a predictive metric context to be used as input for predictive analysis.

9. The non-transitory computer readable medium of claim 8, wherein said meta model also comprises trigger conditions describing how and when said predictive analysis will be triggered.

16. A method for providing predictive modeling capabilities to allow intelligent business performance management of a business comprising the steps of:

generating, using a computer, a meta model consisting of business metrics relating to performance of the business with its customers organized as a hierarchy across operational, tactical and strategic levels of the business with each metric having the ability to be associated with time as a look-ahead dimension,

wherein the computer-generated meta model is a model that describes a language in which models can be expressed, and wherein the computer-generated meta model includes;

an operational level of the business comprising transactional metrics organized in a hierarchy, a tactical level of the business comprising tactical metrics having a relationship with corresponding lower level transactional metrics, and a strategic level of the business comprising strategic metrics having a relationship with lower level tactical metrics;

associating time with each business metric as a look-ahead dimension;

transforming, using a computer, the meta model to service interface definition and related artifacts;

transforming, using a computer, the meta model into performance warehouse meta-data schemas without losing persistence of historical data related to the performance of the business with its customers.