Data processing: financial – business practice – management – or co – Automated electrical financial or business practice or... – Discount or incentive
Reexamination Certificate
1999-05-06
2002-04-23
Hafiz, Tario R. (Department: 2163)
Data processing: financial, business practice, management, or co
Automated electrical financial or business practice or...
Discount or incentive
C707S793000, C707S793000
Reexamination Certificate
active
06377934
ABSTRACT:
The following commonly-owned co-pending applications, including this one, are being filed concurrently and the others are hereby incorporated by reference in their entirety for all purposes:
1. U.S. patent application Ser. No. 09/306,677, Li-Wen Chen and Juan Oritz entitled, “METHOD FOR PROVIDING A REVERSE STAR SCHEMA DATA MODEL”
2. U.S. patent application Ser. No. 09/306,650, Li-Wen Chen entitled, “APPARATUS FOR PROVIDING A REVERSE STAR SCHEMA DATA MODEL”
3. U.S. patent application Ser. No. 09/306,693, Li-Wen Chen entitled, “SYSTEM FOR PROVIDING A REVERSE STAR SCHEMA DATA MODEL”
BACKGROUND OF THE INVENTION
The present invention relates generally to computer database systems, and specifically to methods for organizing information from one or more systems in a data warehousing environment.
Few could foresee the rapid development of computer technology just a few years ago. Computers now have a place in our homes, our offices, our schools and even the our briefcases and satchels. As computer automation continues to impact an ever increasing portion of our daily lives, governments, businesses and individuals have turned to database technology to help them manage the “information explosion” and the exponential proliferation of information that must be sorted, assimilated and managed on a continuing basis. One area of importance to the database design field is data model selection for database applications.
A data model represents the structure or organization of data stored in the database. It enables the use of data in certain forms and may limit the data being used in other forms. Different applications usually require different data models. Many different data models can exist, and they usually differ markedly from one another. Typically, database applications are customized to a particular data model of a particular database. Different database vendors base their products on different data models, adding to the confusion. Usually, these applications must be re-implemented for different databases, even though the functioning of the application remains the same.
Presently, database developers have turned to data warehousing technology to resolve often conflicting data management requirements. Traditional data warehousing approaches focus on decision support applications, which emphasize summarized information. While perceived advantages exist, an inherent disadvantage to these systems is that transaction details about the customer's identity are lost. Traditional approaches exhibit shortcomings when applied to applications such as customer data analysis. Customer data analysis is a decision support analysis that correlates data to customers' activities, events, transactions, status and the like. Summarized information usually loses the detail level of information about customer identity, limiting the usefulness of traditional data warehousing approaches in these types of applications.
What is needed is a method for providing a database that can be customized to fit individual user needs, yet also able to support data analysis applications.
SUMMARY OF THE INVENTION
According to the invention, techniques for organizing information from a variety of sources, including legacy systems, in a data warehousing environment are provided. In an exemplary embodiment, the invention provides a method for analyzing data from one or more data sources of an enterprise. The method provides a meta-model based technique for modeling the enterprise data. The enterprise is typically a business activity, but can also be other loci of human activity. Embodiments according to the invention can translate data from a variety of sources to particular database schema in order to provide organization to a data warehousing environment.
The method includes a variety of steps, such as providing a model for an enterprise. The model can be a meta model that describes at a high level the information used by the enterprise. Meta models can describe relationships between groups of entities in a data model. Entities in a data model can comprise particular data types, and the like. The enterprise can be a business activity, and/or the like. A step of forming a data organization from the model is also part of the method. The data organization can include data schema and the like. Data schema define aspects of the database, such as attributes, domains and parameters, and the like, to a database management system (DBMS). The method also includes creating one or more databases for containing the data. Translating data from one or more sources to the data organization is also part of the method. A step of incorporating data into the database is part of the method. The method can also include a step of performing analysis on the data in the database. Accordingly, the combination of these steps can provide an environment for analyzing information about customers, business processes and the like.
In another aspect of the present invention, techniques for data warehousing are provided. In a particular embodiment, the invention provides a method for creating a database for organizing information from one or more sources. Embodiments can organize the data in the database according to a data schema, such as a reverse star schema. A reverse star schema model comprises an identity element (e.g., core components, and the like) and one or more entities that describe classifications of data (e.g. customer classification components, and the like), which can have one or more relationships with the identity element. In an exemplary embodiment, customer classification components provide different ways to categorize customers or different business views of the customers, for example. For example, customers can be categorized by geographic region, demographics and the like. The method comprises a variety of steps including selecting a data model template from pre-defined ones based upon one or more business requirements. The method also includes a step of selecting customer entities from pre-defined ones that fit the application based on their business processes and operations. The entities can be selected from a focal group, for example. In a particular embodiment, focal groups can describe information about customer characteristics, profiles, business related classifications, customers' roles, definitions and the like in a variety of business functional areas.
A step of defining entities for transactions and/or events and their attributes to form a customized group of customer activity components that are relevant to a particular application is also part of the method. The events can be arranged into customer activity components. These components can be organized into one or more customized groups that correspond to various operations and/or transactions. As event transactions can be scattered over time, these components comprise a set of business measures and attributes. These events can be independent as well as dependent from one another. A particular sequence of events can be used to describe different stages of customer activity. For example, in a particular time period, a customer may go through a sequence of events such as: subscription>billing>payment>promotion>price plan change>service call>cancellation. Each event can involve a plurality of different business processes or operations that reflect a lifecycle of a customer. The method also includes a step of defining one or more customer event types in the customer activity components. A step of selecting data tables and attributes that will comprise the source of a set of data tables having a particular data schema and attributes is also included in the method.
The method can also include steps of determining one or more attributes based on data types in source tables and primary and foreign keys. A step of creating one or more databases from the schema is also part of the method. The database can be a customer data warehouse, and the like. Creating data movement mapping rules can also be part of the method. Such mapping rules can provide informa
Chen Li-Wen
Ortiz Juan J.
Hafiz Tario R.
MetaEdge Corporation
Robertson Dave
Squire Sanders & Dempsey LLP
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