JP2003524812A - How to generate a star schema data model - Google Patents

Abstract

According to the present invention, a technique is provided for organizing information from a system in a data warehouse environment. In the illustrated embodiment, the present invention provides a method for analyzing data from one or more data sources of enterprise data (204). The method provides a meta-model based technique for modeling enterprise data (204). This company is generally a business activity (203), but can also be a trajectory of human activities. Embodiments in accordance with the present invention translate data from various sources (203, 204) into specific database schemas to provide an organized way (200-204) to a data warehouse environment. Is possible.

Description

Detailed Description of the Invention

(Cross Reference to Related Application) The present application claims priority from the following US provisional patent applications, and the disclosure of the application has all purposes including all appendices and attached documents. No. 60 / 116,086, entitled “METHOD AND,” by Li-Wen Chen, filed January 15, 1999, incorporated herein by reference in its entirety.
APPARATUS FOR PERFORMING CUSTOMER DA
TA ANALYSIS OF A COMPUTER DATABASE U
SING REVERSE STAR SCHEMA DATA MODEL (
Method and Apparatus for Performing Customer Data Analysis of Computer Databases Using Star Schema Data Model) "(Attorney Docket Number: 1960)
8-000100 US).

The following co-pending co-pending applications are filed at the same time, including this application, and, except for this case, are incorporated by reference into this application for all purposes: Li-Wen Chen and Juan Or
"METHOD FOR PROVI" by tis (Juan Autits)
DING A REVERSE STAR SCHEMA DATA MODE
US Patent Application No. _________ (Attorney Docket No. 19608-000110U) entitled "L (Method for Providing Star Schema Data Model)"
S); 2. "APPARA" by Li-Wen Chen
TUS FOR PROVIDING A REVERSE STAR SCH
United States Patent Application No. _________ (Attorney Docket No .: EMA DATA MODEL (apparatus for providing a star schema data model))
19608-000120US); 3. "SYSTEM" by Li-Wen Chen
FOR PROVIDING A REVERSE STAR SCHEMA
US Patent Application No. _________ (Attorney Docket Number: 1) entitled "DATA MODEL (System for Providing Star Schema Data Model)"
9608-000130US);

BACKGROUND OF THE INVENTION The present invention relates generally to computer database systems, and more specifically to a method for organizing information from one or more systems within a data warehouse environment. .

Until just a few years ago, no one had foreseen such a rapid development of computer technology. Computers are now found in homes, offices, schools, as well as in briefcases and shoulder bags. As computer automation continually impacts the ever-growing part of our daily lives, governments, businesses, and individuals can direct the flow of database technology to themselves, an “explosion of information” or It has come to rely on the management of information, which is inevitable to be sorted, assimilated and managed on an ongoing basis, which grows exponentially. One important area in the database design field is data model selection for database applications.

A data model represents the structure or organization of data stored in a database. This allows the use of data in a particular format and limits the data used in other formats. Different applications usually require different data models. The existence of many different data models is allowed,
Generally they have obvious differences. Typically, database applications are customized to a particular data model for a particular database. Different database vendors base each product on different data models, which adds to the confusion. In general, these applications are unavoidable for reimplementation with respect to different databases, even if the functionality of the applications is kept the same.

At the present time, database developers are turning to data warehousing technology to resolve the often-infested conflicting data management requirements. Traditional data warehousing approaches have focused on decision support applications that focus on a summary of information. While these systems have a perceptual advantage, they have the inherent drawback of losing transaction details about the customer's identity. Traditional approaches expose this drawback when applied to applications such as customer data analysis. Customer data analysis is a decision support analysis that correlates data with customer activities, events, transactions, status, etc. The summarized information typically loses information about the customer's identity at a level of detail, limiting the usefulness of traditional data warehouse approaches in these types of applications.

What is needed there is a method of providing a database that can be customized to meet the needs of individual users and that can support data analysis applications.

SUMMARY OF THE INVENTION The present invention provides techniques for organizing information from various sources, including legacy systems, in a data warehouse environment. In one exemplary embodiment, the present invention provides a method for analyzing data from one or more databases of an enterprise. This method provides a metamodel-based technique for modeling the company's data. This company is generally a business activity, but can also be a trajectory of human activity. Embodiments in accordance with the present invention are capable of translating data from various sources into a particular database schema to provide an organizational method for a data warehouse environment.

The method involves various steps such as providing a model for the enterprise.
The model may be a metamodel that describes at a high level the information used by the company. A metamodel can describe the relationships between groups of entities within the data model. Entities in the data model may include specific data types, etc. A business may be a business activity and / or the like. Forming the data organization from the model is also part of the method. The data organization can include a data schema and the like. The data schema defines aspects of the database to the database management system (DBMS): attributes, domains, parameters, etc. The method also includes the step of creating one or more databases to contain the data. Further, translating data from one or more sources into the data organization is also part of the method. The step of incorporating the data into the database is also part of the method. Furthermore, the method can also include the step of performing an analysis on the data in the database. In other words, from the combination of these steps, it becomes possible to provide an environment for analyzing information about customers, business processes, and the like.

In another aspect of the invention, data warehousing techniques are provided. In a particular embodiment, the present invention provides a method of creating a database for the organization of information from one or more sources. In some implementations, a data schema, such as a star schema (rev
data in a database according to the erse star schema).
The star schema model is based on identity elements (for example, core
Components, etc.) and one or more entities (eg, customer classification components, etc.) that describe a classification of data that may have one or more relationships with identity elements. In one exemplary implementation, a customer classification component provides different ways of categorizing customers, or different business views of customers, for example. Customers can be categorized by geographic region, demographics, or other criteria, for example. The method includes various steps, including selecting one from a pre-defined data model template based on one or more business requirements. The method also includes making a selection from pre-defined customer entities that are compatible with the application based on their respective business processes and operations. Entities can be selected from a focal group, for example. In particular embodiments, focal groups can provide customer characteristics, profiles, business-related categorizations, customer roles, definitions, and other similar descriptions of various business functional areas.

Defining the entities and their attributes relating to transactions and / or events and forming a customized group of customer activity components relevant to a particular application is also part of this method. These events can be organized into customer activity components. These components can be organized into one or more customized groups for various operations and / or transactions. Since the trading of events can be spread over time, these components make up a set of business measurements as well as attributes. These events can also be independent of each other,
It can also be made dependent. A particular sequence of events can be used to describe different stages of customer activity. For example, at a particular time interval, a customer may go through a sequence of events: subscription>billing>payment>promotion> change price plan> service call> cancel. Each event can be associated with multiple business processes or operations that reflect the customer's life cycle. The method further includes defining one or more types of customer events in the customer activity component. Also included in the method is the step of selecting a data table and attribute that includes the source of the set of data tables having a particular data schema and attribute.

The method may also include the step of determining one or more attributes based on the data type and primary key in the source table and the foreign key. The step of creating one or more databases from the schema is also part of this method. The database can be a customer data warehouse or the like. The creation of data movement mapping rules can also be part of this method. Such a mapping rule can provide information about the attributes of the data source to the data warehouse as well as the translation of the information in the table.

The method in one embodiment in accordance with the invention provides a user with the ability to define user-specific application-specific entities within a customer activity component. In some implementations, the user can select from a number of predefined attributes as well as define their own attributes. Many implementations in accordance with the present invention provide the ability to automatically derive data types. Also, in some implementations, it is possible to provide the option of translating from one data type to another. Further, in some implementations, the user is provided with the ability to change the automatically guided data type. Some embodiments may also provide database content analysis capabilities such as market basket analysis, customer valuation analysis, customer segmentation regarding customer purchasing behavior.

The present invention achieves many benefits over conventional techniques. The present invention can also provide techniques for providing a data model that can be customized to suit various business needs and yet support reusable application code. Furthermore, some embodiments using techniques and data models according to the present invention can be used to solve customer data analysis problems. Many implementations provide the user with the ability to customize the user's data model while providing a set of comprehensive and reusable customer data analysis capabilities. Many implementations enable easier and faster construction of business applications than previously known methods. These and other benefits are described throughout the specification. A further understanding of the nature and advantages of the present invention may be gained by reference to the remaining portions of the specification and the accompanying drawings.

DESCRIPTION OF SPECIFIC EMBODIMENTS The present invention provides techniques, including methods for organizing information from one or more databases, including legacy databases, in a data warehouse environment. The method according to the present invention can provide more effective use and storage of many types of information. In an exemplary embodiment, the invention provides a method of serving a customer-centric data warehouse as business information. Although the present invention generally considers such customer-centric applications, one of ordinary skill in the art will appreciate that many other types of information can be organized and analyzed using this technique in the present invention. You will understand. Therefore, the following discussion is intended to be illustrative and not limiting.

FIG. 1 illustrates a schematic exploded view of an exemplary customer data analysis structure for a data warehouse in a particular embodiment according to the present invention. This diagram is merely an illustration, which should not limit the scope of the claims herein. Those of ordinary skill in the art will appreciate other changes, modifications,
And will recognize alternatives. FIG. 1 shows customer-centered information such as a schema creation element indicated by an arrow 107, a plurality of analysis functions 102 and 104, and reference data indicated by an arrow 111 stored in a data warehouse 100. 1 provides a high level overview of a number of elements in a particular embodiment in accordance with the invention, including a data warehouse 100. A plurality of data sources 101 from which data can be moved to the data warehouse 100, as indicated by arrow 112, are also illustrated in FIG. The data warehouse 100 provides ease of data access to multiple functions and applications, such as metadata-aware customer data analysis function 102 and created customer data analysis function 104. The customer data analysis application 103 can call a function, the customer data analysis function 102 for metadata recognition and the created customer data analysis function 1
Both 04 can be processed. Many of these elements are defined by the data warehouse builder 109.

The presently preferred embodiment includes a data warehouse builder 1 that is key to the environment.
09 is mentioned. The data warehouse builder 109 operates to perform a wide variety of tasks for assembling and managing the data warehouse environment. Such assembling and managing operations include metadata information gathered from business requirements, such as data schema 107, data processing rules and data movement rules 10.
6 includes such work as integrating data source analysis. The data warehouse builder 109 also creates a data schema, such as the data schema 107, for a customer-centric data warehouse, such as the data warehouse 109, and data for metadata, such as the data repository 105. You can perform the work of providing a repository.

The metadata defined and organized by the data warehouse builder 109 includes data schema such as star schema and reverse star schema.
Users on schema and many other data schema topologies, multiple source data processing, move and load rules, job scheduling for loading and maintaining data flow within the data warehouse, and coding for customer data analysis functions. The parameters provided by In addition, the data warehouse builder uses the customer data analysis function codes 102, 104.
To manage the assembly and maintenance operations of the data warehouse 100 and to manage the data flow across the systems 110-112.
You can manage to build and maintain.

The presently preferred embodiment according to the invention provides a data warehouse 100 defined by a data warehouse builder 109. Data warehouse 1
00 provides support for customer-centric data analysis and customer-centric decision making. The data warehouse 100 is illustrated in FIG.
Data from multiple sources, as indicated by arrow 112 in FIG. Data sources can include existing legacy databases, existing business applications, legacy computing methods, management information systems, and the like. This data warehouse is defined according to the data schema 107 created by the data warehouse builder 109.

Data source 101 represents one or more legacy systems that have original customer data. Typically, such legacy systems are operational systems that support a company's day-to-day work. Typically, data 112 derived from these data sources may be, for example, single layer files, ASCII files,
It may be in any of a wide variety of different formats such as relational database tables and the like. Embodiments in accordance with the present invention provide the ability to pre-process data prior to incorporating it into a data warehouse. Further, the present invention provides a technique for populating data 112 in a customer-centric data warehouse 100. Before moving to the data warehouse and populating the database, the data warehouse builder 109
Data 11 based on one or more of the plurality of mapping rules 106 according to
The two can be integrated, converted and formatted.

The customer data analysis functions 102, 104 can access data stored in a customer-centric data warehouse and analyze customer behavior, patterns that correlate to business activities, and the like. The customer data analysis functions 102, 104 may include market basket analysis, customer evaluation analysis, customer subdivision, etc. regarding customer purchasing behavior. The customer analysis function is a static metadata recognition customer analysis function 102.
And a dynamically created customer data analysis function 104. Such analysis capabilities can analyze the contents of databases that provide insight into a company's business.

In certain embodiments in accordance with the present invention, metadata aware customer analytics functionality 102 may be provided as a built-in functionality. Such features include data warehouse
Metadata such as that stored in the metadata repository 105 of the builder 109 can be accessed. In the presently preferred embodiment, the metadata repository 105 can include information about the data schema. Such metadata has made it possible to access applications, understand and retrieve data residing in customer-centric data warehouse 100.

In a particular embodiment in accordance with the invention, a data warehouse builder, such as data warehouse builder 109, is based in part on a plurality of metadata information as indicated by arrow 108 in FIG. Create code for one or more of the plurality of dynamically created customer data analysis functions 104. Metadata information can be embedded in the code created by the data warehouse builder 109.

Customer data analysis application 103 includes front-end business applications that use analysis functions, such as analysis functions 102, 104. These applications deal with information related to the customer and, in particular, to correlate the customer's business transactions with other activities. Electronic commerce applications, sales force automation applications, customer service applications, marketing automation applications are examples of applications that use customer data analysis capabilities.

FIG. 2 depicts a representative computer system suitable for implementing particular embodiments in accordance with the present invention. This diagram is merely an illustration and should not limit the scope of the claims herein. Those of ordinary skill in the art will recognize other changes, modifications, and alternatives. FIG. 2 illustrates the basic subsystems of computer system 113 suitable for use with the present invention. 2, the computer system 113 includes a central processing unit 114, a system memory 116, an input / output controller 118, an external device such as a printer (not shown), and a display adapter 12.
A bus 115 interconnected with the main subsystems such as a display screen through 6, a serial port 128, a keyboard 132, a fixed disk drive 144 and a floppy disk drive 136 operative to receive a floppy disk 138. Include.

Scanner (not shown) via I / O controller 118, serial port 1
28 or many other devices such as a mouse 146 connected to the network interface 148 may be connected.

Many other devices or subsystems (not shown) may be connected in a similar manner. Also, as discussed below, not all of the devices shown in Figure 2 to be presented to implement the present invention are required. Devices and subsystems may be interconnected in different ways than shown in FIG. Operation of a computer system as shown in FIG. 2 is readily known in the art and will not be discussed in detail in this application. The source code for implementing the invention may be operably arranged in the system memory 116 or may be stored on a fixed disk 114 or a removable disk 138 or a storage medium such as a CDROM.

In the presently preferred embodiment according to the present invention, a novel reverse star schema
The data model provides customer data analysis capabilities operatively processed in system memory 116 or fixed disk 144 of system 113 of FIG. Customer data analysis can include, but is not limited to, decision support analysis that correlates enterprise decisions with customer behavior. The customer data analysis application analyzes based on customer identification,
Customer activities, events, transactions, and positions can be correlated with customer identities. Approaches such as decision support applications, summarization techniques, etc. may also be used without departing from the scope of the invention.

The data model represents the organization of data in a database. The choice of data model facilitates the use of data on the one hand, but may limit the use of data on the other. Assembling an application that is built to work with a particular data model can be completely incompatible with other data models. Such applications are often re-executed for databases with different data models, even though the underlying application logic is similar. A metamodel is an abstract data model that describes the relationships between different entities or groups of entities in the data model. Different business applications can customize the data model according to the relationships described in the metamodel. The database application can be written to fit the metamodel and can refer to the detailed data model via such means as a data dictionary if necessary. These and other techniques in accordance with the invention make application code reusable.

FIG. 3A shows a simplified entity-relationship diagram of an exemplary metamodel having a star-schema structure according to a particular embodiment of the present invention. This diagram is merely an illustration, which should not limit the scope of the claims herein. Other changes, modifications,
And will recognize alternatives. The business index group 203 represents a total performance index related to business activities. Business indicator group 203 may include one or more “factual elements” such as factual element 200. A fact element represents a concrete measurement value in a business target area that a decision maker of a company desires to analyze. For example, common fact factors include a number of performance measures such as sales volume, gross profit sales volume, and so on. FIG. 3A depicts a plurality of business activity groups 204 representing business processes or activities that present or describe metrics in the business metrics group 203.
Embodiments may have any number of business activity groups, such as business activity group 204 of FIG. 3A. Business activity group 204 includes one or more dimensional elements 201, one or more dimensional lookup elements 2
02 etc. are included. Dimension element 201 represents a particular business activity that characterizes the data in the fact element. For example, items such as products and sales networks may be dimensions related to sales facts. The dimension lookup element 202 describes detailed information about the dimension element 201. For example, items such as product classification, product style, etc.
It can be a dimension lookup element corresponding to a product dimension element.

Embodiments that use the Star Schema data model are useful in providing a macroscopic view of business activity. Embodiments that provide such a macroscopic perspective enable a decision support system to provide decision-related information using the "holistic perspective" as a guide. Thus, an embodiment-based star schema can provide a static solution based on pre-defined dimensions and summarized data. The data collection provided by the embodiment with a star schema is
Due to the nature of multidimensional models, it can provide a high level of analytical perspective.

FIG. 3B illustrates an exemplary metamodel for a reverse star schema in a particular embodiment for performing customer data analysis in accordance with the present invention. This diagram is merely an illustration, which should not limit the scope of the claims herein. Those of ordinary skill in the art will recognize other changes, modifications, and alternatives. Multiple customer data analyzes correlate with customer events or activities that originate from various business activities that span one or more areas of business activity. In a particular embodiment, the customer data analysis is
Responsible for interrelating various activities within various customer activity elements, such as customer activity element 215 of FIG. 3B. Such analysis can be correlated with customer activity via multiple core elements, such as core element 212. The data in core element 212 can be used in identifying customer activity entities from various customer activity elements. In some embodiments, the data analysis is performed on a more detailed customer activity. Such embodiments search for one or more activity lookup elements, such as activity lookup element 214, for more detailed customer activity data. The data in the customer classification element provides another way to classify customers or their various business views. For example, customers can be classified by geographic region, demographics, and so on. Embodiments that use one or more such customer classifiers provide multiple potentially useful ways of viewing the results of analysis of customer data. Embodiments using the inverse star scheme provide a detailed view of the data, which provides the ability to perform conceptual based analyzes such as customer data, customer activity and their correlation at the transaction or event level.

FIG. 4A illustrates a simplified general form of an exemplary data model in certain embodiments according to this invention. This diagram is merely an example, which should not limit the scope of the claims herein. Those skilled in the art will appreciate other variations, modifications and alternatives. The data model of FIG. 4A is a data model having a reverse star schema structure. FIG. 4A illustrates an exemplary data model that includes a focal group 210 that includes at least one of a plurality of core components 212, at least one of a plurality of customer classification components 213, and a plurality of customer classification components 213. At least one of the plurality of customization groups 211, at least one of the plurality of customer activity components 215, and at least one of the plurality of activity lookup components 214. Also, other components can be included, and all illustrated components need not be present in some embodiments without departing from the scope of the present invention.

Focal groups, such as focal group 210, include components that describe information such as customer characteristics, profiles, business-related classifications, customer roles, definitions, etc. in different business functional areas. FIG. 4A illustrates two types of components in the focal group 210. Other components may be included without departing from the scope of the invention. FIG. 4A illustrates core component 212 and customer classification component 213. The core component 212 includes a customer entity (CC1),
Other related customer identification data designated by (CC2-CCn). Information such as account identification, social security number, encrypted name, etc. are examples of such customer identification data. These entities are particularly useful in performing customer event correlation analysis. Customer classification component 213 describes information about the role or positioning of customers in a business organization or process. These descriptive components may relate to the structure or organization of the customer's business, such as information such as region, area, sales organization, or characteristics of the customer, such as business overview, statistics, current profile.

The components of the customization group 211 support various forms of business commerce. These components can be independent or dependent on each other as event transactions can be spread over time. A particular sequence of events includes a set of business instruments and attributes. These events can be used to describe different stages of customer activity. For example, during a particular period, the customer may apply>bill>pay>
You may experience a series of events such as sales promotion> price plan change> service call> cancellation. Each event can include multiple different business processes or actions that reflect the customer's life cycle.

Customization group 211 includes a plurality of customer activity components 215, a plurality of activity lookup components 214, and so on. Customer activity component 215 may represent an event transaction or instrument related to a customer activity. These entities can include one or more attributes, such as transaction type, transaction timestamp, and so on. Once the customer activity component 215 is defined, a domain value for the type of transaction is selected. Transaction type is a useful attribute in event correlation analysis. In certain embodiments,
A user can define a customer activity component, such as customer activity component 215, by selecting an attribute from a plurality of pre-created attributes. Also, some embodiments may provide the ability to add user-defined attributes. Many embodiments include customer activity entities (eg, FIG.
It provides the function to define A's CAC3). The activity lookup component 214 represents an entity that details the characteristics of a customer event transaction. For example,
Products purchased during the transaction, store locations of purchases, etc. may be stored as activity lookup entities 214 for analysis.

FIG. 4B illustrates a simplified entity-relationship diagram of a representative example of a data model having a reverse star schema structure in a particular embodiment according to the present invention. This diagram is merely an example, which should not limit the scope of the claims herein. Those skilled in the art will appreciate other variations, modifications and alternatives. In the exemplary data model of FIG. 4B, core component 212 of FIG. 4A
Includes a customer entity 220 and an account entity 222. Arrow 221 indicates a one-to-many relationship between customer entity 220 and account entity. The presence of entities such as customer entity 220 and account entity 222 allows data model 219 to provide an account level concept of customer-related queries in the business model under consideration. FIG. 4B illustrates the customer classification component 213 of FIG. 4A including multiple entities, four of which are shown: a sales channel entity 228, a customer domain entity 230, a customer profile entity 224, and a statistics entity 226. Many embodiments may include other entities or only some, but not all, of these entities without departing from the scope of the invention.

In certain exemplary embodiments, statistics entity 226, customer profile entity 224, and customer domain entity 230 have a relationship with customer entity 220. For example, as shown in FIG. 4B, arrow 22
3 shows a many-to-one relationship between the customer entity 220 and the statistical entity 226. Similarly, arrow 225 indicates customer entity 220 and customer profile 2
24, and the arrow 227 indicates the many-to-one relationship between the customer entity 220 and the area entity 230. Further, in this particular embodiment, sales channel entity 228 has a one-to-many relationship with account entity 222, as indicated by arrow 229 in FIG. 4B.

In a particular exemplary embodiment illustrated by FIG. 4B, the different entities include customer activity component 215 of FIG. 4A. These entities include billing transaction entity 232, purchase / return transaction entity 234, service event entity 236, sales activity entity 240, promotion entity 242, and user-defined event 238. Further, FIG. 4B illustrates an activity lookup component that includes a product entity 244. Other entities not shown or described herein may also be included in some embodiments according to the invention. Moreover, some embodiments may not provide all of the entities described herein without departing from the scope of the invention.

FIG. 5A is a simplified process block diagram of an exemplary process for customizing a data model in a particular embodiment in accordance with the present invention. This diagram is merely an example, which should not limit the scope of the claims herein. Those of ordinary skill in the art will recognize other variations, modifications and variations. In the embodiment of FIG. 5A, template schema 401 is transformed into a physical schema 403 that can be used to create a customer-centric data warehouse 100, such as customer-centric data warehouse 100. In a particular embodiment according to the present invention, a customer may, as illustrated by business requirement 405 and data source analysis 406 of FIG.
Customize your data warehouse based on the characteristics of your particular application and the availability of source data.

Template schema 401, which includes templates for data schemas, can include multiple entities that provide support for a wide variety of different types of applications. Entities and attributes related to a particular application have been selected, defined, and defined by processes such as business model 407, selected business entities 408, to form a customized group, such as customized group 211 of FIG. 4A. May be excluded by customer transaction event entity and attribute 409. Further, new entities can be added to a particular business model. The logical schema 402 can be a logical data model that includes convenient entities and attributes, both built-in and customized. Processes such as selected business model 407, selected business entity 408, selected and defined customer transaction event entity and attribute 409, and selected source data and attribute 412 are from template schema 401. It provides the inputs for creating the logical schema 402. Physical schema 403, which may include types of events or other transactions, is a customer-centric data warehouse with data type definitions 411 and 413, primary and foreign key 411 determination, and customer event type 410 definition. Can be used to create Further, in certain embodiments, multiple association rules 404 may be used to map a data source, such as data source 101, to a customer-centric data warehouse, such as customer-centric data warehouse 100. It is possible. The mapping rules 404 include meta data that describes how the data in the external source maps to the data tables and attributes in the data warehouse. The association rule 404 can further include multiple variations in some embodiments.

FIG. 5B illustrates an exemplary flow diagram of a simple process for creating a data model having a reverse star schema structure in a particular embodiment according to the present invention. This diagram is merely an example, which should not limit the scope of the claims herein. One of ordinary skill in the art will recognize other variations, modifications, and alternatives. FIG. 5B shows the first step 4 in which the business model template is selected.
07 is illustrated. The selected business model can remove unnecessary entities from further consideration. In this step, the predefined business model is selected from the plurality of predefined business models. Different applications can use different business models with the entities involved in their business processes and behaviors. In the currently preferred embodiment, the user can select from multiple pre-created entities in the template for a particular data schema 401 based on business requirements 405 related to the user's business. Then, in step 408, the customer entity determines the focal group 2 of FIG. 4A.
Selected from multiple pre-defined customer entities in a focal group such as 10. This removes further unrelated entities from further consideration. Entities included in core component 212 and customer classification component 213 can be selected based on their suitability to user requirements based on their business processes and behaviors.

In step 409, customer transaction and event entities and their attributes are defined to create a customer group, such as customization group 211 of FIG. 4A. The user may select an event trading entity for the customer activity component that is relevant to the characteristics of the user's application. Also, in particular embodiments, users may define their application-specific entities in customer activity component 215. In some embodiments, the user can select from a number of pre-defined attributes and define their own attributes. Entities in customer activity component 215 are linked to detailed information that can be accessed via multiple activity lookup components 214 that can be defined. The activity lookup component 214 can include, for example, a business entity referenced in a commercial transaction such as a product, a store, etc.

In step 410, multiple customer event types are defined in the customer activity component. In many embodiments, a user may define an event transaction type that will be used in a customer activity component, such as customer activity component 215 of Figure 4A. In many embodiments, these event transaction types can be used as domain constraints when a data warehouse is created. Some embodiments provide event trading as attribute values for customer event correlation queries during customer data analysis.

In step 412, the source data tables and attributes are 4
Selected to match the selected entity, such as the entity selected at 07. In some embodiments, a user may select a data table and attributes to include a source of data tables and attributes for a customer-centric data warehouse, such as source data 101, a data source Can be browsed and navigated via.

In step 413, the plurality of data types is the source data 1 of FIG.
Determined based on source data such as 01. The data types of the tables and attributes in the customer-centric data warehouse can be derived based on the data types in the source table. Many embodiments provide the ability to automatically derive these data types. Also, some embodiments provide the ability for the user to change what is automatically derived.

In step 411, the data type and primary and foreign keys (if desired) are determined. In certain embodiments, the relationship between the data type and the primary and foreign keys can be completed. This is done using some of the techniques known to those skilled in the art, such as Oakbrook Terra, Illinois.
Headquartered in Platinum Technologies, Inc.
Using a database design tool called ER Win / ERX by
Can be realized. However, other equivalent products and methods may be used without departing from the scope of the present invention.

At step 414, a customer-centric data warehouse database is created from the schema created at step 411. This data warehouse
The builder 100 can create a customer-centric data warehouse based on the schema 403 and the database configuration information provided by the user. The data warehouse builder uses database commands and programming interfaces to accomplish the creation of the data warehouse.

In step 415, multiple data movement association rules are created. The association rules provide information about the conversion of information in tables and attributes of data sources, such as data source 101, to a customer-centric data warehouse, such as the customer-centric data warehouse created in step 414. It uses several of the techniques known to those skilled in the art, for example E, which is headquartered in Austin, Texas.
volumeary Technologies International
It can be implemented using a database design tool called EXTRACT by al. However, equivalent products or methods may be used without departing from the scope of the present invention.

6A-6F illustrate simplified user interface screens in an exemplary data warehousing method in certain embodiments in accordance with the present invention. These drawings are merely illustrations and should not limit the scope of the claims herein. Those skilled in the art will recognize other variations, modifications and alternatives.
FIG. 6A illustrates a project screen 501 having a hierarchical view of functional components in a particular embodiment according to the present invention. The first level is
It has components to create a data warehouse for a particular enterprise.
Project ”folder 502 is included. Below that project level is a component level with multiple components belonging to the parent project folder. First component 5 of the "Reverse Star Schema"
04 is highlighted. This highlight indicates that this component is the next component defined. Other components include a customer analysis component 506, an application component 508, a physical layout component 510, a data conversion component 512, a data loading component 514, and an OLAP component 516. Other components may be included in many embodiments without departing from the scope of the invention. When the user selects option 504,
The business model dialog box appears.

FIG. 6B illustrates a business model dialog box 503 in a particular embodiment according to the invention. Whenever the "Reverse Star Schema" component 504 is selected from the project start screen 501, a dialog box 503 is displayed. Dialog box 503
Users can customize business templates based on the business model prevailing in a particular industry. Dialog box 50
3 includes a business model selection field 520 that illustrates that the user is selecting a business model for the communications industry. A plurality of business characteristics are promoted using check boxes 522 that include whether the business is account-based, product-based, or service-based. By entering the appropriate information and clicking the "OK" button, this embodiment displays the database creation screen.

FIG. 6C shows the contents of the general tab of the database creation screen 505. The screen 505 includes a database name field 530, a database file area 532, and a file characteristics area 534. The user enters the appropriate information in these fields and clicks on the "OK" button. The user can select the transaction tab to display the screen 507 of FIG. 6D. Screen 507 includes fields for entering information regarding the transaction type and size of the database including file area 542 and file characteristic area 544. The user enters the appropriate information in these fields and clicks on the "OK" button. The user can then go to the Reverse Star Schema customization screen.

FIG. 6E illustrates a reverse star schema customization screen 509 in a particular embodiment according to the present invention. The customization screen 509 illustrates the focal group 550 displayed to the user. The user highlights a particular group, such as focal group 550, and causes the embodiment to display detailed information about that group for editing or the like. The selection box 551 allows the user to choose between adding a new group or editing or deleting an existing group. Focal group 5
50 includes a plurality of tables 552-559. You can add new tables or edit existing tables in a group, plan for layout, identify data sources, identify data transformations for each table or column, or load data for tables. It is possible to make a plan.

FIG. 6F illustrates a data schema customization screen 511 in a particular embodiment according to the present invention. The customization screen 511 illustrates the customization group 560 displayed to the user. Customize Group 560
Contains a plurality of entities, such as highlighted entity 562, that supplies the “transaction id”. Customized group 560 includes activity lookup
It has a relationship with the group 564 and the activity lookup group 566. Select box 568 allows the user to define and configure additional entities and activity lookup groups.

6A-6F illustrate a user interface in a particular embodiment according to the present invention. 6A-6F provide a reverse star schema data model. However, other data models and organizations of data may be used without departing from the scope of the present invention.

FIG. 7A illustrates an exemplary customer data analysis function, such as customer data analysis functions 102, 104 of FIG. 1, in a particular embodiment according to the present invention. This diagram is merely an example, which should not limit the scope of the claims herein. Those skilled in the art will appreciate other variations, modifications and alternatives. Customer data analysis includes customer purchase behavior analysis, customer (market) segmentation, customer evaluation, customer transfer (ch
urn) analysis, including multiple customer data analysis types. Such analysis can be integrated with the meta-model of the data schema described above. These features work across different business applications and embodiments that use the same data model.

FIG. 7A details an exemplary plurality of customer analysis functions that are divided into various layers of software in a particular embodiment according to the invention. Many other embodiments that arrange these functional layers in various orders and add or remove features without departing from the scope of the invention may be considered by one of ordinary skill in the art. it can. The metadata access layer 605 is a metadata repository 600.
Provides functionality to communicate with and access metadata from a metadata repository such as. A mapping layer 604 between the metamodel and the data schema provides the functionality to translate the entities from the metamodel to the data schema to form a database. In many embodiments, SQL queries can then be generated to query the resulting database.

Customer activity correlation analysis layer 603 provides the basis for customer data analysis in layer 602. Layer 603 provides functionality for customer activity correlation queries to access a customer-centric data warehouse. Layer 603 is the metadata repository 6
The metamodel at 00 is referenced to determine the data schema of the data in the customer-centric data warehouse.

The customer data analysis layer 602 provides a customer data analysis function. Layer 602 consists of components that can analyze information about the customer. These customer analysis components can be composed of a market basket analysis function and a customer evaluation function. The result of the analysis performed by the customer data analysis function 602 can be used by the business application 601. The customer data analysis function provides the function of defining the parameters for these functions. These parameters are metadata
Stored in repository 600 and accessed by this layer.

FIG. 7B illustrates a representative customer-centric data warehouse builder, such as customer-centric data warehouse builder 109 of FIG. 1, in certain embodiments in accordance with the invention. This diagram is for illustration purposes only and should not limit the scope of the claims. One of ordinary skill in the art will recognize other variations, modifications and alternatives. The customer-centric data warehouse builder of FIG. 7B includes a data schema designer module 623 that is operable to integrate business model requirements and data source analysis and generate target data for the customer-centric data warehouse. . The process of data schema generation was discussed with reference to Figure 5B. Module 623 provides a graphical user interface that allows a user to plan, edit, navigate, etc. a data model defined according to the process steps of FIG. 5B.

The metadata repository 600 is a schema designer module 623.
Such as reverse star schemas, data movement mapping rules, various job schedules for storing or maintaining data flows in and within the data warehouse, and user parameters for code generation for customer data analysis. , Storing metadata, including metadata about the data schema.

The code generation module 624 generates a customer data analysis function code. The function code is generated based on a stored metacode, such as the metadata stored in the metadata repository 600. The metadata, such as the complete data schema, and the user parameters input to the customer data analysis function designer module 621 comprise the input part of the code generation process. In particular embodiments according to the invention, code generation may be performed based on a set of built-in function code templates.

The job / event / schedule management module 627 manages the operations of creating, managing, and maintaining a customer-centric data warehouse environment. This can include jobs, events, exclusions, schedules, etc. through the data warehouse environment. Planned tasks can be managed as jobs using schedules. Occurrences of activities can be represented as events and exclusions, and can be managed by this module. Events and exclusions can trigger other jobs in some environments.

The data processing / movement / reading management module 626 provides pre-processing of data prior to data movement, physical data transfer through the network, and data reading tasks on the destination machine.

The data warehouse formation and management module 625 manages the formation of the data warehouse, including database object management tasks such as table generation, physical space management, and indexing. In addition, module 625 manages the operation of the data warehouse after it has been created.

The customer data analysis function designer module 621 includes the customer data analysis function 1
The configurations of 02 and 104 are defined. Since the data analysis function is designed to be customized, the customer data analysis function designer module 621 allows the user to define various types of analysis functions. This module provides the user with a graphical user interface that constitutes the user's own customer data analysis functions for his application. The user-defined configuration information can be stored in the metadata repository as analysis function metadata, such as analysis function metadata 600.

The data source analysis module 622 performs data source analysis. Data analysis can include source data models, data domain values and data volume analysis, and so on. This module provides the user with a graphical user interface for user analysis activities.

Management console 628 is a graphical user interface that integrates user design and management activities. The management console 628 includes a data schema designer 623, a data source analyzer 622, an operation planner 629,
And an interface with the customer data analysis function designer 621 and the like.

The operations planner module 629 provides a graphical user interface for planning the operational aspects of the customer data analysis environment.
The module 629 uses operation metadata such as job rules, event rules, and schedule information as metadata to retrieve the job / event / schedule management module 627 and perform a task based on the operational metadata. -It can be stored in the repository 600.

The database application consists of application code based on the data model used for the database. Typically, application code is designed and developed after the data model is realized. The application code follows a certain abstract data model pattern called the metamodel, thus providing the portability of that code to other applications. Applications designed for a particular metamodel can be reused in other similar situations. By making customizations and these customization-related details available as metadata to application code, an application can function with the unique requirements of a particular customer. Database designers choose among competing factors to design a particular data model and attach a metamodel. Factors to consider include (1) flexibility in customizing the data model to meet different business application needs, (2) flexibility in designing reusable application code, and (3). It consists of the level of usefulness of the application functionality by using the data model.

For example, a star schema data model combined with a multidimensional meta model is 1
It consists of a combination of three expected data models and metamodels. A star schema consists of a data model, and a multidimensional model consists of a metamodel. A data warehouse schema can be implemented using many data model and meta model combinations. The metamodel simplifies the analysis or understanding of the implementation or efficiency of operational forms of operation in an enterprise. The multidimensional model provides a detailed description of the firm's performance. Typically, this detailed description can be highly summarized so that corporate managers, such as corporate officers and government employees, are provided with a clear "critical situation" for the company. The data is indexed by multiple processes and morphologies within the enterprise to provide various perspectives to the enterprise manager.

In many embodiments, a multidimensional model is mapped to a particular rational data model called a schema, which can be a star schema or some other schema. A schema is a database organization that corresponds to a data model. The records in the rational database dimension table map to multiple representations of the dimensions in the multidimensional model, and the records in the database fact table map to measures or data points in the multidimensional model. In many embodiments, the data model provides an operational fulfillment analysis used by multiple enterprise decision support applications. Various applications, tools, systems, frameworks, database managers, etc. can be implemented as reusable products based on metamodels or multidimensional models, and data models or schemas and their variants.

CONCLUSION While the above generally describes the present invention according to a particular system, the present invention
It has a very wide range of applicability. In particular, the invention is not limited to any particular type of data schema, but can be applied to any data model where improved or optimized analysis is desired for use in customer-centric data warehouse systems and applications. Thus, in some embodiments, the techniques of the present invention will provide access to many accumulated business, government and education databases of all types. Of course, one of ordinary skill in the art will recognize other variations, modifications and alternatives.

[Brief description of drawings]

[Figure 1]   2 illustrates an exemplary architecture for data analysis according to the present invention.

FIG. 2 illustrates a representative computer system suitable for implementing particular embodiments in accordance with the present invention.

FIG. 3A   3 illustrates an exemplary metamodel in a particular embodiment according to the present invention.

FIG. 3B   3 illustrates an exemplary metamodel in a particular embodiment according to the present invention.

FIG. 4A illustrates an exemplary data model in a particular embodiment according to this invention.

FIG. 4B illustrates an exemplary data model in a particular embodiment according to the present invention.

FIG. 5A illustrates an exemplary flow chart of simplified process steps according to an embodiment of the present invention.

FIG. 5B illustrates an exemplary flow chart of simplified process steps according to an embodiment of the present invention.

FIG. 6A is a representative user interface in a particular embodiment according to the invention.
The screen is illustrated.

FIG. 6B is an exemplary user interface in a particular embodiment according to the present invention.
The screen is illustrated.

FIG. 6C is an exemplary user interface in a particular embodiment according to the invention.
The screen is illustrated.

FIG. 6D is an exemplary user interface in a particular embodiment according to the invention.
The screen is illustrated.

FIG. 6E is an exemplary user interface in a particular embodiment according to the invention.
The screen is illustrated.

FIG. 6F is an exemplary user interface in a particular embodiment according to the invention.
The screen is illustrated.

FIG. 7A illustrates an exemplary data analysis function in a particular embodiment according to the present invention.

FIG. 7B illustrates an exemplary data analysis function in a particular embodiment according to the present invention.

─────────────────────────────────────────────────── ─── Continued front page    (31) Priority claim number 09 / 306,650 (32) Priority date May 6, 1999 (May 6, 1999) (33) Priority claiming countries United States (US) (31) Priority claim number 09 / 306,693 (32) Priority date May 6, 1999 (May 6, 1999) (33) Priority claiming countries United States (US) (81) Designated countries EP (AT, BE, CH, CY, DE, DK, ES, FI, FR, GB, GR, IE, I T, LU, MC, NL, PT, SE), OA (BF, BJ , CF, CG, CI, CM, GA, GN, GW, ML, MR, NE, SN, TD, TG), AP (GH, GM, K E, LS, MW, SD, SL, SZ, TZ, UG, ZW ), EA (AM, AZ, BY, KG, KZ, MD, RU, TJ, TM), AE, AL, AM, AT, AU, AZ, BA, BB, BG, BR, BY, CA, CH, CN, C R, CU, CZ, DE, DK, DM, EE, ES, FI , GB, GD, GE, GH, GM, HR, HU, ID, IL, IN, IS, JP, KE, KG, KP, KR, K Z, LC, LK, LR, LS, LT, LU, LV, MA , MD, MG, MK, MN, MW, MX, NO, NZ, PL, PT, RO, RU, SD, SE, SG, SI, S K, SL, TJ, TM, TR, TT, TZ, UA, UG , US, UZ, VN, YU, ZA, ZW F-term (reference) 5B075 NK02 NR02 NR12 PP02

Claims (34)

[Claims] 1. A computer-based method for analyzing data contained in at least one of a plurality of data sources of a company: a model for the at least one of a plurality of data sources of the company. Providing a data organization for the model; creating a database having the data organization; translating data from the at least one of a plurality of data sources into the data organization and providing a plurality of translations; Forming translated data; incorporating the translated data into the database; and performing an analysis on the translated data in the database, the data organization comprising at least a plurality of data Including one, the data is: (a) at least 1 And (b) at least one attribute element; wherein the identity element and the attribute element have at least one of a plurality of relationships between each other. A method characterized by. 2. The model further comprises: a focal group comprising: at least one of a plurality of classification components; and at least one of a plurality of core components; and a customized A customized group comprising: at least one of a plurality of customer activity components; and at least one of a plurality of activity lookup components; and wherein the customized group comprises: The method of claim 1 having at least one of a plurality of relationships with the focal group. 3. The method of claim 2, wherein the classification component comprises categorization of information related to a customer's business process. 4. The method of claim 2, wherein the activity component comprises a commercial transaction. 5. The method of claim 2, wherein the activity component comprises a business event. 6. The method of claim 2, wherein the activity component comprises a business activity. 7. The method of claim 2, wherein the activity component comprises a measure of business. 8. The method of claim 2, wherein the activity lookup component contains reference information about a customer's business transaction. 9. The method of claim 2, wherein the activity lookup component contains reference information about a customer's business event. 10. The method of claim 2, wherein the activity lookup component contains reference information about a customer's business activity. 11. The method of claim 2, wherein the activity lookup component contains reference information about a customer's business measurements. 12. The data organization further comprises at least one of a plurality of core components, the core component comprising: a customer identifier; and at least one of a plurality of classification components; and The method of claim 1, wherein the classification component has at least one of a plurality of relationships with the core component. 13. The method of claim 12, wherein the core component further comprises an account identifier. 14. The method of claim 12, wherein the classification component includes sales channels. 15. The method of claim 12, wherein the classification component includes customer geographic regions. 16. The method of claim 12, wherein the classification component includes a customer profile. 17. The method of claim 12, wherein the classification component comprises a demographic profile. 18. The method of claim 1, wherein the enterprise constitutes a business. 19. The method of claim 1, wherein the data comprises communication information. 20. The method of claim 1, wherein the data comprises financial information. 21. The method of claim 1, wherein the data comprises retail marketing information. 22. The method of claim 1, wherein the data includes insurance information. 23. The method of claim 1, wherein the data comprises health information. 24. A computer-based method for performing a customer analysis of at least one content of multiple data sources, wherein: one of a plurality of predefined templates embodying multiple characteristics of a business. Selecting at least one of the plurality of customer entities from a plurality of predefined customer entities based on the template to form a focal group of customer entities; at least one of the plurality of customer trading entities Defining one and at least one of a plurality of attributes of the customer transaction entity to form a customized group of customer activity components; at least one of a plurality of customer event types within the customer activity component.
Defining at least one of a plurality of data tables, as well as the data event, the customer event type including attributes of the customer transaction entity within the customer activity component. Selecting at least one of a plurality of attributes of the table to form a data schema, the data schema being a star data schema; a plurality of data schemas based on a data type of the data source table; Determining at least one of a plurality of primary keys for the attribute; creating a data warehouse database from the data schema; and a plurality of data mappings. Steps to create at least one of the rules A is, the mapping rules, steps shall provide the data source attributes and information about the table translation for the data warehouse; wherein to encompass. 25. The method of claim 24, further comprising defining at least one of a plurality of foreign keys for the attribute. 26. The method of claim 24, further comprising: defining an application-specific entity for the customer activity component. 27. At least one of a plurality of attributes for the application-specific entity.
27. The method of claim 26, including the step of defining one. 28. The method of claim 24, wherein the derivation of the data type is performed automatically. 29. Further: for the user, if the user does not use the automatically derived data type,
29. The method of claim 28, comprising providing the ability to change the data type. 30. A method of integrating a data analysis tool with at least one of a plurality of database systems, the method comprising the steps of: installing a data warehouse database;
Said database having a star schema data model, said star schema mapping to data in said at least one of a plurality of database systems; and said data warehouse database. Performing customer data analysis using the method. 31. The method of claim 30, wherein the customer data analysis comprises an analysis of customer purchasing behavior. 32. The method of claim 30, wherein the customer data analysis comprises a customer market segment analysis. 33. The method of claim 30, wherein the customer data analysis comprises a customer valuation analysis. 34. The method of claim 30, wherein the customer data analysis comprises a customer buddy analysis.