Method and system for developing data integration applications with reusable functional rules that are managed according to their output variables

Abstract

Methods and systems for developing data integration applications with reusable functional rules that are managed according to their output variables. Methods include receiving a functional rule as part of a data integration application, associating the functional rule with the semantic name of its output value, and reusing the same functional rule later in the development process in order to express the same output value.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS[0001]This application claims the benefit under 35 U.S.C. §119(e) of the following application, the contents of which are incorporated by reference herein:[0002]U.S. Provisional Application No. 61 / 052,548, entitled Method and System for Structuring and Performing Data Integration and Conversion Using a Semantic Model, filed on May 12, 2008.[0003]This application is related to the following applications, filed on an even date herewith:[0004]U.S. patent application Ser. No. ______, entitled Method and System for Debugging Data Integration Applications with Reusable Synthetic Data Values;[0005]U.S. patent application Ser. No. ______, entitled Method and System for Managing the Development of Data Integration Projects to Facilitate Project Development and Analysis Thereof,[0006]U.S. patent application Ser. No. ______, entitled Method and System for Developing Data Integration Applications with Reusable Semantic Identifiers to Represent Application ...

AI Technical Summary

Benefits of technology

[0011]When a database system is upgraded or replaced, the existing data must be transferred to the new system. This process, called data migration, is becoming increasingly expensive as database systems become larger and more complex. Planning

Problems solved by technology

This process, called data migration, is becoming increasingly expensive as database systems become larger and more complex.

Planning and executing a data migration consumes valuable resources and can often result in considerable downtime.

Also, mistakes in data migration can lead to data corruption, which is not an acceptable risk for institutions that handle sensitive data.

These difficulties are compounded when it is necessary to combine data from several different data storage systems, a process known as data integration.

These are complicated tasks, and they require careful planning and detailed knowledge of the structure of the source databases.

Errors in data integration are common, difficult to diagnose, and expensive to fix.

In the past, data integration applications have typically been developed for a specific database upgrade or merger task, and they become useless after this task is complete.

This ad hoc approach makes it impossible to reuse program code, substantially increasing the cost of development.

Also, it tends to produce applications that are written from scratch and not comprehensively tested, increasing the likelihood of data corruption.

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