Automatic match tuning

Abstract

Methods and apparatus, including computer program products, for identifying matches between disparate schemas calculates a degree of similarity between elements of two schemas using each of multiple matching processes. The calculated degrees of similarity are combined using a first weighting vector to produce first combined degrees of similarity. The first weighting vector includes multiple weighting coefficients and each weighting coefficient corresponds to one of the matching processes. The weighting coefficients are tuned using information relating to a predicted degree of matching accuracy associated with the first weighting vector.

Description

BACKGROUND [0001] The present invention relates to data processing by digital computer, and more particularly to mapping elements between disparate schemas. [0002] Integration of applications in an enterprise can lead to more efficient operations. Enterprise application integration can require significant effort when migrating from disparate legacy applications to a more integrated framework. Enterprise application integration can be performed using a message exchange procedure, in which messages are exchanged between different data sets. Application data is typically organized according to the type of application or applications with which the data is designed to operate. As a result, the organization or structure of the data can be highly specialized. The messages used for enterprise application integration are generally structured sets of data in a well-defined syntax. The structure of the data can be referred to as its schema. Countless different schemas and / or schema domains (e...

AI Technical Summary

Benefits of technology

[0007] The invention can be implemented to include one or more of the following advantageous features. The calculated degrees of similarity are combined using each of multiple weighting vectors. Each weighting vector includes multiple weighting coefficients, and each weighting coefficient corresponds to one of the matching processes. The weighting coefficients are tuned by determining, using the combined degrees of similarity for each of the weighting vectors, a predicted degree of matching accuracy associated with each of the weighting vectors. A second weighting vector is selected to determine possible matches between the elements of the two schemas. The second weighting vector is selected based on a comparison of information relating to the respective predicted degrees of matching accuracy associated with the first weighting vector and the second weighting vector. Each predicted degree of matching accuracy is determined using a number of ambiguous matches, a number of unambiguous matches, and / or a number of impossible matches.

[0013] The invention can be implemented to realize one or more of the following advantages. The invention can be used to provide enhanced matching performance, to improve the quality of matching, and / or, depending on the particular algorithms that are used, regulate the number and types of possible matches that are identified for manual review and approval. In addition to providing improved matching results for schemas that previously have been classified, the invention can also be used to provide enhanced matching results for unclassified schemas. In addition, the invention can be used to assist users with manual finishing touches because the system can provide some different mapping examples as suggestions to the user. In other words, the elements of disparate schemas may be mapped without detailed knowledge of the characteristics of the schemas. In this regard, the techniques provide generic data model matching (i.e., the techniques can perform matching independent of the data model). Furthermore, mapping can be performed automatically or at least semi-automatically. One implementation of the invention provides all of the above advantages.

Problems solved by technology

Enterprise application integration can require significant effort when migrating from disparate legacy applications to a more integrated framework.

Simple, hybrid, and composite matchers, however, are inflexible and tend to produce good results for some types of schemas while producing poor results for other types of schemas.

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