| By Gus Bjorklund | Article Rating: |
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| October 19, 2006 01:00 PM EDT | Reads: |
10,967 |
Calculations
Often a data element used in one
service may have a different data format than another and the values
may be expressed in different units (inches versus millimeters,
Fahrenheit versus Celsius, pounds versus kilograms, and so on). One
service may define a value with three decimal places (e.g., 3.602) and
another as a whole number (e.g., 3). Should the decimal value be
rounded or truncated when converting?
The conceptual data modeling tools should be able to define such commonly used conversions as well as application-specific conversion or calculations so that they can be defined once and used where needed. This will make the conversions easier to change and improve overall system reliability.
Industry-Standard Data Models
Standard data models
have been defined by many different industry-specific groups such as
insurance (ACORD), healthcare (HL7), and telecommunications (SID). Most
of these data models are large and complex, with 1,000 or more classes.
If you can use one of these data models, it can save you considerable
effort and ease conformance with your industry's standards.
To use these standards, you'll have to add them to your conceptual data model. Since they're usually large and complex, the modeling tool has to have a means to import them. Once imported, you'll also want to be able to add layers on top of the industry model without changing it. For example, since these data models are usually abstract, attribute names tend to be generic, such as a set of "ContacMediums" instead of "main telephone number" and "home telephone number." You'll want to add mappings to the names used by your own data and applications.
The Metadata Repository
The metadata (the
mappings, transformation rules, data format conversions, validation
rules, and all the other things we've discussed already) have to be
stored somewhere where they can be easily used and where the tools can
get and save their artifacts. This is the role of the metadata
repository. The repository isn't a big shoebox that you can just throw
all the metadata and related artifacts into. It should give you a way
to organize and keep track of everything.
When you use repository-based modeling tools, besides easier implementation, ongoing maintenance costs will be lower and you can achieve the following benefits:
- Automated impact analysis - As you plan changes to your SOA, the tools can provide reports about the interdependencies between services and consumers and the impact of any proposed changes.
- Reusable components - When all the data definitions, transformations, validation rules, conversion formats, and the generated code are stored in the repository, you'll be able to find and reuse elements that have already been developed.
- Correctness - By using the elements defined in the repository where they're needed, overall system and application correctness, consistency, and reliability will be enhanced. You have the assurance that a change can be propagated to all the places it needs to go. Your SOA will have fewer bugs.
Take care of the needs of your service consumers to be sure you can achieve the SOA benefits of simpler and faster application and data integration, agility, and the ability to respond to the needs of your business more effectively. Modeling tools, a common conceptual data model, and a metadata repository will promote reuse, reduce errors, and control the effects of change. Then you can bask in the glorious sunshine of your SOA.
References
Published October 19, 2006 Reads 10,967
Copyright © 2006 SYS-CON Media, Inc. — All Rights Reserved.
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More Stories By Gus Bjorklund
Gus Bjorklund is vice president of technology for Progress Software Corporation and works with technical and business leaders across the company's product units to clarify technical strategies, identify technical synergies, evaluate new technology directions, and coordinate cross-unit technology efforts, particularly in the area of data management. Gus joined Progress in 1989 and has over 30 years of experience in domain-specific programming languages, data communications, industrial automation, real-time control, manufacturing quality control, operating systems, and financial systems.
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