Simulate a Smile

 
 
By Peter Coffee  |  Posted 2006-04-24 Email Print this article Print
 
 
 
 
 
 
 

Opinion: Service-oriented computing is an attitude, not just an architecture.

Were past the stage of ITs evolution where people come to a computer in the manner that they pick up a power tool, having a specific task in mind and probably some training in how to do it. The fastest-growing model of computing today, with no end to the trend in sight, is one of systems that seek to recognize a situation or an opportunity to be of service—whether were talking about a process-to-human or a process-to-process relationship.

When this service-based model meets the real-world environment, system builders will find that their task is much more complicated than it seems when theyre crafting a scripted technology demonstration.

A vivid example of the challenge is in voice-synthesis notifications for active environments—for example, those that try to assist the driver of a car. This merely begins with the challenge of meeting rising expectations for a natural-sounding voice that correctly pronounces same-spelled words: for example, "close" ("you are close to an object behind you") and "close" ("please close the trunk lid before driving"). Navigation systems must also deal with database entries that were meant to be read by a person, not spoken by a machine: A now-ancient Digital Equipment Corp. demo showed an algorithm correctly inferring from context to pronounce the text string "St. James St." as "Saint James Street."

Problems like the ones that Ive just described can be solved by experts in speech synthesis who dont know anything about the task environment of driving. Other domains also pose distinctive technical challenges that may seem to represent the crucial problems to be solved—but it may turn out that those milestones, once reached, arent enough to yield a useful result.

Additional perspective is needed to prioritize tasks: in the automotive case, for example, so that warnings about what might be a child playing behind a car thats in reverse are issued more immediately and with greater urgency than warnings of traffic problems or routine service reminders. Allocation of processor cycles, sensor network bandwidth and "air time" on the driver cockpit audio channel all depend on task knowledge and the provision of scalable infrastructure to provide any needed "mother ship" support.

Developers crafting service-oriented architectures for enterprise IT operations should also have this kind of perspective. They need to maintain a big picture of the resources that theyre allocating themselves and also the resources that theyre consuming. They need to consider the manner in which services should best be prioritized by urgency, and enabled to communicate with each other and with the user based on task-related models of what will happen if a request is not fulfilled or a piece of information is not delivered.

Its one thing to build a system thats impressive when its working as planned. Its quite another to build one thats not ridiculous, or worse, when its best-case assumptions arent valid.

Tell me what youd call good service at peter_coffee@ziffdavis.com. Check out eWEEK.coms for the latest news, reviews and analysis in programming environments and developer tools.
 
 
 
 
Peter Coffee is Director of Platform Research at salesforce.com, where he serves as a liaison with the developer community to define the opportunity and clarify developers' technical requirements on the company's evolving Apex Platform. Peter previously spent 18 years with eWEEK (formerly PC Week), the national news magazine of enterprise technology practice, where he reviewed software development tools and methods and wrote regular columns on emerging technologies and professional community issues.Before he began writing full-time in 1989, Peter spent eleven years in technical and management positions at Exxon and The Aerospace Corporation, including management of the latter company's first desktop computing planning team and applied research in applications of artificial intelligence techniques. He holds an engineering degree from MIT and an MBA from Pepperdine University, he has held teaching appointments in computer science, business analytics and information systems management at Pepperdine, UCLA, and Chapman College.
 
 
 
 
 
 
 

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