October 24, 2009
4 Steps To Better SOARaghuraman Krishnamurthy, Vinod Ranganathan, Baskar Senguttuvan
Ontology gives semantic integration the boost it needs to speed data sharing
Raghuraman Krishnamurthy is a principal architect, Vinod Ranganathan a technical lead, and Baskar Senguttuvan a senior architect at Cognizant's Technology Consulting Group.
Looking back on NASA's effort to integrate data from many different sources, Richard Keller shakes his head.
The space agency wanted to take data collected during a field expedition, including sample data, photos, voice recordings, and GPS information and integrate it with satellite imagery, GIS data, and information about the characteristics of minerals found in the samples. "Not only were the types of information heterogeneous, but the formats ranged widely from spreadsheet files to SQL databases to Web pages," the senior research computer scientist says.
But that was only half the problem. Once he got the information, Keller then needed to determine how data stored in all those different formats was related.
"If you have fields in two separate databases that are both named 'temp,'is it legitimate to assume they represent the same quantity and can be integrated together?" Keller asks.
"One might represent a 'temperature' and the other a 'temporary' value. To properly combine these two fields, you really need to understand what the data represents." (Read more from Keller .)
At NASA and other large entities -- both in government and business -- integrating heterogeneous data is a challenge, but it's one that must be faced in order to easily share information internally and with outside partners. The integration challenge is one reason why NASA and many organizations are turning to service-oriented architectures combined with semantic integration. SOA consists of services that offer interoperability capabilities built into the network. While SOA's business-centric approach has sparked enthusiasm, the challenge is now to build in inference capabilities to make intelligent and dynamic selection of Web services possible. This is where semantic technology -- the modeling of an area of knowledge like biology or economics as close to natural language as possible -- comes into play.
While SOA's business-centric approach has sparked enthusiasm, the challenge is now to build in inference capabilities to make intelligent and dynamic selection of Web services possible. This is where semantic technology -- the modeling of an area of knowledge like biology or economics as close to natural language as possible -- comes into play.
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