Semantic Technologies

imageI am always intrigued when reading about Smarter IT solutions that help solve real business challenges, especially the ones that are related with the well-being of our planet. One such solution was described in the recently published IBM Redpaper, REDP-5081 and the associated IBM Redbooks Solution Guide Smarter Environmental Analytics Solutions: Offshore Oil and Gas Installations example, TIPS-11131.

The solution described in these documents is an IBM Environmental Analytics solution customized for a specific environmental monitoring use case for oil and gas industries. The solution implements real-time monitoring of environmental data in order to facilitate early detection of, and response to operational events surrounding offshore oil and gas installations, and enables oil companies to implement responsible production operations without inflicting harm to the environment. The solution is built on the semantic framework that enables operational business intelligence for various “smarter solutions".

Here are 5 interesting facts about the semantic technology. Each of these areas is discussed in greater detail in these documents:

1. Semantic technologies provide an abstraction layer above existing IT technologies that connects data, content, and processes

Semantic technologies do this by additional contextual information to some existing data set. An example would be adding traffic and weather data to road maps such as the road conditions, weather information, probability of delays, condition of road, status of constructions, and road hazards. This additional data can be used by “smarter automobiles” to make decisions that minimize the risk of accidents.

2. Semantic technology provides a flexible integration approach

Semantic technologies offer better integration and interoperability of diverse information assets than the traditional IT systems and can easily accommodate changes with no need to re-do the schema. Adding, changing and implementing new relationships is easy with semantic modelling, since unlike the traditional technologies, the meanings and relationships are not predefined and “hard wired” into data formats and the application program code at design time. Because of this traditional relational databases are inflexible and fragile when the nature of the world changes, and thus require constant re-architecting in the face of new knowledge or new relationships.

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