Better Data Acquisition with More Articulate Database Designs

Environmental data and life science studies are heavily dependent on data acquisition systems and these in turn are becoming more noticeably dependent on well designed collection systems best represented by database systems. The more articulate these database systems become the more accurate and plentiful does knowledge in these areas rise.

The environment and life science data collected thus far in history is beginning to become richer in terms of metadata and mapping strategies for building more dynamic databases. These new inputs help design closer phraseologies matching the scientific, engineering and technical scenarios necessary to build each new generational model.

Computers have long ago outperformed the promises made by database applications that suggested we would be able to easily collect various real world signals and waveforms and easily extract critical information for subsequent use as a systematic controlling agent for dynamic expansion of knowledge acquisition.

But as our data acquisition has grown exponentially through better sensors and more elaborate instrumentation, our knowledge acquisition has not. That is, until maybe, now. The heart of the solution might be the database system concepts slowly emerging and their more articulate reach into semantic scenario measurement systems.

Sensors working in conjunction with instruments all tied to a data logger provide the measurement configuration. Data loggers are electronic devices that collect data over time or in a specific radius.

These immediate flat data files connected to data loggers house the raw sensor signals. Their readings are periodically transferred to simple but more secure databases which in turn contribute to a knowledge database management system as more data feeds into relational and hierarchical databases.

The Knowledge base grows as the feed into relational and hierarchical database management systems grow to provide level-tag assignments and assist in the eventual manipulation that will be necessary for successful extraction of meaningful real world data to help develop an insight in understanding the environment.

Through the use of a human interface and because the databases mentioned above are so well mapped with predetermined location and expectation tags and are strongly aided with relevant metadata, a system of data highlighted reports are produced that help suggest different kinds of conclusions both scientific technical for practical engineering purposes.

This process continues to evolve into more intricate patterns as database designs become more articulate in their abilities to store, compare, manipulate and report data.