Improvement of Healthcare Services using Metadata
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In today's fast growing world we have analyzed distributed data sources publishing up to gigabytes of data each day, accumulating over the period of several months to the terabyte scale. This raises the challenge of how to efficiently store these distributed datasets, both in working caches for fast real-time access and archived forms which can be rein satiated for offline data analysis. In this paper, we have presented the processing services need to access several datasets at once to produce intelligent data fusion results, which are subsequently made available to decision makers in real-tim...
In today's fast growing world we have analyzed distributed data sources publishing up to gigabytes of data each day, accumulating over the period of several months to the terabyte scale. This raises the challenge of how to efficiently store these distributed datasets, both in working caches for fast real-time access and archived forms which can be rein satiated for offline data analysis. In this paper, we have presented the processing services need to access several datasets at once to produce intelligent data fusion results, which are subsequently made available to decision makers in real-time. Since it is challenging to analyze all the results efficiently we have to make a solution of making the processing faster and more efficient. Here we are designing a method using Metatags to reduce the processing time and load of the existing systems. The metatags basically define the various attributes of data files and provide us options to access the files on the basis of selecting attributes. In proposed system light weight and heavy weight semantic is being separated on the basis of size. Above 10 are being added to heavy weight list and below 10 are added to light weight list.
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