Storage, Retrieval and Manipulation Techniques of Human Brain Data
Improving the ways to analyze, investigate and understand the Human Brain working from both macro and micro level
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Existing image based indexing, storage and retrieval techniques for human brain data are difficult to understand and work with, the limitations inherited by them such as inefficiency, cost, difficulty to use, incomplete and irrelevant data retrieval present many challenges to modern research community. With advancements of technology, it demands on the development of alternative ways to present the best form of brain data, and to efficiently access and use it. In order to reconcile these requirements, we worked on the Formal Conceptual Model of Human Brain. We defined its architecture and conc...
Existing image based indexing, storage and retrieval techniques for human brain data are difficult to understand and work with, the limitations inherited by them such as inefficiency, cost, difficulty to use, incomplete and irrelevant data retrieval present many challenges to modern research community. With advancements of technology, it demands on the development of alternative ways to present the best form of brain data, and to efficiently access and use it. In order to reconcile these requirements, we worked on the Formal Conceptual Model of Human Brain. We defined its architecture and concomitant storage, retrieval and manipulation techniques. We applied the most efficient available secondary indexing structure, B+ tree, to increase the performance of search and retrieval. We also extend the regular Structured Query Language (SQL) and define some operators specific to the proposed Model. In our opinion, the proposed techniques give new and effective ways to deal with human brain data. Also, the implementation of these techniques aimed to provide better support for scientists and researchers in carrying out their tasks in an efficient and easy manner.
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