In order to achieve the high power computations required for analyses of omics data, we utilize the emerging database system, NoSQL [ 37], which accelerates retrieval of large data from databases.
As an approximate similarity retrieval technique, LSH is aimed to accelerate the retrieval speed at the cost of slight accuracy decrease.
We develop a technique to accelerate data retrieval and have combined many graphical techniques in visualization.
Then, part layer code and CMFC content code for accelerating subpart retrieval are given to filter out unmatched subparts efficiently.
Thus, this emphasizes the importance of developing efficient methods for (semi-) automatic syllabus classification in order to accelerate syllabus retrieval.
Then the rapid filtering for accelerating subpart retrieval is presented to filter out infeasible subparts quickly under the requirements of NC process reuse.
Content popularity is often used as an important factor for accelerating content retrieval, while content diversity for increasing the types of contents cached locally means that even popular contents should not be cached in multiple local caching nodes.
Signal data retrieval is accelerated by a DMA-driven PCI Express™ x1 Interface to the ATCA system controller, providing an overall throughput in excess of 100 MB/s.
A vantage-based case indexing mechanism is developed to accelerate the case retrieval efficiency.
In our Skip-Octree, the idea of skip list is utilized to accelerate the data retrieval efficiency of Octree.
