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With the increasing popularity of in-memory computing, Spark [1] has been highly successful in implementing large scale data intensive applications, especially for those that reuse data across multiple parallel operations. However due to the fact that Moore's Law has slowed down and memory resources are still costly, we presented an elastic data persisting solution for Spark, which enables data compression...
A fine-grain scalable and efficient audio compression scheme based on adaptive significance-trees is presented. Common approaches for 2-D image compression like EZW (embedded wavelet zero tree) and SPIHT (set partitioning in hierarchical trees) use a fixed significance-tree that captures well the inter- and intraband correlations of wavelet coefficients. For 1-D audio signals, such rigid coefficient...
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