Big dataClassificationClusteringCo-effective and adaptive neuro-fuzzy system (Co-EANFS)HadoopMapReducePredictionAn exponential rise has been observed in the data volume over the time when considering a real time environment. A phenomenal feature termed as `Predictability' helps in predicting and portraying r...doi:10.1007/s10916...
19,20]. Wavelet-based compression methods are effective in isolating the ROI but often result in higher computational complexity. CNNs, on the other hand, provide high compression rates but may not fully
Snappy is widely used in Google projects like Bigtable, MapReduce and in compressing data for Google's internal remote Procedure Call - RPC systems. Snappy for fast compression uses, zstd for read-intensive storage, and brotli if a user is waiting for the data or low-read-throughput storage...
Our purpose is to find the basic shape in images similar to Fig.2with data mining. This is exactly the starting point of soft compression whose basic component unit is the shape, representing an image by using both shapes and locations. Of course, this is merely a visual explanation for so...
data gigabytes exchanged between connected cars will increase even more. The flood of data like this creates a processing problem. To deal with it, both the architecture and data must become more complex. This is where multisensor fusion and data compression play a significant role in making ...
Big data workloads generate massive quantities of information that need to be sent across the network and stored on data servers. Learn how data compression can be useful in your applications, and learn about the Data Plane Development Kit compression API and how it can help deflate your data!
In ESWC, pages 170-184, 2013.A. K. Joshi, P. Hitzler, and G. Dong. Logical linked data compression. In The Semantic Web: Semantics and Big Data, pages 170-184. Springer, 2013.Joshi, A.K., Hitzler, P., Dong, G.: Logical linked data compression. In: The Se- mantic Web: ...
The exponential growth of various complex images is putting tremendous pressure on storage systems. Here, we propose a memristor-based storage system with an integrated near-storage in-memory computing-based convolutional autoencoder compression network
1.3.1 Source Coding and Data Compression In computer science and information theory, data compression or source coding is the process of encoding information with fewer bits than an unencoded representation would use based on specific encoding schemes. As with any communication, compressed data communi...
[11] used a rapid error-constrained lossy compression on aggregated data before transmission. They use a short error-bounded lossy compressor on the acquired data before transmission, considered the largest energy user in an IoT device. In a subsequent step, they reconstruct the transmitted data ...