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Variational autoencoders for 3D data processing
Variational autoencoders (VAEs) play an important role in high-dimensional data generation based on their ability to fuse the stochastic data...
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How natural language processing derived techniques are used on biological data: a systematic review
The decoding of the human genome, completed two decades ago, marked a revolutionary moment in biology by introducing a vast amount of data. This...
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Integrated method for distributed processing of large XML data
The traditional standalone computing approach is difficult to handle the task of processing large XML data due to scalability, thus distributed...
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HXPY: A High-Performance Data Processing Package for Financial Time-Series Data
A tremendous amount of data has been generated by global financial markets everyday, and such time-series data needs to be analyzed in real time to...
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Data Processing and Transformation
In this chapter, we will explore the domain of data manipulation and alteration, centering our attention on Google Cloud Dataflow and Google Cloud...
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Attractor Properties of Spatiotemporal Memory in Effective Sequence Processing Task
AbstractFor autonomous AI systems, it is important to process spatiotemporal information to encode and memorize it and extract and reuse abstractions...
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3D Point Cloud Data and Processing
3D point cloud data can be obtained by laser scanning or photogrammetry and can also be seen as a representation of 3D digitization of the physical...
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A distributed B+Tree indexing method for processing range queries over streaming data
A data stream exhibits as a massive unbounded sequence of data elements continuously generated at a high rate. Stream databases raise new challenges...
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An improved self-attention for long-sequence time-series data forecasting with missing values
Long-sequence time-series data forecasting based on deep learning has been applied in many practical scenarios. However, the time-series data...
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Processing Data in the Background
Processing data can be quite time-consuming; for instance, when a file is uploaded to the database, and it needs to be parsed and loaded into several...
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Sequence Modeling Based Data Augmentation for Micro-expression Recognition
Micro-expressions (MEs) can reveal people’s true emotions and expose deceitful behaviors. With the introduction of deep learning, the accuracy of...
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The flip-flop neuron: a memory efficient alternative for solving challenging sequence processing and decision-making problems
Sequential decision-making tasks that require information integration over extended durations of time are challenging for several reasons, including...
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StreamFilter: a framework for distributed processing of range queries over streaming data with fine-grained access control
Access control is a fundamental component of any data management system, ensuring the prevention of unauthorized data access. Within the realm of...
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Distributed processing of spatiotemporal ocean data: a survey
Ocean data exhibits interesting yet human critical features affecting all creatures around the world. Studies on Hydrology and Oceanology become the...
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Edge Computing with Fog-cloud for Heart Data Processing using Particle Swarm Optimized Deep Learning Technique
Chronic illnesses such as heart disease, diabetes, cancer, and respiratory diseases are complex and pose a significant threat to global health....
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Parallel and distributed processing for high resolution agricultural tomography based on big data
In the field of high-resolution tomography, there is currently a notable increase in the volume of tomographic projections and data produced. Such a...
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Image processing-based protection of privacy data in cloud using NTRU algorithm
The recent and fast improvements in communication networks and digital technologies have facilitated the easy transmission and storage of multimedia...
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Usage of biorthogonal wavelet filtering algorithm in data processing of biomedical images
Though bi-orthogonal filtering (BWF) algorithm has been applied in image processing, the image after processing still has the phenomenon of edge...
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Tempura: a general cost-based optimizer framework for incremental data processing (Journal Version)
Incremental processing is widely adopted in many applications, ranging from incremental view maintenance, stream computing, to recently emerging...
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Minimum Epsilon-Kernel Computation for Large-Scale Data Processing
Kernel is a kind of data summary which is elaborately extracted from a large dataset. Given a problem, the solution obtained from the kernel is an...