Efficient Processing Capabilities Research Articles

Real-time data warehousing in the big data environment: A comprehensive review of implementation in the internet industry

A real-time data warehouse is a crucial tool for information management and analysis, enabling the capture, processing, and analysis of vast amounts of data from diverse sources in real-time. It offers enterprises enhanced decision support through its efficient processing capabilities and timely data feedback. This paper reviews the technical characteristics and application scenarios of real-time data warehouses, with a particular focus on the Internet sector. It explores the evolution from traditional data warehouses to modern data lake and lakehouse architectures, emphasizing the advancements in data processing capabilities, including the separation of storage and compute functions. Real-time data warehouses, which enable immediate data processing and feedback, are essential for enterprises requiring up-to-the-minute insights. The study compares the Lambda and Kappa architectures, detailing their strengths and weaknesses in terms of data throughput, latency, and scalability. Innovations such as Apache Hudi and lakehouse architectures offer new opportunities for performance optimization and functional expansion. The emergence of hybrid architectures like HTAP (Hybrid Transactional/Analytical Processing) and HSAP (Hybrid Serving/Analytical Processing) represents a significant advancement in integrating transactional and analytical processing. Future research should focus on the impact of artificial intelligence and machine learning on real-time data warehouses to enhance their analytical and predictive capabilities, reduce complexity, and lower operational costs.

ImageDTA: A Simple Model for Drug-Target Binding Affinity Prediction.

Predicting the drug-target binding affinity (DTA) is crucial in drug discovery, and an increasing number of researchers are using artificial intelligence techniques to make such predictions. Many effective deep neural network prediction models have been proposed. However, current methods need improvement in accuracy, complexity, and efficiency. In this study, we propose a method based on a multiscale 2-dimensional convolutional neural network (CNN), namely ImageDTA. Many studies have shown that CNN achieves good learning effects with limited data. Therefore, we take a unique perspective by treating the word vector encoded with a simplified molecular input line entry system (SMILES) string as an "image" and processing it like handling images, fully leveraging the efficient processing capabilities of CNN for image data. Furthermore, we show that ImageDTA has higher training and inference efficiency than pretrained large models and outperforms attention-based graph neural network models in accuracy and interpretability. We also use visualization techniques to select appropriate convolutional kernel sizes, thereby increasing the network's interpretability.

Digital intelligence driven and elemental energy cultivated together: Teaching application and method innovation based on artificial intelligence

With the rapid development of information technology, artificial intelligence technology is gradually being introduced in the field of education to achieve more efficient and personalized teaching. The educational concept of combining intelligence with numbers and cultivating abilities with all elements emerged in this context, aiming to promote the development and reform of education through the deep integration of artificial intelligence and education. The combination of digital and intelligent technologies emphasizes the dual role of digitalization and intelligence in the field of education, which promotes the quality and effectiveness of education through mutual promotion of digital means and intelligent technologies. Digitalization can provide education with massive data resources and efficient processing capabilities, while intelligence can achieve precise identification and satisfaction of students' personalized needs based on these data and resources. The joint cultivation of elements and abilities emphasizes the joint cultivation of literacy and abilities, that is, in the education process, not only should we focus on imparting knowledge, but also on cultivating students' literacy and abilities. Literacy includes information literacy, innovation literacy, humanistic literacy, etc., while abilities include learning ability and practical ability. Through the co cultivation of elements and abilities, students can better adapt to the development and changes of future society.

Digital intelligence driven and elemental energy cultivated together: Teaching application and method innovation based on artificial intelligence

With the rapid development of information technology, artificial intelligence technology is gradually being introduced in the field of education to achieve more efficient and personalized teaching. The educational concept of combining intelligence with numbers and cultivating abilities with all elements emerged in this context, aiming to promote the development and reform of education through the deep integration of artificial intelligence and education. The combination of digital and intelligent technologies emphasizes the dual role of digitalization and intelligence in the field of education, which promotes the quality and effectiveness of education through mutual promotion of digital means and intelligent technologies. Digitalization can provide education with massive data resources and efficient processing capabilities, while intelligence can achieve precise identification and satisfaction of students' personalized needs based on these data and resources. The joint cultivation of elements and abilities emphasizes the joint cultivation of literacy and abilities, that is, in the education process, not only should we focus on imparting knowledge, but also on cultivating students' literacy and abilities. Literacy includes information literacy, innovation literacy, humanistic literacy, etc., while abilities include learning ability and practical ability. Through the co cultivation of elements and abilities, students can better adapt to the development and changes of future society.

Expectation-Maximization-Based Simultaneous Localization and Mapping for Millimeter-Wave Communication Systems.

In this paper, we proposed a novel expectation–maximization-based simultaneous localization and mapping (SLAM) algorithm for millimeter-wave (mmW) communication systems. By fully exploiting the geometric relationship among the access point (AP) positions, the angle difference of arrival (ADOA) from the APs and the mobile terminal (MT) position, and regarding the MT positions as the latent variable of the AP positions, the proposed algorithm first reformulates the SLAM problem as the maximum likelihood joint estimation over both the AP positions and the MT positions in a latent variable model. Then, it employs a feasible stochastic approximation expectation–maximization (EM) method to estimate the AP positions. Specifically, the stochastic Monte Carlo approximation is employed to obtain the intractable expectation of the MT positions’ posterior probability in the E-step, and the gradient descent-based optimization is used as a viable substitute for estimating the high-dimensional AP positions in the M-step. Further, it estimates the MT positions and constructs the indoor map based on the estimated AP topology. Due to the efficient processing capability of the stochastic approximation EM method and taking full advantage of the abundant spatial information in the crowd-sourcing ADOA data, the proposed method can achieve a better positioning and mapping performance than the existing geometry-based mmW SLAM method, which usually has to compromise between the computation complexity and the estimation performance. The simulation results confirm the effectiveness of the proposed algorithm.

The network compute fabric – Advancing Digital Transformation with Ever-Present Service Continuity

In order to fully support emerging use cases such as the Internet of Senses [1], cyber-physical systems and connected intelligent machines [2], the foundation of future network platforms must be built on tight integration between reliable, deterministic connectivity and scalable, affordable and efficient processing capabilities. The result of this tight integration is what we call the network compute fabric.

Hybrid Optimization Model for Energy Efficient Cloud Assisted Wireless Sensor Network

The role of wireless sensor networks is ubiquitous in the present era. The dependency of wireless sensor networks is inevitable for small scale to large scale applications due to its compact, reliable, and efficient processing capabilities. However, wireless sensor network has its few limitations. Since the network is created by deploying sensor nodes and it requires efficient energy management procedures. Localization of nodes is an important process that should be considered in wireless sensor networks which directly relates the energy management. To reduce the node localization issues in wireless sensor networks, this research work proposed a hybrid optimization model using Particle Swarm Optimization and Grey Wolf Optimization as a combined approach. The proposed model effectively handles the node localization issues. To reduce the data processing and storage issues in wireless sensor networks, Cloud module is incorporated in the proposed model which improves the energy management features. Similarly, to transfer the data from node to cloud, hybrid optimization model shortest path discovery process is utilized. This combined approach reduces the packet loss, avoids route failures, improves network reliability, and lifetime compared to conventional models such as ant colony optimization.

Online handwriting recognition systems for Indic and non-Indic scripts: a review

Handwriting recognition is one of the challenging tasks in the area of pattern recognition and machine learning. Handwriting recognition has two flavors, namely, Offline Handwriting Recognition and Online Handwriting Recognition. Though, saturation level has been achieved in machine printed (Offline) character recognition. Presently, due to dramatical development in IT sector, touch-based devices are available in the market with efficient processing capabilities. With this revolution, research in the area of handwriting recognition has become more popular in real-time (Online) mode. In this paper, a comprehensive review has been reported for online handwriting recognition of non-Indic and Indic scripts. The six non-Indic-scripts and eight Indic script namely, Arabic, Chinese, Japanese, Persian, Roman, Thai, and, Assamese, Bangla, Devanagari, Gurmukhi, Kannada, Malayalam, Tamil, Telugu, respectively have been considered in this article. This study comprises introduction of online handwriting recognition process, various challenges, motivations, feature extraction, and classification methodologies, used for recognizing the various scripting languages. Moreover, an effort has been made to provide the list of publicly available online handwritten dataset for various scripting languages. This study also provides the recognition and beneficial assistance to the novice researchers in field of handwriting recognition by providing a nut shell studies of various feature extraction strategies and classification techniques, used for the recognition of both Indic and non-Indic scripts.

Real-time automated video highlight generation with dual-stream hierarchical growing self-organizing maps

Video has rapidly become one of the most common sources of visual information transfer. The number of videos uploaded to YouTube in a single day is estimated to take over 82 years to watch. Automated tools and techniques for analyzing and understanding video content, thus, have become an essential requirement. This paper addresses the problem of video highlight generation for large video files. We propose a novel skimming-based unsupervised video highlight generation method utilizing statistical image processing and data clustering, which process frame-level static and dynamic features of input video in two streams. The dynamic feature stream is represented by computing a dense optical flow for each consecutive frame, providing instantaneous velocity information for every pixel, which is then characterized by a per-frame orientation histogram, weighted by the norm, with orientations quantized. To process multi-scene videos, we utilize the divisive hierarchical clustering capability of growing self-organizing map (GSOM) using a dual-step top-down hierarchical approach in which the first level consists of clustering of spatial and temporal features of the video and in the second level, each parent cluster is hierarchically subdivided into child clusters using GSOM. The video highlight generation process is conducted real time by evaluating segments of video snippets based on a pre-defined time interval. We demonstrate the accuracy, robustness and the quality of highlights generated using a qualitative analysis conducted using 1625 human experts on highlights generated from two datasets. Further, we conduct a runtime analysis to demonstrate the efficient processing capability of the proposed method, to be used in real-time settings.

A survey on multipliers, adders and adiabatic logic styles suitable for power reduction

Low power circuits and designs are the need of the hour as they find applications in electronic components which have power efficient processing capabilities. High speed processors are power consuming and multipliers contribute to a maximum extent for this power consumption. Thus, to achieve low power designs, adiabatic logic is one of the noted technologies in this regard. Adiabatic logic can be implemented for different types of circuit designs and this paper concentrates on a survey of various multipliers and adders that can be modified into a low power multiplier using adiabatic logic.

Building an Earth Observations Data Cube: lessons learned from the Swiss Data Cube (SDC) on generating Analysis Ready Data (ARD)

Pressures on natural resources are increasing and a number of challenges need to be overcome to meet the needs of a growing population in a period of environmental variability. Some of these environmental issues can be monitored using remotely sensed Earth Observations (EO) data that are increasingly available from a number of freely and openly accessible repositories. However, the full information potential of EO data has not been yet realized. They remain still underutilized mainly because of their complexity, increasing volume, and the lack of efficient processing capabilities. EO Data Cubes (DC) are a new paradigm aiming to realize the full potential of EO data by lowering the barriers caused by these Big data challenges and providing access to large spatio-temporal data in an analysis ready form. Systematic and regular provision of Analysis Ready Data (ARD) will significantly reduce the burden on EO data users. Nevertheless, ARD are not commonly produced by data providers and therefore getting uniform and consistent ARD remains a challenging task. This paper presents an approach to enable rapid data access and pre-processing to generate ARD using interoperable services chains. The approach has been tested and validated generating Landsat ARD while building the Swiss Data Cube.