A balanced decomposition approach to real-time visualization of large vector maps in CyberGIS

To evaluate if real-time dose visualisation during computed tomography (CT)-guided interventions leads to a reduction in radiation dose to participating radiologists. The individual radiation dose radiologists are exposed to during CT interventions was measured using dedicated dosimeters (RaySafe i2-system, Unfors RaySafe GmbH, Billdal, Sweden) worn over the usual radiation protective apron. Initially, only the total radiation dose was measured, without visualisation (control group). In the following study period, the radiation dose was shown to participants on a live screen in real-time (experimental group). In both groups, the dose was recorded in 1-second intervals. The results collected were evaluated by comparison using descriptive statistics and mixed-effect models. In particular, the variables experience, gender, role, and position during the intervention were analysed. In total, 517 measurements of 304 interventions (n=249 with and n=268 without live screen) performed by 29 radiologists acting as interventionalists or assistants were analysed. All CT-guided interventions were performed percutaneously, the majority of which (n=280) were microwave ablations (MWA). Radiation doses in the group without visualisation were comparable with usual dose rates for the corresponding intervention type. The mean total radiation dose was reduced by 58.1% (11.6 versus 4.86 μSv) in the experimental group (p=0.034). The highest reduction of 78.5% (15.55 versus 3.35 μSv) was observed in radiologists with the role of assistant (p=0.002). Sub-analysis showed significant dose reduction (p<0.0001) for the use of live screen in general; considering all variables, the role "assistant" alone had a statistically significant influence (p=0.002). The real-time visualisation of active radiation dose during CT interventions leads to a relevant reduction in radiation dose to participating radiologists.

Perceptual encryption with compression for secure vector map data processing

This paper presents a heart disease prediction model. Among the recent technology, internet of things-enabled healthcare plays a vital role. The medical sensors used in healthcare provide a huge volume of medical data in a continuous manner. The speed of data generation in IoT healthcare is high so the volume of data is also high. In order to overcome this problem, the proposed model is a novel three-step process to store and analyze the large volumes of data. The first step focuses on a collection of data from sensor devices. In Step 2, HBase has been used to store the large volume of medical sensor data from a wearable device to the cloud. Step 3 uses Mahout for devolving logistic regression-based prediction model. At last, ROC curve is used to find the parameters that cause heart disease.

The rapid increase in the number of aerial and orbital Earth observation systems is generating a huge amount of remote sensing data that need to be readily transformed into useful information for policy and decision makers. This exposes an urgent demand for image interpretation tools that can deal efficiently with very large volumes of data. In this work, we introduce a set of methods that support distributed processing of georeferenced raster and vector data in a computer cluster, which may be a virtual cluster provided by cloud computing infrastructure services. The set of methods comprise a particular technique for indexing distributed georeferenced datasets, as well as strategies for distributing efficiently the processing of spatial context-aware operations. They provide the means for the development of scalable applications, capable of processing large volumes of geospatial data. We evaluated the proposed methods in a remote sensing image interpretation application, built on the MapReduce framework, and executed in a cloud computing infrastructure. The experimental results corroborate the capacity of the methods to support efficient handling of very large earth observation datasets.

Privacy preserving multi-party computation delegation for deep learning in cloud computing

Marine data acquisition and processing has generated new concepts in velocity analysis, amplitude analysis, and frequency analysis. This has been possible because of consistent surface conditions on marine data, development of direct detection of hydrocarbon concepts, and improved computing power. Exploitation of the correlation of time, amplitude, velocity, and frequency relations has been largely confined to time, amplitude, and velocity. Such basic seismic theories as dispersion, fluidity, and multiple energy indicate a strong relation between frequency and velocity. Experiments on synthetic data illustrates an additional interdependence between apparent velocity and frequency. Both the theoretical assumptions and the synthetic exercises are reenforced by experiments on seismic data. Overlays of continuous velocity analysis and continuous frequency analysis show a substantial correlation. Frequency analysis in a seismic section dimensionally compatible contour allows the correlation with velocity on large volumes of data. An example on Gulf Coast data indicates good potential for this type of analysis. INTRODUCTION The advent of marine seismic recording in conjunction with increased energy needs resulted in a very large volume of seismic data. It was also marine data which generated improved methods of rock velocity estimation. Direct detection of hydrocarbons motivated attention toward such neglected seismic observations as phase and frequency anomalies. Time relations continue to be the basis of interpretation of structure but stratigraphic exploration and direct detection of hydrocarbons brought amplitude to the fore as a meaningful and interpretable measure. Amplitude, while difficult on land data, is reasonably consistent on marine data due to the relatively steady surface conditions at sea. Analysis of both velocity and frequency began to receive more attention for the same reason. Interpretation techniques on seismic data broadened to include frequency and velocity considerations. Time, amplitude, velocity, and frequency are all directly related to the earth response. Anomalies in the seismic data usually will create anomalies in some of these basic parameters so that they are at least indirectly related to each other. Some formulas relating these measures are well known and much utilized. The best known such equation is perhaps the "moveout" equation directly relating time and velocity: (Available In Full Paper) The relation between frequency and velocity or frequency and amplitude is also direct but seldom used in data processing. However, any discussion of compressional wave theory must include frequency as a very basic parameter. Some existing seismic theory does relate velocity and frequency but actual measurement on real data is scant. It is proposed to consider their possible relationship in the context of geophysical theory and practical observation. VELOCITY DISPERSION One accepted theory, which directly relates frequency and velocity is dispersion. Residual attenuation of the seismic wavefront after distance and reflection factors are removed is proportional to frequency. The equation which governs this dependence of velocity on frequency according to O'Brien and Lucas, 1971, is: (Available In Full Paper) It is indicated by the equation that if the attenuation, Q, is known, the velocity at each frequency could be computed.

Chapter 21 - Big Data Integration

The rapid development of computer technologies has significantly impacted various sectors, including healthcare. The ability to collect, process, and visualize medical data in real time is becoming increasingly important, especially for managing chronic conditions such as epilepsy. This paper presents a web-based application designed for real-time monitoring of health indicators, enabling healthcare professionals to track patient data efficiently. The system automates the process of collecting data from fitness trackers, transmitting it via a mobile device to a server, and visualizing it in a web application. Its architecture employs a thin-client model with Node.js for backend logic and React.js for the user interface, ensuring scalability and responsiveness. Key features include real-time data visualization, historical trend analysis, and the ability to export health metrics for further examination. The system architecture follows a modular approach, with a clear separation of concerns between the client-side, server-side, and database components. MongoDB is used as the database provider, offering flexibility in handling large volumes of health data. The system underwent extensive testing in two stages. During the first stage, real-world data collection demonstrated an average data transmission time of less than 112 ms, ensuring compliance with real-time requirements. In the second stage, stress testing with up to 100 simultaneous users showed an average server response time of 145.8 ms and a 95th percentile response time of 167.1 ms. These results confirm the system’s robustness and suitability for deployment in medical facilities. Future work aims to enhance the system by incorporating advanced real-time alert mechanisms and additional health metrics, such as oxygen saturation and activity levels, to provide comprehensive monitoring. The presented solution showcases the potential of integrating modern web technologies into healthcare, contributing to improved patient outcomes and more efficient workflows for medical professionals.

This study presents the design, development, and deployment of a high-performance Internet of Things (IoT) monitoring system featuring an integrated Redis-based in-memory processing architecture. The system collects real-time environmental data—including temperature, humidity, and pressure—from field-deployed TI CC1352R SensorTag devices operating within an LR-WPAN network. These measurements are forwarded to a TI CC1352R LaunchPad border router node, which employs an ESP32 Wi-Fi SoC interface to transmit the sensed data to cloud-based web services via the global Internet. The web service dynamically caches and stores incoming traffic according to database write speed and network intensity, while the Redis caching layer significantly enhances data ingestion and monitoring response times by reducing database load. The backend follows modular and scalable CI/CD pipeline principles, automating the testing, building, and deployment phases. Except for the firmware running on sensor nodes, every component of the system is triggered by version-controlled merges, ensuring automated testing and re-deployment. One of the main challenges in complex IoT systems—especially those requiring continuous weekly feature updates (sprint-based development)—is constructing a fully integrated, end-to-end system that remains stable and extensible. Experimental findings demonstrate rapid recovery from fault conditions (with total re-deployment times below 10 minutes), efficient real-time data visualization, and improved system resilience. The proposed architecture also provides practical guidance for researchers focusing on IoT systems that traditionally rely on simulations by offering a fully operational, end-to-end technological solution. By caching large volumes of sensor data before disk storage, the system overcomes one of the major scalability bottlenecks in growing IoT networks. On a 12 GB RAM server, the architecture successfully stores data from approximately 530,000 sensor messages using a 10-second caching interval, effectively absorbing sudden data bursts through Redis buffering. Consequently, the study establishes a robust foundation for future extensions involving machine learning integration and large-scale sensor deployments.

Computationally demanding tasks are typically calculated in dedicated data centers, and real-time visualizations also follow this trend. Some rendering tasks, however, require the highest level of confidentiality so that no other party, besides the owner, can read or see the sensitive data. Here we present a direct volume rendering approach that performs volume rendering directly on encrypted volume data by using the homomorphic Paillier encryption algorithm. This approach ensures that the volume data and rendered image are uninterpretable to the rendering server. Our volume rendering pipeline introduces novel approaches for encrypted-data compositing, interpolation, and opacity modulation, as well as simple transfer function design, where each of these routines maintains the highest level of privacy. We present performance and memory overhead analysis that is associated with our privacy-preserving scheme. Our approach is open and secure by design, as opposed to secure through obscurity. Owners of the data only have to keep their secure key confidential to guarantee the privacy of their volume data and the rendered images. Our work is, to our knowledge, the first privacy-preserving remote volume-rendering approach that does not require that any server involved be trustworthy; even in cases when the server is compromised, no sensitive data will be leaked to a foreign party.

Text classification in medical domain could result in an easier way of handling large volumes of medical data. They can be segregated depending on the type of diseases, which can be determined by extracting the decisive key texts from the original document. Due to various nuances present in understanding language in general, a requirement of large volumes of text-based data is required for algorithms to learn patterns properly. The problem with existing systems such as MedScape, MedLinePlus, Wrappin, and MedHunt is that they involve human interaction and high time consumption in handling a large volume of data. By employing automation in this proposed field, the large involvement of manpower could be removed which in turn speeds up the process of classification of the medical documents by which the shortage of medical technicians in third world countries are addressed.

The aims of this study were to find out: (1) the effects of visual mapping on students’ critical thinking skills, (2) the effects of science-related attitudes on students’ critical thinking skills, (3) the interactions between visual mapping and science-related attitudes on students’ critical thinking skills, (4) the effects of visual mapping on students’ problem solving skills, (5) the effects of science-related attitudes on students’ problem solving skills, and (6) the interactions between visual mapping and science-related attitudes on students’ problem solving skills. This study was conducted at MAN 1 Tanjung Pura, totally 141 students. It was a quasi-experimental technique by using a pretest-posttest experimental group with 4x2 factorial design. The technique of data analysis was processed by the Two-Way ANOVA and followed by Duncan’s Multiple Range Test. The results showed that: (1) there were the significant effects of visual mapping on students’ critical thinking skills (F=87.082; P=0.000), (2) there were the significant effects of science-related attitudes on students’ critical thinking skills (F=2.493; P=0.040), (3) there were the interactions between visual mapping and science-related attitudes on students’ critical thinking skills (F=2.037; P=0.000), (4) there were the significant effects of visual mapping on students’ problem solving skills (F=94.214; P=0.000), (5) there were the significant effects of science-related attitudes on students’ problem solving skills (F=3.397; P=0.031), and (6) there were the interactions between visual mapping and science-related attitudes on students’ problem solving skills (F=2.195; P=0.000).

Machine learning algorithms are a natural fit for printing fully dense superior metallic parts since 3D printing embodies digital technology like no other manufacturing process. Since traditional machine learning needs a large volume of reliable historical data to optimize many printing variables, the algorithm is augmented with human intelligence derived from the rich knowledge base of metallurgy and physics-based models. The augmentation improves the computational efficiency and makes the problem tractable by enabling the algorithm to use a small set of data. We provide a verifiable quantitative index for achieving fully dense superior parts, facilitate material selection, uncover the hierarchy of important variables that affect the density, and present easy-to-use visual process maps. These findings can improve the quality consistency of 3D printed parts that now limit their greater industrial adaptation. The approach used here can be applied to solve other problems of 3D printing and beyond.

Large volumes of monitoring and logging data result from the operation of a large scale astrophysical observatory. In the last few years several "Big Data" technologies have been developed to deal with such volumes of data especially in the Internet of Things (IoT) framework. We present the logging, monitoring and alarm system architecture for the ASTRI Mini-Array aimed at supporting the analysis of scientific data and improving the operational activities of the telescope facility . A prototype was designed and built considering the latest software tools and concepts coming from Big Data and IoT and a particular relevance has been given in satisfying quality requirements such as performance, scalability and availability.

The increase in the growth of healthcare IoT devices leads to a large volume and variety of healthcare data. The large healthcare data generates high data traffic that will cause network bottleneck and burdens the cloud with an extra amount of workload. The main requirement of healthcare IoT is to have a minimum network and service latency along with conserve network bandwidth. Both the volume of data as well as factors related to internet connectivity may lead to high network latency in analyzing and acting upon device data. Therefore, due to high network latency, high service latency and large data transmission from IoT devices, the cloud is unable to send the patient health data in real-time mode. Fog computing is a new approach to address this requirement of cloud and healthcare IoT devices. A tentative novel solution for the above-mentioned problem using fuzzy-based fog computing architecture that integrates healthcare IoT devices with the cloud and uses fog services with Fuzzy Data Packet Allocation (FDPA) algorithm. Using virtualization and fuzzy inference system (FIS) in fog computing, the proposed algorithm is expected to transfer the patient health data in real-time mode to agencies and doctors.