Transforming Business with AI-Driven IoT Insights: Query, Process, and Visualize Data

This article delves into the communication quality monitoring system driven by big data under the C/S (client/server) architecture, aiming to provide a comprehensive, real-time, and efficient security monitoring solution for factory production and manufacturing environments. The system integrates LoRa wireless communication technology, big data processing and analysis technology, and innovative dynamic expansion algorithms to achieve real-time monitoring and rapid response to various safety hazards in the production environment. The core of the system lies in the construction of LoRa wireless communication network, which adopts an innovative umbrella shaped network structure to ensure stable and fast transmission of sensor monitoring data to the server. By using the STM32 development board with LoRa module, dynamic expansion of hardware integration modules has been achieved, enhancing the flexibility and scalability of the system. At the level of data transmission and processing, introducing Kafka distributed message queue as a data cache effectively alleviates the pressure of processing massive real-time data. Combining the Spark Streaming streaming data processing framework, a distributed data processing model was constructed to achieve real-time consumption and efficient parallel processing of messages in Kafka queues. At the same time, the designed dynamic extension algorithm model can automatically persist the data of new monitoring points, ensuring the system's rapid adaptation to data changes. In terms of data storage and visualization, a data persistence layer architecture for a big data platform has been constructed. Real time data, historical data, and log data are stored separately in Redis, MySQL, and HDFS systems through a data streaming model, improving data storage efficiency and system performance. Based on Tomcat network server and SSM architecture, a B/S structured web server visualization platform has been developed, which supports users to remotely query the security status of the production environment through computer or mobile browser, and receive alarm notifications in case of abnormalities. The system integration test results show that the communication quality monitoring system performs excellently in wireless communication quality, data processing speed, data storage efficiency, and data visualization, and can meet the complex monitoring needs of factory production and manufacturing environments, providing solid technical support for enterprise safety production. However, there is still room for improvement in real-time data collection loss rate, alarm function completeness, and mobile access experience. Future research will focus on optimizing data transmission software models, adding multi-channel alarm functions, and developing mobile apps to enhance user experience.

In a variety of disciplines, from biology and physics to computer science and the social sciences, computational topology has become a potent tool for data analysis and visualisation. This study explores computational topology's uses in data analysis and visualisation, showing its potential for revealing hidden structures and patterns in large, complicated datasets.The paper starts out by giving a general introduction of computational topology and outlining its core ideas and methods. The use of it for extracting important information from big, high-dimensional datasets is then explored in relation to data analysis. Computational topology allows for the discovery of topological properties such as holes, voids, and connection patterns by modelling data as topological structures, such as simplicial complexes or persistent homology diagrams.The study also explores the function of computational topology in data visualisation, highlighting its capacity to offer clear and insightful visual representations of challenging datasets. Computational topology enables the construction of simpler and aesthetically pleasing representations while preserving the fundamental topological properties of the data through methods like topological simplification and dimensionality reduction.The usefulness of computational topology in several application domains, such as genomics, image analysis, and network analysis, is demonstrated through a number of case studies. These instances show how computational topology can improve activities like data exploration, clustering, classification, and anomaly detection, resulting in fresh perceptions and learnings.This study emphasises the enormous potential of computational topology for applications in data processing and visualisation. Computational topology provides a new perspective that complements current approaches by utilising the inherent geometric and topological properties of data. This allows researchers and practitioners to better understand complex datasets and make decisions based on the knowledge gleaned from them.

This article addresses the urgency of improving hydrological monitoring and effective water resource management, which is particularly crucial for flood prevention and rapid emergency response.The problem lies in the need to develop a tool for efficient collection, processing, and visualization of water level data in the rivers of the Lviv region. The research objective is to develop and implement an interactive web application based on ArcGIS for visualizing and analyzing water level dynamics. This tool is designed to display the location of hydrological posts, provide access to their current status information, and analyze time series of hydrological data. The key feature of this approach is the integration and visualization of data from both automated and manual (gauge) posts, significantly expanding the cover-age of hydrological information.Based on the stated objectives, the geoinformation approach has been implemented for collecting and visualizing hydro-logical post data in the Lviv region using ArcGIS tools. The data source for this system comprises observations provided by the Basin Water Resources Management of the Western Bug and Sian Rivers. As a result, the interactive ArcGIS monitoring dash-board has been developed. It visualizes spatial and temporal water level data, integrating a web map with tabular time series data. This ensures automatic synchronization between selecting a post on the map and displaying dynamic charts and analytical tables. The web application facilitates comprehensive analysis of hydrological data.The proposed solution significantly enhances hydrological monitoring and supports managerial decision-making regarding flood prevention and emergency response. Its practical value lies in its application for water resource management and flood risk assessment, including the visualization of indicators and access to operational information for specialists and au-thorities. Future research will focus on real-time data updates, integration with meteorological indicators, and development of models for forecasting water levels and potential flood zones.

Effective fiscal control and monitoring of public management are critical for preventing and mitigating corruption, which in turn, enhances government performance and benefits citizens. Given the vast amounts of data involved in government operations, applying advanced data analysis methods is essential for strengthening fiscal oversight. This paper explores data management strategies aimed at enhancing fiscal control, beginning with a bibliometric study to underscore the relevance of this research. The study reviews existing data capture techniques that facilitate fiscal oversight, addresses the challenges of data storage in terms of its nature and the potential for contributing to this goal, and discusses data processing methods that yield actionable insights for analysis and decision-making. Additionally, the paper deals with data interoperability, emphasizing the importance of these practices in ensuring accurate and reliable analysis, especially given the diversity and volume of data within government operations. Data visualization is highlighted as a crucial component, enabling the detection of anomalies and promoting informed decision-making through clear and effective visual representations. The research concludes with a case study on the modernization of fiscal control in Colombia, focusing on the identification of user requirements for various data-related processes. This study provides valuable insights for modern audit and fiscal control entities, emphasizing that data capture, storage, processing, interoperability, and visualization are integral to the effective supervision of public expenditure. By ensuring that public funds are managed with transparency, accountability, and efficiency, the research advances the literature by addressing both the technological aspects of data management and the essential process improvements and human factors required for successful implementation.

Nowadays, with the continuous development of big data technology, various industries use big data technology to process and mine massive data, and realize the value of data efficiently. In terms of air quality data processing, big data technology can also play a certain advantage. The platform is based on big data technology to design an air quality data analysis and prediction platform including data layer, business layer, interaction layer and visualization platform. Data is cleaned, calibrated, and stored in the data layer to ensure data consistency, integrity, and security. The air quality data is analyzed and predicted at the business layer. The interaction layer includes the functions of algorithm management, data query, and the data visualization platform provides intuitive information display. This design is a significant application for fully exploiting environmental data information. It has powerful data processing functions and scalability, which is a reliable data analysis and prediction platform.

The rise of the digital economy is shaping the future of business and society. In this digital era, data collection and analysis have become crucial to support decision-making, innovation, and efficiency improvement. Cloud computing and the Internet of Things technology, as the core driving forces of the digital economy, are opening new possibilities and providing strong support for data collection and analysis platforms. To broaden the scope of network big data applications, improve the accuracy of network data storage and management to a greater extent, and reduce the time for network data processing and control, a research method for network big data based on cloud computing and the Internet of Things in the context of the digital economy is proposed. The author first uses hierarchical network coding to transmit network data, based on the transmitted data, the CRC algorithm is used to calculate network data, and then the data is stored in a group storage manner, secondly, the high-precision query of network data is carried out using the hierarchical reverse stacking positioning method, thereby completing the research on network big data. Finally, to demonstrate the overall performance of network big data research methods based on cloud computing and the Internet of Things, a simulation experiment is conducted. The experimental results show that when the data query parameter ε is 8, the data query accuracy curve is unstable, and the query accuracy is low. When the data query parameter ε is 4-5, the data query accuracy curve is relatively flat, and the query accuracy is high. The method proposed by the author can comprehensively and concretely study network big data, improve data processing accuracy and network data calculation speed, increase network data storage capacity and query efficiency, reduce the loss rate of network data during operation, expand the operating range of network data, and provide a strong basis for subsequent research on network big data.

<p>Cultural heritage monuments, that were created by mankind for centuries are scattered throughout the world. Most of them are experiencing impacts coming from nature and humans each year that result damage and changing their common state. Many of the monuments are facing the critical condition and require diagnostics, study and planning and management of conservation/rehabilitation works. Due to impact of environmental factors such as temperature, humidity, precipitation, existence of complex structure of cracks, infiltrated water and runoff water streams, together with active tectonics in the region, Uplistsikhe rock cut city monument located in central part of Georgia faces problems and permanent destruction.</p><p>In parallel with technological development, it is now possible to conduct complex monitoring of the environment parameters in real time using sensor systems, data acquisition, communication network, data visualization and processing methods.</p><p>Complex approach with equipment used in research and experiment was used to study various factors affecting the monument and presents the very important issue. Gained experience, research methodologies and technical skills will be good basement for future study/research projects on similar monuments as the collected information gives us an in depth understanding of processes that impact on the monument and can then be followed by a coherent plan of risk reduction to increase the effectiveness of the used solutions. </p><p>During the study of cultural heritage monuments, there are some technical limitations that can occur: The electricity or communication wiring might not be available on the site or the wiring is impossible without damaging the monument itself.  So, there is a rising need of low power wireless sensor acquisition and transmission systems.</p><p>Paper discusses the usage of IoT based sensor systems for study of Uplistsikhe Cultural heritage monument. The built system uses low power data transmission network based on LoRa standard.</p><p>Measurement points were selected where several parameters (Temperature, Humidity, Crack meter value) are acquired and sent to the central information platform using so called Internet of things.</p><p>Central web-built system that is based on open platform Grafana is responsible for the data storage, visualization, processing and alarm generation.</p><p>The statistical processing of acquired collected data resulted in calculation of the parameter ranges. Calculations were made on the 24-hour data of each day to calculate the variations. First the maximal and minimal recorded values were identified, then the difference between maximums and minimums were calculated. Additionally, the mean values and the standard deviations were calculated resulting to the ranges considered as normal, excessive, dangerous and critical parameters.</p><p>These parameters were integrated into the web-system. In case of dangerous and critical parameters the system is able to distribute alarm state information via several channels such as email, chat message, SMS or other means.</p><p>As a result, the low power wireless system sensor measurement system was created that sends acquired data it to the cloud-based web platform with possibility of the data processing and issuing alarms.</p><p>This work was supported by Shota Rustaveli National Science Foundation of Georgia (SRNSFG) [Grant Number fr17_90]</p>

BackgroundPedigree data (family history) are indispensable for genetics studies and the assessment of individuals' disease susceptibility. With the popularity of genetics testing, the collection of pedigree data is becoming more common. However, it can be time-consuming, laborious, and tedious for clinicians to investigate all pedigree data for each patient. A self-service robot could inquire about patients' family history in place of professional clinicians or genetic counselors.ObjectiveThe aim of this study was to develop a mobile-based and self-service tool to collect and visualize pedigree data, not only for professionals but also for those who know little about genetics.MethodsThere are 4 main aspects in the iPed construction, including interface building, data processing, data storage, and data visualization. The user interface was built using HTML, JavaScript libraries, and Cascading Style Sheets (version 3; Daniel Eden). Processing of the submitted data is carried out by PHP programming language. MySQL is used to document and manage the pedigree data. PHP calls the R script to accomplish the visualization.ResultsiPed is freely available to all users through the iPed website. No software is required to be installed, no pedigree files need to be prepared, and no knowledge of genetics or programs is required. The users can easily complete their pedigree data collection and visualization on their own and through a dialogue with iPed. Meanwhile, iPed provides a database that stores all users’ information. Therefore, when the users need to construct new pedigree trees for other genetic traits or modify the pedigree trees that have already been created, unnecessary duplication of operations can be avoided.ConclusionsiPed is a mobile-based and self-service tool that could be used by both professionals and nonprofessionals at any time and from any place. It reduces the amount of time required to collect, manage, and visualize pedigree data.

The article describes the modern escalation of “big” and open data; situations where sociologist needs new competences such as coding and programming are analyzed. The author puts attention to two key research strategies when dealing with big data: self-localization and dipping into data. The first strategy refers to the researcher’s focus on traditional sociological analysis restricted by small sample size; the second one implies maximal use of the growing open source data. The challenges a researcher has to face when dealing with big data are as follows: (1) data extraction; (2) data processing and improvements; (3) data visualization. This is why a researcher should master coding and programming. Sociologists with humanitarian education should choose R statistical environment and Python programming language becasue they have simple syntax and allow solving complicated data processing, analysis and visualization tasks. The article provides examples of scripts as a demonstration of the possibilities of the R and Python programming languages.

Big data is one of the important development direction of modern information technology, realizing the sharing and analysis of large data will bring immeasurable economic value, but also has a tremendous role in promoting the social. In the age of big data, unified the data representation, large data processing, query, analysis and visualization are the key problem to be solved urgently. In order to provide a standardized framework construction of the large data service platform, this paper designed a large data service oriented architecture user experience. Secondly, in the aspect of data model, in order to achieve high data service for non structured data, the design of the non structured data model based on subject behavior. In large data service model, algebraic model large data services and their composition was established by using process algebra. In large data service applications, detailed retrieval, process analysis and visualization services, and by improving the retrieval accuracy and efficiency of the service in two aspects of measures to achieve the high data service optimization.

Whole genome and exome sequencing (WGS/WES) are the most popular next‐generation sequencing (NGS) methodologies and are at present often used to detect rare and common genetic variants of clinical significance. We emphasize that automated sequence data processing, management, and visualization should be an indispensable component of modern WGS and WES data analysis for sequence assembly, variant detection (SNPs, SVs), imputation, and resolution of haplotypes. In this manuscript, we present a newly developed findable, accessible, interoperable, and reusable (FAIR) bioinformatics‐genomics pipeline Java based Whole Genome/Exome Sequence Data Processing Pipeline (JWES) for efficient variant discovery and interpretation, and big data modeling and visualization. JWES is a cross‐platform, user‐friendly, product line application, that entails three modules: (a) data processing, (b) storage, and (c) visualization. The data processing module performs a series of different tasks for variant calling, the data storage module efficiently manages high‐volume gene‐variant data, and the data visualization module supports variant data interpretation with Circos graphs. The performance of JWES was tested and validated in‐house with different experiments, using Microsoft Windows, macOS Big Sur, and UNIX operating systems. JWES is an open‐source and freely available pipeline, allowing scientists to take full advantage of all the computing resources available, without requiring much computer science knowledge. We have successfully applied JWES for processing, management, and gene‐variant discovery, annotation, prediction, and genotyping of WGS and WES data to analyze variable complex disorders. In summary, we report the performance of JWES with some reproducible case studies, using open access and in‐house generated, high‐quality datasets.

AI Reading Comprehension Assistants Integrating Visual Data for Enhanced Human Understanding

Composed Image Retrieval (CIR) represents a novel retrieval paradigm that is capable of expressing users' intricate retrieval requirements flexibly. It enables the user to give a multimodal query, comprising a reference image and a modification text, and subsequently retrieve the target image. Notwithstanding the considerable advances made by prevailing methodologies, CIR remains in its nascent stages due to two limitations: 1) inhomogeneity between dominant and noisy portions in visual data is ignored, leading to query feature degradation, and 2) the priority of textual data in the image modification process is overlooked, which leads to a visual focus bias. To address these two limitations, this work presents a focus mapping-based feature extractor, which consists of two modules: dominant portion segmentation and dual focus mapping. It is designed to identify significant dominant portions in images and guide the extraction of visual and textual data features, thereby reducing the impact of noise interference. Subsequently, we propose a textually guided focus revision module, which can utilize the modification requirements implied in the text to perform adaptive focus revision on the reference image, thereby enhancing the perception of the modification focus on the composed features. The aforementioned modules collectively constitute the segmentatiOn-based Focus shiFt reviSion nETwork (OFFSET), and comprehensive experiments on four benchmark datasets substantiate the superiority of our proposed method. The codes and data are available on https://zivchen-ty.github.io/OFFSET.github.io/.

Proposes a flexible and friendly user interface for data queries using data visualization. There are a variety of ways how computers help users search data, and query interfaces are expected to allow users to choose these ways according to their purposes. These ways are classified into two aspects, filtering and browsing. While filtering is a function which searches data on behalf of users, browsing is function which helps users to search data. In the proposed query interface, both browsing function and filtering function are integrated as follows. The browsing function consists of data visualizing and image zooming. With data visualizing, the original data can be transformed into representations from views, which helps users understand the data more easily. Then the image zooming function displays the visualized representation in various scale factors, which enables users to investigate the outline of the entire data or the detail of partial data. The filtering function is also enhanced by providing several ways of post-processing the filtered results. These include marking the results on the visualized representation as well as extracting them. We implemented an experimental system using the proposed query interface. The system shows that it works well as a post-processing unit to conventional data query interfaces, and it is powerful for flexible handling of the data which are roughly searched with the conventional query interfaces.

The amount of data generated is increasing exponentially. However, processing data and producing fast results is a technological challenge. Parallel stream processing can be implemented for handling high frequency and big data flows. The MPI parallel programming model offers low-level and flexible mechanisms for dealing with distributed architectures such as clusters. This paper aims to use it to accelerate video analytics and data visualization applications so that insight can be obtained as soon as the data arrives. Experiments were conducted with a Domain-Specific Language for Geospatial Data Visualization and a Person Recognizer video application. We applied the same stream parallelism strategy and two task distribution strategies. The dynamic task distribution achieved better performance than the static distribution in the HPC cluster. The data visualization achieved lower throughput with respect to the video analytics due to the I/O intensive operations. Also, the MPI programming model shows promising performance outcomes for stream processing applications.