MongoDB Database Research Articles

Development of the Web Application to Support the Process of Conducting Intellectual Games

The article discusses technologies for developing the web application designed to accompany the process of conducting an intellectual game in the format of an interactive quiz. An analysis of similar applications was carried out, which showed that none of them satisfies all the criteria essential in this situation. Requirements for the web application have been determined: the ability to create a library of quizzes, various types of questions, edit ready-made quizzes, participants enter answers through their devices online, award points for the correctness and speed of answers, display questions on the presenter's screen and on team devices, view intermediate and final results in the form of a list, returning participants to the game if necessary, saving the points received. The process of developing an optimized user interface using HTML, CSS, JavaScript, which allows users to interact with the web application, is described, the structure of the web application, the main forms of the administrator and game participants are presented. The server side of the application was developed using Node.JS technologies, including the Socket.io library, and the MongoDB database, the resulting collection of non-relational database is shown. To implement the functionality of the application pages, JavaScript was used; examples of codes are presented that implement the mechanism for connecting the presenter to the game, retrieving information from the database, recording the team's response, and awarding points for the correctness and speed of the response. The web application allows you to reduce the time spent by event organizers and participants and makes it possible to improve the organization of work in general. The presented web application for accompanying intellectual games was successfully approbated during events at the Nizhny Tagil State Social-Pedagogical Institute (branch) of the Russian State Vocational Pedagogical University. The materials of the article can serve as practical recommendations for the development of such web applications to accompany intellectual games held in the format of interactive quizzes.

Next-Gen Database Queries: AI-Driven Natural Language Interface for MongoDB

This paper present an novel concept to query MongoDB database using NLP and Large Language Model (LLM). It is called LLM Based MongoDB Querying System through which users can search in MongoDB databases just by using simple words of English language rather than using complex language queries. To help improve the ai quality we use OpenAI’s LLM to parse the user queries and create correct MongoDB queries themselves. In a large-scale experiment, system is able to classify with an average of 95 percentage accuracy. The findings of this research are important forthe database querying research and might be beneficial for multiple domains. This research is valuable, because our system can help ordinary users become more fluent in database operations and make working with databases more efficient.

Advancing microbial diagnostics: a universal phylogeny guided computational algorithm to find unique sequences for precise microorganism detection.

Sequences derived from organisms sharing common evolutionary origins exhibit similarity, while unique sequences, absent in related organisms, act as good diagnostic marker candidates. However, the approach focused on identifying dissimilar regions among closely-related organisms poses challenges as it requires complex multiple sequence alignments, making computation and parsing difficult. To address this, we have developed a biologically inspired universal NAUniSeq algorithm to find the unique sequences for microorganism diagnosis by traveling through the phylogeny of life. Mapping through a phylogenetic tree ensures a low number of cross-contamination and false positives. We have downloaded complete taxonomy data from Taxadb database and sequence data from National Center for Biotechnology Information Reference Sequence Database (NCBI-Refseq) and, with the help of NetworkX, created a phylogenetic tree. Sequences were assigned over the graph nodes, k-mers were created for target and non-target nodes and search was performed over the graph using the depth first search algorithm. In a memory efficient alternative NoSQL approach, we created a collection of Refseq sequences in MongoDB database using tax-id and path of FASTA files. We queried the MongoDB collection for the target and non-target sequences. In both the approaches, we used an alignment free sliding window k-mer-based procedure that quickly compares k-mers of target and non-target sequences and returns unique sequences that are not present in the non-target. We have validated our algorithm with target nodes Mycobacterium tuberculosis, Neisseria gonorrhoeae, and Monkeypox and generated unique sequences. This universal algorithm is a powerful tool for generating diagnostic sequences, enabling the accurate identification of microbial strains with high phylogenetic precision.

Multiple channels, low-cost, and dual data storage data logger for building a soil temperature network

Temperature measurement is critical in many areas of research, particularly in agriculture, where it can have a significant impact on crop health and yield. Experiments such as seed germination often require numerous temperature sensors to collect extensive data. Typically, data loggers are used to store information, but market options are expensive and offer limited, non-customizable inputs (channels), creating challenges for comprehensive soil temperature monitoring. This study aims to develop a network of open-source, low-cost data loggers with multiple customizable channels for local and remote temperature data storage. The hardware includes Arduino, temperature sensors, a Real Time Clock, and a LoRa module to transmit data to a LILYGO TTGO board, which sends it to a remote MongoDB database while also storing it locally on a microSD card. In addition, a digital tool was developed to retrieve and display both current and historical readings from the MongoDB database. The total cost of this hardware is approximately US$ 72 (based on current prices) for the simplest network, which is approximately 18 % of the commercial cost. The system achieved a root mean square error (RMSE) of 1.6 °C compared to a manual sampling probe thermometer, proving it to be a reliable measurement source. The hardware developed in this study surpasses commercial options by allowing the integration of multiple sensors and emitters, creating a network of data loggers at a lower cost. In addition to the hardware, an open-source digital tool was developed to visualize historical data at no additional cost.

Archery Analytic Workflow in a Web-Based Application

The integration of sports science and camera sensing technology has recently emerged to be an advanced analytical tool in sportsperson performance enhancement. The use of computing power and a web-based application can provide quick information analysis and data reporting between coaches and athletes. The design of an archery analytic workflow is demonstrated in this paper using the Python Flask framework, video analytic algorithms, a structured video inventory framework, MongoDB database setup and integration of the Keypoint R-CNN machine learning backend. A user-friendly data visualisation interface on the front end is integrated in the software to deliver athletes’ analytical capabilities such as thorough frame-by-frame video analysis, posture consistency estimation and joint kinematic analysis. This web application framework is not limited to archery sports, and can be extended to numerous sports, such as shooting, weightlifting and cycling. The significance of integrating camera sensing technology with the sports science field can offer quantitative and qualitative observations to improve training programs and performance evaluation.

ELEVATING HOSPITALITY THROUGH DIGITAL INNOVATION

Abstract: In response to the burgeoning demand for elevated hospitality experiences in Nagpur’s luxury event venues, Rama Celebration has embarked on a transformative digital journey aimed at redefining guest experiences and operational excellence. This research paper delves into Rama Celebration’s innovative approach to hospitality through the integration of cutting-edge digital technologies. The centerpiece of this endeavor is Rama Celebration’s web-based platform, meticulously crafted with advanced technologies such as Node.js, Tailwind CSS, and JavaScript. This platform represents a quantum leap in event planning and management, offering an intuitive interface that simplifies the complexities of event booking, venue exploration, and access to premium amenities. Through an exhaustive analysis of the platform’s development process, this paper sheds light on its pivotal role in enhancing guest satisfaction, optimizing resource allocation, and fostering business growth. The platform’s user-centric design philosophy prioritizes seamless navigation, aesthetically pleasing layouts, and interactive elements, thereby enhancing user engagement and satisfaction. Leveraging modern web technologies, the platform ensures scalability, responsiveness, and cross-device compatibility, catering to the diverse needs and preferences of users.Moreover, the platform’s robust architecture, fortified by Node.js server-side scripting and MongoDB database integration, ensures reliability, security, and efficient data management. Advanced features such as real-time availability updates, map-based visualization, and personalized recommendations further enrich the user experience, empowering guests to make informed decisions effortlessly. By embracing digital innovations, Rama Celebration aims to set a new benchmark for excellence in the hospitality industry, spearheading a paradigm shift in luxury event venue management.Looking ahead, the platform holds immense potential for future enhancements and innovations. Integration with mobile applications, augmented reality (AR) features for virtual venue tours, and predictive analytics for personalized guest experiences are among the key areas of focus for future development. Additionally, continuous evaluation, user feedback mechanisms, and performance optimization will be instrumental in ensuring the platform’s sustained relevance and competitiveness in the dynamic hospitality landscape. Keywords: Rama Celebration, Node.js, Tailwind CSS, JavaScript, Event Venue, Digital Transformation, Guest Experience, Operational Efficiency.

CANTEEN MANAGEMENT SYSTEM

ABSTRACT— An educational canteen management system’s abstract describes the main features and goals of the program. Usually, it contains the following important details: Introduction: Provides a brief overview of the system’s goals and the role it plays in effectively overseeing the school canteen’s operations. Objectives: Describes the primary aims of the system, including expediting food delivery, strengthening order processing, inventory management, and streamlining canteen operations. Features: Lists the system’s primary features, which could include: Online ordering: Using a mobile app or the internet, staff and students can order food. Menu management: Makes it simple for canteen employees to add new items, change menus, and establish prices.. INDEX TEARMS- Frontend development HTML, CSS, JavaScript react js , Backend development React, Database Mongodb Canteen Management Software, Menu Planning, Accounting, Feedback System Chatgpt.

Analysis for an open-source library in database management systems

AbstractSpatial data management is crucial for applications like urban planning and environmental monitoring. While traditional relational databases are commonly used, they struggle with large and complex spatial data. NoSQL databases provide support for unstructured data and scalability. This article compares the performance and disk space usage of SQL Server (a relational database) and MongoDB (NoSQL database) using an open-source library. Experiments conducted with the OpenStreetMap dataset from Central America show that the MongoDB database outperformed the relational SQL Server database in most cases, offering practical advantages for spatial data management in Geographic Information System applications.

Automatic house location identification using location service based VANET

SummaryThe vehicular ad hoc network (VANET) provides a variety of applications and is gaining popularity due to the reuse of network resources. Exact location identification with optimum delay is the demand of all vehicle users. Currently, individuals are utilizing the Global Positioning System (GPS) to ascertain the precise geographical coordinates of a given location. The drawback of GPS technology is its inability to accurately determine location and use GPS in some remote areas. Searching for the location of a particular house randomly incurs a loss of fuel as well as increases delay. This motivates us to propose one automated method through VANET using location service‐based routing for the location identification of a house. The proposed work involves searching for the location of a house using the open‐source MongoDB database, and the operations on the database are performed using the tool Node‐Red. By simulation using SUMO and Network Simulator 2.35, the proposed work is evaluated and compared with existing location service‐based routing like geographic location service (GLS) and hierarchical location service (HLS). The proposed work performs better in terms of routing efficiency (end‐to‐end latency, packet delivery rate), location efficiency (request sends, query success rate, and request travel time), and routing and location overhead (MAC bandwidth consumption). Also, the performance of the proposed work is presented as stable by increasing the number of vehicles. The statistical analysis of the packet delivery ratio and CBR end‐to‐end latency is carried out using T score.

PERANCANGAN STRATEGI VALIDASI DATA DENGAN PEMANFAATAN APLIKASI VALIDASI BERBASIS GMAPS UNTUK PRODUK INDIHOME

Guaranteeing the accuracy and completeness of data is a critical aspect in the scope of telecommunications services. In this context, this research aims to design an effective data validation strategy through the use of a Google Maps (GMaps) based validation application in Indihome products. The design involved an approach that combined GMaps interactive map technology, the React JS framework, and the MongoDB database. Using the React JS framework, an interactive user interface has been developed to facilitate the use of validation applications. Utilization of Google Maps allows visual integration of validation data with precise geographic locations. The use of the MongoDB database supports efficient storage and management of validation data, ensuring that the data obtained meets requirements. Application testing has been carried out to ensure the effectiveness of the designed data validation strategy. The result of this research is the design of an effective data validation strategy, through the application of GMaps, React JS, and MongoDB technology. This implementation is expected to provide a more accurate, efficient and intuitive solution in ensuring data quality in Indihome services. This research can also provide benefits for technological developments in the field of data validation and the application of map concepts in user experience

Fighting moniliasis in Orellana with sensors and PWA for sustainable agriculture

The primary objective of this research was to enhance cocoa production and quality in tropical countries, such as Latin America and Africa, where cocoa cultivation plays a pivotal role in the economy of rural communities. The primary challenge addressed in this study was moniliasis, a fungal disease that affects cocoa fruits and leads to a significant decline in crop production and quality. A multidisciplinary approach was employed to tackle this issue, combining sensors, MongoDB Compass databases, Progressive Web Applications (PWAs), and predictive models. A research methodology incorporating predictive analysis techniques, particularly the logistic regression method, was utilized to achieve early detection and efficient management of moniliasis. Data collection instruments included sensors monitoring vital environmental factors like humidity and temperature alongside MongoDB Compass databases for storing and managing the gathered data. Furthermore, a PWA was developed for real-time data collection and analysis. The results of implementing this sensor-based tool in cocoa cultivation were highly effective. Early detection of moniliasis allowed for more precise preventive and corrective measures, resulting in a significant improvement in cocoa production and quality. These results were substantiated with concrete data demonstrating the tool's efficacy. Keywords. Data Prediction: Models and Applications; Efficient MongoDB Database Management; High-Quality Cocoa; Moniliasis: Treatment and Prevention; Progressive Web Apps (PWA): User Experience.

Fighting moniliasis in Orellana with sensors and PWA for sustainable agriculture

The primary objective of this research was to enhance cocoa production and quality in tropical countries, such as Latin America and Africa, where cocoa cultivation plays a pivotal role in the economy of rural communities. The primary challenge addressed in this study was moniliasis, a fungal disease that affects cocoa fruits and leads to a significant decline in crop production and quality. A multidisciplinary approach was employed to tackle this issue, combining sensors, MongoDB Compass databases, Progressive Web Applications (PWAs), and predictive models. A research methodology incorporating predictive analysis techniques, particularly the logistic regression method, was utilized to achieve early detection and efficient management of moniliasis. Data collection instruments included sensors monitoring vital environmental factors like humidity and temperature alongside MongoDB Compass databases for storing and managing the gathered data. Furthermore, a PWA was developed for real-time data collection and analysis. The results of implementing this sensor-based tool in cocoa cultivation were highly effective. Early detection of moniliasis allowed for more precise preventive and corrective measures, resulting in a significant improvement in cocoa production and quality. These results were substantiated with concrete data demonstrating the tool's efficacy. Keywords: Data Prediction: Models and Applications; Efficient MongoDB Database Management; HighQuality Cocoa; Moniliasis: Treatment and Prevention; Progressive Web Apps (PWA): User Experience.

Document Management System - AWS Comprehend, Generative AI and NoSQL DB

Abstract: Classical document management systems like Microsoft word and Google Docs allow users to retrieve, make, improve and store documents using on device or cloud-based storage mechanisms with One-drive and Google Docs respectively. The growth in the amount of data that needs to be processed in organizations calls for a centralized system that allows users to store, retrieve, analyze, create and improve documents. To solve this, we have built a centralized system that allows users to create and improve documents using generative ai, store documents in a NoSQL based database MongoDB and analyze their documents using AWS Comprehend.

INTEGRATION OF HBIM, XR AND BEACONS FOR CULTURAL MEDIATION OF HISTORICAL HERITAGE: THE CASE OF “AL-QUARAOUIYINE MOSQUE” IN FES

Abstract. The mediation of cultural heritage is a significant concern in the field of history and culture valorization. Technologies such as HBIM (Historic Building Information Modeling), XR (Extended Reality), and Beacons offer highly promising solutions that have demonstrated their value in various heritage-related applications, namely 3D reconstruction, documentation, restoration, preservation, and mediation. The primary objective of this paper is to propose an approach that combines HBIM () and XR (), integrating Beacon sensors to create a rich cultural mediation experience. Our approach is based on an HBIM model of the site, enriched with multi-source semantic information stored in a NoSQL database. It also leverages Beacon sensors to deliver historical content related to the study site (Mosque Al-Quaraouiyine) based on the user's position, thereby providing a cultural mediation experience within an XR environment. This study resulted in the creation of an HBIM model of the study site, the integration of a MongoDB database for storing semantic data, the utilization of the Unity SDK to retrieve this data, and the deployment of Beacons for user position detection within the XR environment. These advancements led to the display of contextual historical and multimedia information through interactive panels, offering a rich and interactive experience. The added value of this project lies in its innovative combination of several modern technologies to provide an educational and immersive experience to users, while also paving the way for future developments towards the dissemination of cultural information based on new technologies.

A database of computed Raman spectra of inorganic compounds with accurate hybrid functionals

Raman spectroscopy is widely applied in identifying local structures in materials, but the interpretation of Raman spectra is non-trivial. An accurate computational database of reference spectra calculated with a consistent level of theory can significantly aid in interpreting measured Raman spectra. Here, we present a database of Raman spectra of inorganic compounds calculated with accurate hybrid functionals in density functional theory. Raman spectra were obtained by calculating dynamical matrices and polarizability tensors for structures from the Inorganic Crystal Structure Database. The calculated Raman spectra and other phonon properties (e.g., infrared spectra) are stored in a MongoDB database publicly shared through a web application. We assess the accuracy of our Raman calculations by statistically comparing ~80 calculated spectra with an existing experimental Raman database. To date, the database contains 161 compounds and is continuously growing as we add more materials computed with our automated workflow.

Performance Evaluation of Face Recognition System (FRS) Developed with Haar Cascade and MongoDB Integration in Recognition of Covered Faces

Although traditional face recognition systems (FRS) can detect with a certain success rate whether a mask is worn, they may fail due to the fact that most of the faces of the people who wear masks are covered. The difficulties arising from the fact that a significant part of the faces of individuals wearing masks are covered limits the performance of existing FRSs. In this research, it is aimed to integrate the Haar Cascade method with the MongoDB database in real time using the OpenCV library for mask-wearing face recognition and to demonstrate its performance with extensive experiments. In the experiments, the data set created within the scope of this study from realistic face images, in which most of the masked faces are covered, was used. Our research has shown that the accuracy of face recognition is 85% for masked faces, 61% for unmasked faces, and 41% when half of the face is covered by a different object. It is considered that this study will contribute to the literature in terms of providing a more effective and applicable mask detection solution by combining the Haar Cascade method with real-time database management integration.

Features of software implementing the prefix search method in cryptographically protected databases

The article addresses the specific considerations associated with the development of software implementing the prefix search method in cryptographically protected databases. This method is a variant of symmetric searchable encryption, which allows search among the encrypted data. The prefix search method allows searching for prefixes among encrypted data without the need for decryption. Such an approach resolves the issue of maintaining data confidentiality stored on remote or cloud servers. However, its usage introduces a set of issues that must be considered during the development of the corresponding software. The paper analyzes the requirements for software that implements the prefix search method, defines the software architecture, and justifies the choice of technologies and tools for software implementation, including ASP.NET, Java, JavaScript, PHP, Python programming languages, MongoDB database management system, and the FastAPI framework. A description of the deployment process of the corresponding software is provided. To assess the performance of the developed software, the well-known Apache JMeter tool for conducting load testing was utilized. The obtained performance evaluations of the proposed solution indicate acceptable time delays in processing relevant data search queries.

The Development of High Availability Database Infrastructure for OSS Projects with Monitoring Systems in Cloud Computing Environments

This study addressed a critical problem in the Kominfo's Operation Support System (OSS) project, which significantly impacted business operations. The outage of the MongoDB database, a vital component of OSS, resulted in server crashes and data damage. To overcome this issue, the OSS team initiated a project to migrate to PostgreSQL for high availability and improved database performance. The implementation involved the use of HA PostgreSQL technology, with multiple connected servers sharing data in real-time. Through functional and performance testing, the study has demonstrated that the HA PostgreSQL system increased database availability, managed server failures, and facilitated effective cluster administration. The findings of this research can guide the development of the OSS project's IT infrastructure and serve as a reference for similar projects utilizing HA PostgreSQL technology.

A study of performance comparison of databases for HALF data archiving system

The Hefei Advanced Light Facility (HALF) is a 2.2 GeV fourth-generation synchrotron radiation light source. The HALF control system is a distributed control system based on Experimental Physics and Industrial Control System (EPICS). As the essential part of the HALF control system, the Historical Data Archiving System (HDAS) is responsible to store operational data for the entire facility including the accelerator and beamlines, and provides the functions for data query and analysis. According to the estimation based on the HALF scale, approximately 25,000 EPICS PVs will be stored in HDAS, and these accumulated massive data require a dedicated database for persistent storage and management. Under the EPICS PV data scenario of HALF, a fair database test platform is designed and built to test the read-write performance of databases commonly used in the particle accelerator field. The tested objects include EPICS Archiver Appliance and the five databases MongoDB, HBase, InfluxDB, TimescaleDB, and Cassandra. Additionally, research on the five databases software ecosystem is conducted for the development and extension of HDAS. The test results indicate that TimescaleDB has the fastest read performance, and 1.4×10^6 items of data can be read per second. TimescaleDB also has superior write performance. 1.2 × 105 items of data can be written per second, which can fully meet the writing requirements of HDAS. Regarding the software ecosystem, TimescaleDB is compatible with a larger number of data visualization software and big data processing frameworks. Therefore, TimescaleDB is more suitable as the HDAS database. In the future, a TimescaleDB distributed cluster will be designed and deployed, and an HDAS prototype system will be developed based on this cluster.

Smart Parking System using Image Detection Algorithms & Optical Character Recognition (OCR)

Abstract: The Smart Parking System is an innovative and effective solution designed to make parking more efficient in urban areas, using advanced technology to help both vehicle owners and parking operators automate and optimize the parking process. When a vehicle enters the parking lot, it is successfully identified using an advanced sensor and camera system, with a highresolution camera that records the license plate number and optical character recognition (OCR) technology. Get accurate license plate numbers. The data is then stored in the MongoDB database along with the input timestamp and when the vehicle moves, the license plate is re-identified using the same technique and the system stores the input timestamp associated with the detected license plate in the database. he does. Reading from Parking time is calculated from the time difference between entering and leaving the parking lot, and the parking fee is calculated per hour. To facilitate payment, a QR code is generated and displayed at the exit. Vehicle owners can use their mobile device to scan this QR code and connect to the secure payment gateway. After the payment is completed, the exit door will open automatically and the car can leave the parking lot. This smart parking system offers many benefits, including fewer numbers, increased accuracy, reduced waiting time, and efficient payment processing. It reduces congestion in urban areas and improves traffic flow while providing users with convenient and troublefree parking