Back-end Database Research Articles

THE OPEN DATABASE OF REGIONAL MODELS OF THE INTERNATIONAL SERVICE FOR THE GEOID

Abstract. This paper describes the development of a web application to facilitate the management and processing of geoid models in the ISG collection as well as of additional information available in the ISG website (mainly projects, services and publications). The ISG service pays significant attention to metadata and data interoperability from published geoid models. As a result, the possibility of creating a database with all necessary information for describing geoid models has been considered. PostgreSQL was chosen as the database for the implementation to take advantage of PostGIS geographic processing functions for developing a Database Management System for the resources available inside the ISG service catalogue (including geoid products, software, publications, etc.). The project's web development environment is Django, a web framework which is supported by the PostgreSQL database back-end and which makes it easier to integrate new applications or processing services into the website. A model solution for raster data integration using a PostGIS database back-end named Django-raster has also been investigated as part of the implementation, provided also that it is possible to take advantage of the vast Django community. The Django-raster model allows for the storage of datasets as tiles in the database, their exposure as a Tiled Map Services (TMS), and allows as well raster map algebra operations. TMS services will be utilized to share the geoid models and metadata included in the open access archive using a WebGIS, created with OpenLayers.

Expediting knowledge acquisition by a web framework for Knowledge Graph Exploration and Visualization (KGEV): case studies on COVID-19 and Human Phenotype Ontology

BackgroundKnowledges graphs (KGs) serve as a convenient framework for structuring knowledge. A number of computational methods have been developed to generate KGs from biomedical literature and use them for downstream tasks such as link prediction and question answering. However, there is a lack of computational tools or web frameworks to support the exploration and visualization of the KG themselves, which would facilitate interactive knowledge discovery and formulation of novel biological hypotheses.MethodWe developed a web framework for Knowledge Graph Exploration and Visualization (KGEV), to construct and visualize KGs in five stages: triple extraction, triple filtration, metadata preparation, knowledge integration, and graph database preparation. The application has convenient user interface tools, such as node and edge search and filtering, data source filtering, neighborhood retrieval, and shortest path calculation, that work by querying a backend graph database. Unlike other KGs, our framework allows fast retrieval of relevant texts supporting the relationships in the KG, thus allowing human reviewers to judge the reliability of the knowledge extracted.ResultsWe demonstrated a case study of using the KGEV framework to perform research on COVID-19. The COVID-19 pandemic resulted in an explosion of relevant literature, making it challenging to make full use of the vast and heterogenous sources of information. We generated a COVID-19 KG with heterogenous information, including literature information from the CORD-19 dataset, as well as other existing knowledge from eight data sources. We showed the utility of KGEV in three intuitive case studies to explore and query knowledge on COVID-19. A demo of this web application can be accessed at http://covid19nlp.wglab.org. Finally, we also demonstrated a turn-key adaption of the KGEV framework to study clinical phenotypic presentation of human diseases by Human Phenotype Ontology (HPO), illustrating the versatility of the framework.ConclusionIn an era of literature explosion, the KGEV framework can be applied to many emerging diseases to support structured navigation of the vast amount of newly published biomedical literature and other existing biological knowledge in various databases. It can be also used as a general-purpose tool to explore and query gene-phenotype-disease-drug relationships interactively.

Digital science platform: an interactive web application and database of osteological material for anatomy education

BackgroundTo meet the remote-learning constraints imposed due to the COVID-19 pandemic, the Digital Science Platform was developed. Human anatomy courses require practical classes that involve working on prepared specimens, although access to such specimens has been restricted. Therefore, the aim was to prepare appropriate-quality, scanned 3D model databases of human bone specimens and an interactive web application for universal access to educational materials.Main bodyThe database is located on the pcn.cnt.edu.pl website and contains 412 three-dimensional osteological models created via a structured light scanner, tomography and microtomography. The webservice contains a search engine and enables interactive visualization of the models. The database can be accessed, without restrictions, by any student or researcher wishing to use the models for noncommercial purposes. The stored models can be visualized with the open-source VisNow platform, which is also available to download from the webservice. The MariaDB backend database was deployed, and an Apache server with a personal home page (PHP) frontend was used.ConclusionThe models in the database are unique due to the specific digitalization process and skeleton specimen origin. Further development of the Digital Science Platform is foreseen in the near future to digitize other valuable materials.

VRprofile2: detection of antibiotic resistance-associated mobilome in bacterial pathogens.

VRprofile2 is an updated pipeline that rapidly identifies diverse mobile genetic elements in bacterial genome sequences. Compared with the previous version, three major improvements were made. First, the user-friendly visualization could aid users in investigating the antibiotic resistance gene cassettes in conjunction with various mobile elements in the multiple resistance region with mosaic structure. VRprofile2 could compare the predicted mobile elements to the collected known mobile elements with similar architecture. A new mobilome indicator was proposed to give an overall estimation of the mobilome size in individual bacterial genomes. Second, the relationship between antibiotic resistance genes, mobile elements, and host strains would be efficiently examined with the aid of predicted strain's sequence typing, the incompatibility group and the transferability of plasmids. Finally, the updated back-end database, MobilomeDB2, now collected nearly a thousand active mobile elements retrieved from literature or based on prediction. The pre-computed results of the antibiotic resistance gene-carrying mobile elements of >5500 ESKAPEE genomes were also provided. We expect that VRprofile2 will provide better support for researchers interested in bacterial mobile elements and the dissemination of antibiotic resistance. VRprofile2 is freely available to all users without any login requirement at https://tool2-mml.sjtu.edu.cn/VRprofile.

A Detailed Survey on Various Aspects of SQL Injection in Web Applications: Vulnerabilities, Innovative Attacks, and Remedies

In today’s world, Web applications play a very important role in individual life as well as in any country’s development. Web applications have gone through a very rapid growth in the recent years and their adoption is moving faster than that was expected few years ago. Now-a-days, billions of transactions are done online with the aid of different Web applications. Though these applications are used by hundreds of people, in many cases the security level is weak, which makes them vulnerable to get compromised. In most of the scenarios, a user has to be identified before any communication is established with the backend database. An arbitrary user should not be allowed access to the system without proof of valid credentials. However, a crafted injection gives access to unauthorized users. This is mostly accomplished via SQL Injection input. In spite of the development of different approaches to prevent SQL injection, it still remains an alarming threat to Web applications. In this paper, we present a detailed survey on various types of SQL Injection vulnerabilities, attacks, and their prevention techniques. Alongside presenting our findings from the study, we also note down future expectations and possible development of countermeasures against SQL Injection attacks.

Hotel Management System Using Python Tkinter GUI

Hotel Management System is a desktop application software system where the management of entire hotel is computerized. The hotel management system is using Python. As the rich TKinter GUI for front end and SQL server as the secured backend database. The Tkinter is the gui library of python which is designed to help python developers create system program with a user interface. The hotel management system is rich gui user interface system.

Incorporating Environmental Protection Requirement in Industrial IoT Access Control Security Using Arduino Technology MQ2 and DHT11 Sensor Networks

This paper presents the development of environmental monitoring mechanism based on Arduino Sensor Technology for Industrial Internet of Things (Industrial IoT) Critical Infrastructure (CI) Protection in an Access Control role. Access control of hazardous substances is one of the most vulnerable aspects of Industrial IoT CI. The idea behind this study is to harden environmental security through live ecological monitoring of Temperature, Humidity and emitted hazardous substances or gases due to Industrial activities and processes in Smart CI places such as Nuclear Processing Plant, Thermal Generation Power Industries, Fossil Processing Plant, Military Chemical warfare plant, food processing factory, Agriculture Chemical Manufacturing Plant and many other CI industries. Live monitoring is one of the most important security requirements for any cyber-Access Control Mechanism. The literature revealed that similar systems were developed and implemented in different organizations and with different System Requirements and specifications. However, most CI industries have not automated environmental monitoring requirements and integrated the requirement into a broad institution security continuum. The consequence of neglecting environmental security is climate change. Furthermore, Toxic gas pollution affects millions of people around the world and contributes to about 5.4 percent of deaths worldwide. Pollution kills more people than the combination of malaria, AIDS, and tuberculosis hence the development of security mechanisms to monitor pollutants in the atmosphere. The main objective of this study was to develop a computer-based application for monitoring environmental hazardous substances such as extreme temperature, humidity, carbon dioxide (CO2), carbon monoxide (CO), and Smoke in the atmosphere surrounding Industrial IoT CI Industries. The system development approach employed was the Arduino engineering process model. To achieve this objective, cheaper ecological sensor Networks that include MQ2 and DHT11 Arduino Sensors are connected to Arduino Uno Microcontroller through a solderless breadboard Arduino component. The backend database was MySQL Relational database management system. The developed prototype application produced a number of results including live monitoring of temperature, humidity, CO2, CO, Smoke, and many others. A system evaluation process was conducted and the result indicated 99.999% accuracy levels. The results, it evidence that the developed prototype application can improve environmental security in Industrial IoT CI institutions.

COVID-19 prevention and control strategies: learning from the Macau model.

Background: Macau is a densely populated international tourist city. Compared to most tensely populated countries/territories, the prevalence and mortality of COVID-19 in Macau are lower. The experiences in Macau could be helpful for other areas to combat the COVID-19 pandemic. This article introduced the endeavours and achievements of Macau in combatting the COVID-19 pandemic.Method: Both qualitative and quantitative analysis methods were used to explore the work, measures, and achievements of Macau in dealing with the COVID-19 pandemic.Results: The results revealed that Macau has provided undifferentiated mask purchase reservation services, COVID-19 vaccination services to all residents and non-residents in Macau along with delivering multilingual services, in Chinese, English and Portuguese, to different groups of the population. To facilitate the travels of people, business and trades between Macau and mainland China, the Macau government launched the Macau Health Code System, which uses the health status declaration, residence history declaration, contact history declaration of the declarant to match various relevant backend databases within the health authority and provide a risk-related colour code operations. The Macau Health Code System connects to the Chinese mainland's own propriety health code system seamlessly, whilst effectively protecting the privacy of the residents. Macau has also developed the COVID-19 Vaccination Appointment system, the Nucleic Acid Test Appointment system, the Port and Entry/Exit Quarantine system, the medical and other supporting systems.Conclusion: The efforts in Macau have achieved remarkable results in COVID-19 prevention and control, effectively safeguarding the lives and health of the people and manifesting the core principle of “serving the public”. The measures used are sustainable and can serve as an important reference for other countries/regions.

Проектирование баз данных автодорожной отрасли на основе теоретико-множественного анали-за сложных систем

In the article, a method for designing databases for the road industry that meets the requirements of the digital economy was developed. The set-theory analysis of complex systems implemented by relational databases (RDBs) was used. This approach meets current trends in the global digital economy, where relational databases provide efficient operation of the banking sector, industrial production management, fast data processing (bank cards, Internet search), etc. The Department of Roads at Irkutsk National Research Technical University designed and is developing the road database for the Irkutsk region. The paper considers the concept of this project and prospects of its further development with a view to the possible use of this database by remote users. The specifics of digital economy in the Russian Federation call for establishing branches and territorial databases, including the road database for the Russian regions. The efficient solutions for such a database were proposed, including, in particular, integrating remote users with the back-end database based on the attribute characteristics to limit the available content and allowable changes to the back-end data.

Online Simulation Education for Audiometry Training.

The purpose of this clinical focus article was to describe a new online simulation program for pure-tone audiometry. Fictional but realistic patient profiles and testing environments were created to teach students about hearing screening protocols and pure-tone audiology. The diversity of the demographics of the United States is represented throughout the program. The web app was created using HTML/JS/CSS with a Flask server backend and MySQL database. The program allows students to learn the process of conducting a hearing screening and measuring audiometric thresholds using a web-based virtual clinical audiometer. The virtual audiometer includes standard audiometer features and allows for instruction based on standard guidelines. The diversity of the patients within the simulation program allows for discussions of diversity to be woven throughout the curriculum. The new simulation program is designed for use as a clinical training tool enabling undergraduate and graduate students to actively participate in hearing screening testing and pure-tone audiometry using any web browser. The program is also designed with the intent to improve pedagogical outcomes at the undergraduate and graduate level for communication sciences and disorders education for pure-tone audiometry by providing instructors with content that focuses on the diversity that is represented in the demographics of the United States.

IoT Enabled Notice Board

This paper deals with the implementation of software tools and the whole framework for IoT Enabled Notice Board. Notice boards can change the way people communicate with each other, providing important information to large people at the right time. Notice boards are used extensively in schools, colleges, hospitals, railway stations, hotels, malls, etc. The developed system includes the notice board being connected to a local server running on Raspberry Pi 24*7. The notice to be displayed can be sent via the developed Android application or from the webpage. The data is then sent onto the server where it is pushed in a backend MySQL database. From the database, the contents of the notice are then displayed on the Monitor. The system provides an authentication layer to avoid any unauthorized activities since the target audience for the prototype developed is mainly schools and colleges.

A Big Data Application for Energy Consumption Management of Commercial Buildings

The demand for energy is increasing rapidly, and after a few years, it may surpass the available energy, which may lead the energy providers to increase the cost of energy consumption to compensate the cost for the production. This paper provides design and implementation details of a prototype big data application developed to help large buildings to automatically manage their energy consumption by setting energy consumption targets, collecting periodic energy consumption data, storing the data streams, displaying the energy consumption graphically in real-time, analyzing the consumption patterns, and generating energy consumption graphs and reports. The application is connected to Mongo NoSQL backend database to handle the large and continuously changing data. This big data energy consumption management system is expected to help the users in managing energy consumption by analyzing the patterns to see if it is within or above the desired consumption targets and displaying the data graphically.

MODOMICS: a database of RNA modification pathways. 2021 update.

The MODOMICS database has been, since 2006, a manually curated and centralized resource, storing and distributing comprehensive information about modified ribonucleosides. Originally, it only contained data on the chemical structures of modified ribonucleosides, their biosynthetic pathways, the location of modified residues in RNA sequences, and RNA-modifying enzymes. Over the years, prompted by the accumulation of new knowledge and new types of data, it has been updated with new information and functionalities. In this new release, we have created a catalog of RNA modifications linked to human diseases, e.g., due to mutations in genes encoding modification enzymes. MODOMICS has been linked extensively to RCSB Protein Data Bank, and sequences of experimentally determined RNA structures with modified residues have been added. This expansion was accompanied by including nucleotide 5′-monophosphate residues. We redesigned the web interface and upgraded the database backend. In addition, a search engine for chemically similar modified residues has been included that can be queried by SMILES codes or by drawing chemical molecules. Finally, previously available datasets of modified residues, biosynthetic pathways, and RNA-modifying enzymes have been updated. Overall, we provide users with a new, enhanced, and restyled tool for research on RNA modification. MODOMICS is available at https://iimcb.genesilico.pl/modomics/.

PDBrt: A free database of complexes with measured drug-target residence time

Background: Difficulties in translating the in vitro potency determined by cellular assays into in vivo efficacy in living organisms complicates the design and development of drugs. However, the residence time of a drug in its molecular target is becoming a key parameter in the design and optimization of new drugs, as recent studies show that residence time can reliably predict drug efficacy in vivo. Experimental approaches to binding kinetics and target ligand complex solutions are currently available, but known bioinformatics databases do not usually report information about the ligand residence time in its molecular target. Methods: To extend existing databases we developed the Protein Data Bank (PDB) residence time database (PDBrt) which reports drug residence time. The database is implemented as an open access web-based tool. The front end uses Bootstrap with Hypertext Markup Language (HTML), jQuery for the interface and 3Dmol.js to visualize the complexes. The server-side code uses Python web application framework, Django Rest Framework and backend database PostgreSQL. Results: The PDBrt database is a free, non-commercial repository for 3D protein-ligand complex data, including the measured ligand residence time inside the binding pocket of the specific biological macromolecules as deposited in The Protein Data Bank. The PDBrt database contains information about both the protein and the ligand separately, as well as the protein-ligand complex, binding kinetics, and time of the ligand residence inside the protein binding site. Availability: https://pdbrt.polsl.pl

PRODORIC: state-of-the-art database of prokaryotic gene regulation.

PRODORIC is worldwide one of the largest collections of prokaryotic transcription factor binding sites from multiple bacterial sources with corresponding interpretation and visualization tools. With the introduction of PRODORIC2 in 2017, the transition to a modern web interface and maintainable backend was started. With this latest PRODORIC release the database backend is now fully API-based and provides programmatical access to the complete PRODORIC data. The visualization tools Genome Browser and ProdoNet from the original PRODORIC have been reintroduced and were integrated into the PRODORIC website. Missing input and output options from the original Virtual Footprint were added again for position weight matrix pattern-based searches. The whole PRODORIC dataset was reannotated. Every transcription factor binding site was re-evaluated to increase the overall database quality. During this process, additional parameters, like bound effectors, regulation type and different types of experimental evidence have been added for every transcription factor. Additionally, 109 new transcription factors and 6 new organisms have been added. PRODORIC is publicly available at https://www.prodoric.de.

Predicting Student Performance for Early Intervention using Classification Algorithms in Machine Learning

Predicting Student’s Performance System is to find students who may require early intervention before they fail to graduate. It is generally meant for the teaching faculty members to analyze Student's Performance and Results. It stores Student Details in a database and uses Machine Learning Model using i. Python Data Analysis tools like Pandas and ii. Data Visualization tools like Seaborn to analyze the overall Performance of the Class. The proposed system suggests student performance prediction through Machine Learning Algorithms and Data Mining Techniques. The Data Mining technique used here is classification, which classifies the students based on student’s attributes. The Front end of the application is made using React JS Library with Data Visualization Charts and connected to a backend Database where all student’s records are stored in MongoDB and the Machine Learning model is trained and deployed through Flask. In this process, the machine learning algorithm is trained using a dataset to create a model and predict the output on the basis of that model. Three different types of data used in Machine Learning are continuous, categorical and binary. In this study, a brief description and comparative analysis of various classification techniques is done using student performance dataset. The six different machine learning Classification algorithms, which have been compared, are Logistic Regression, Decision Tree, K-Nearest Neighbor, Naïve Bayes, Support Vector Machine and Random Forest. The results of Naïve Bayes classifier are comparatively higher than other techniques in terms of metrics such as precision, recall and F1 score. The values of precision, recall and F1 score are 0.93, 0.92 and 0.92 respectively.

TSCache

Time-series databases are becoming an indispensable component in today's data centers. In order to manage the rapidly growing time-series data, we need an effective and efficient system solution to handle the huge traffic of time-series data queries. A promising solution is to deploy a high-speed, large-capacity cache system to relieve the burden on the backend time-series databases and accelerate query processing. However, time-series data is drastically different from other traditional data workloads, bringing both challenges and opportunities. In this paper, we present a flash-based cache system design for time-series data, called TSCache . By exploiting the unique properties of time-series data, we have developed a set of optimization schemes, such as a slab-based data management, a two-layered data indexing structure, an adaptive time-aware caching policy, and a low-cost compaction process. We have implemented a prototype based on Twitter's Fatcache. Our experimental results show that TSCache can significantly improve client query performance, effectively increasing the bandwidth by a factor of up to 6.7 and reducing the latency by up to 84.2%.

QTeller: a tool for comparative multi-genomic gene expression analysis.

Over the last decade, RNA-Seq whole-genome sequencing has become a widely used method for measuring and understanding transcriptome-level changes in gene expression. Since RNA-Seq is relatively inexpensive, it can be used on multiple genomes to evaluate gene expression across many different conditions, tissues and cell types. Although many tools exist to map and compare RNA-Seq at the genomics level, few web-based tools are dedicated to making data generated for individual genomic analysis accessible and reusable at a gene-level scale for comparative analysis between genes, across different genomes and meta-analyses. To address this challenge, we revamped the comparative gene expression tool qTeller to take advantage of the growing number of public RNA-Seq datasets. qTeller allows users to evaluate gene expression data in a defined genomic interval and also perform two-gene comparisons across multiple user-chosen tissues. Though previously unpublished, qTeller has been cited extensively in the scientific literature, demonstrating its importance to researchers. Our new version of qTeller now supports multiple genomes for intergenomic comparisons, and includes capabilities for both mRNA and protein abundance datasets. Other new features include support for additional data formats, modernized interface and back-end database and an optimized framework for adoption by other organisms' databases. The source code for qTeller is open-source and available through GitHub (https://github.com/Maize-Genetics-and-Genomics-Database/qTeller). A maize instance of qTeller is available at the Maize Genetics and Genomics database (MaizeGDB) (https://qteller.maizegdb.org/), where we have mapped over 200 unique datasets from GenBank across 27 maize genomes. Supplementary data are available at Bioinformatics online.

Design of Library Mobile User Behavior Analysis model for Personalized Information Service

This paper summarizes the research status of Library mobile user behavior at home and abroad, and provides a reference for the design of Library mobile user behavior analysis model for personalized information service. Using the method of theoretical research and model analysis, the relationship between user behavior sequence and user behavior characteristics is studied from three aspects of data collection, data processing and data application. This paper defines and represents the invisible feedback behavior of Library mobile users on IOS app, android app and web page and product UI by combining the front-end operation behavior with the back-end business database, collects the data from three aspects: basic information field, user behavior related field and configuration flume of mobile users’ client, and introduces the time dimension, user dimension, Department dimension, resource dimension, resource dimension The data warehouse level modeling is carried out from six dimensions of behavior dimension and entrance channel, and the data application is realized from two steps of user behavior sequence prediction and user interest guidance.

A Python-WSGI and PHP-Apache Web Server Performance Analysis by Search Page Generator (SPG)

The web servers (WSGI-Python) and (PHP-Apache) are in middleware tier architecture. Middleware architecture is between frontend tier and backend tier, otherwise it’s a connection between frontend tier and backend tier for three tier architecture. The ELearning systems are designed by two different dynamic web technologies. First is by Python-WSGI and the second is by Personal Home Page (PHP-Apache). The two websites were designed with different open source and cross platform web technologies programming language namely; Python and PHP in the same structure and weight will evaluate perform over two different operating systems (OSs): 1) Windows-16 and 2) Linux-Ubuntu 20.4. Both systems run over the same computer architecture (64bit) as a server side with a common backend MySQL web database for both of them. Nevertheless, the middleware for PHP is a cross Apache MySQL PHP Perl (XAMPP), but the middleware for Python is Pycharm and the web server gateway interface (WSGI). WSGI and Apache are both web servers and this paper will show which of them has a better response time (RT). On the one hand, the experimental results demonstrate that the Python-WSGI is even weightier in Mbyte than PHP-Apache, on the other hand Python is still faster and more accurate than PHP. The designed SPG is by handwriting codes: one time designed the SPG by PHP source code and the other time designed by Python source code. Both Python-WSGI and PHP-Apache results are targeted to compare by the least time in milliseconds and take in to account enhanced performance.