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COCONUT online: Collection of Open Natural Products database
Natural products (NPs) are small molecules produced by living organisms with potential applications in pharmacology and other industries as many of them are bioactive. This potential raised great interest in NP research around the world and in different application fields, therefore, over the years a multiplication of generalistic and thematic NP databases has been observed. However, there is, at this moment, no online resource regrouping all known NPs in just one place, which would greatly simplify NPs research and allow computational screening and other
in silico
applications. In this manuscript we present the online version of the COlleCtion of Open Natural prodUcTs (COCONUT): an aggregated dataset of elucidated and predicted NPs collected from open sources and a web interface to browse, search and easily and quickly download NPs. COCONUT web is freely available at
https://coconut.naturalproducts.net
.
Journal Article
Data science fundamentals for Python and MongoDB
\"Build the foundational data science skills necessary to work with and better understand complex data science algorithms. This example-driven book provides complete Python coding examples to complement and clarify data science concepts, and enrich the learning experience. Coding examples include visualizations whenever appropriate. The book is a necessary precursor to applying and implementing machine learning algorithms. The book is self-contained. All of the math, statistics, stochastic, and programming skills required to master the content are covered. In-depth knowledge of object-oriented programming isn't required because complete examples are provided and explained. Data Science Fundamentals with Python and MongoDB is an excellent starting point for those interested in pursuing a career in data science. Like any science, the fundamentals of data science are prerequisite to competency. Without proficiency in mathematics, statistics, data manipulation, and coding, the path to success is rocky at best. The coding examples in this book are concise, accurate, and complete, and perfectly complement the data science concepts introduced.\"-- Provided by publisher.
Book
Smart Weather Data Management Based on Artificial Intelligence and Big Data Analytics for Precision Agriculture
Smart management of weather data is an essential step toward implementing sustainability and precision in agriculture. It represents an important input for numerous tasks, such as crop growth, development, yield, and irrigation scheduling, to name a few. Advances in technology allow collecting this weather data from heterogeneous sources with high temporal resolution and at low cost. Generating and using these data in their raw form makes no sense, and therefore implementing adequate infrastructure and tools is necessary. For that purpose, this paper presents a smart weather data management system evaluated using data from a meteorological station installed in our study area covering the period from 2013 to 2020 at a half-hourly scale. The proposed system makes use of state-of-the-art statistical methods, machine learning, and deep learning models to derive actionable insights from these raw data. The general architecture is made up of four layers: data acquisition, data storage, data processing, and application layers. The data sources include real-time sensors, IoT devices, reanalysis data, and raw files. The data are then checked for errors and missing values using a proposed method based on ERA5-Land reanalysis data and deep learning. The resulting coefficient of determination (R2) and Root Mean Squared Error (RMSE) for this method were 0.96 and 0.04, respectively, for the scaled air temperature estimate. The MongoDB NoSQL database is used for storage thanks to its ability to deal with real-world big data. The system offers various services such as (i) weather time series forecasts, (ii) visualization and analysis of meteorological data, and (iii) the use of machine learning to estimate the reference evapotranspiration (ET0) needed for efficient irrigation. To this, the platform uses the XGBoost model to achieve the precision of the Penman–Monteith method while using a limited number of meteorological variables (air temperature and global solar radiation). Results for this approach give R2 = 0.97 and RMSE = 0.07. This system represents the first incremental step toward implementing smart and sustainable agriculture in Morocco.
Journal Article
Getting MEAN with Mongo, Express, Angular, and Node
Juggling languages mid-application can radically slow down a full-stack web project. The MEAN stack (MongoDB, Express, Angular, and Node) uses JavaScript end to end, maximizing developer productivity and minimizing context switching. And you'll love the results! MEAN apps are fast, powerful, and beautiful. \"Getting MEAN, second edition\" teaches you how to develop full-stack web applications using the MEAN stack. Practical from the very beginning, the book helps you create a static site in Express and Node. Expanding on that solid foundation, you'll integrate a MongoDB database, build an API, and add an authentication system. Along the way, you'll get countless pro tips for building dynamic and responsive data-driven web applications!
Book
Consistency Models of NoSQL Databases
Internet has become so widespread that most popular websites are accessed by hundreds of millions of people on a daily basis. Monolithic architectures, which were frequently used in the past, were mostly composed of traditional relational database management systems, but quickly have become incapable of sustaining high data traffic very common these days. Meanwhile, NoSQL databases have emerged to provide some missing properties in relational databases like the schema-less design, horizontal scaling, and eventual consistency. This paper analyzes and compares the consistency model implementation on five popular NoSQL databases: Redis, Cassandra, MongoDB, Neo4j, and OrientDB. All of which offer at least eventual consistency, and some have the option of supporting strong consistency. However, imposing strong consistency will result in less availability when subject to network partition events.
Journal Article
MongoDB Aggregation Pipeline Performance: Analysis of Query Plan Selection and Optimizer Behavior Across Versions and Collection Scales
This article examines how MongoDB optimizes aggregation pipeline queries, focusing on two mechanisms: a trial-based plan selection process that runs candidate execution plans in parallel and picks the one returning the most results for the least work, and rule-based operator rewriting by the Pipeline Optimizer. The study tests nine aggregation query types on a synthetic e-commerce dataset with 50K documents, using MongoDB versions 6.0.3 and 8.2.5 under identical conditions. For each query, all valid operator orderings are evaluated together with the physical execution plan and the Pipeline Optimizer output. Each test runs 20 times with the plan cache cleared before every run. The study also tests scalability with datasets of 150K and 250K documents. Three cases are identified where the rule-based optimizer falls short: IXSCAN preference bias at low selectivity, where the suboptimal plan is up to nine times slower than the optimal (80 ms vs. 699 ms at 250K under MongoDB 8.2.5), unbounded document multiplication after$unwind, and failure to account for $ group output cardinality. MongoDB 8.2.5 improves performance in most cases compared to version 6.0.3.$match + $ group queries run up to 28% faster. Queries that rely on IXSCAN improve by up to 18%. Unbounded projection operations run slower in MongoDB 8.2.5 at all tested sizes. The slowdown is +23% at 50K, +3% at 150K, and +14% at 250K, pointing to a change in the projection execution path between versions.
Journal Article
A Transfer Learning-Based Approach with Deep CNN for COVID-19- and Pneumonia-Affected Chest X-ray Image Classification
The COVID-19 pandemic creates a significant impact on everyone’s life. One of the fundamental movements to cope with this challenge is identifying the COVID-19-affected patients as early as possible. In this paper, we classified COVID-19, Pneumonia, and Healthy cases from the chest X-ray images by applying the transfer learning approach on the pre-trained VGG-19 architecture. We use MongoDB as a database to store the original image and corresponding category. The analysis is performed on a public dataset of 3797 X-ray images, among them COVID-19 affected (1184 images), Pneumonia affected (1294 images), and Healthy (1319 images) (
https://www.kaggle.com/tawsifurrahman/covid19-radiography-database/version/3
). This research gained an accuracy of 97.11%, average precision of 97%, and average Recall of 97% on the test dataset.
Journal Article
MongoDB Database as Storage for GPON Frames
This work is focused on creating an open-source software-based solution for monitoring traffic transmitted through gigabit passive optical network. In this case, the data are captured by the field-programmable gate array (FPGA) card and reassembled using parsing software from a passive optical network built on the International Telecommunication Unit telecommunication section (ITU-T) G.984 gigabit-capable passive optical network GPON recommendation. Then, the captured frames are converted by suitable software into GPON frames, which will be further processed for analysis. Due to the high transfer rate of GPON recommendations, the work describes the issue of writing to the Mongo database system. In order to achieve the best possible results and minimal loss of transmitted frames, a series of tests were performed. The proposed test scenarios are based on different database writing approaches and are implemented in the Python and C# programming languages. Based on our results, it has been shown that the high processing speed is too high for Python processing. Critical operations must be implemented in the C# programming language. Due to rapid application development, Python can only be used for noncritical time-consuming data processing operations.
Journal Article
Performance Evaluation of NoSQL Document Databases: Couchbase, CouchDB, and MongoDB
NoSQL document databases emerged as an alternative to relational databases for managing large volumes of data. NoSQL document databases ensure big data storage and good query performance and are essential when the data scheme does not fit into the scheme of relational databases. They store their data in the form of documents and can handle unstructured, semi-structured, and structured data. This work evaluates the top three open-source NoSQL document databases: Couchbase, CouchDB, and MongoDB with Yahoo! Cloud Serving Benchmark (YCSB), which has become a standard for NoSQL database evaluation. The performance and scale-up of document databases are assessed using YCSB workloads with a different number of records and threads, where the runtime is measured for each database. In the experimental evaluation, we concluded that MongoDB is the database with the best runtime, except for the workload composed by scan operations. In addition, we identified CouchDB as the database with the best scale-up when varying the number of threads.
Journal Article