Structured Query Language Database Research Articles

IoT-based Machine Learning Comparative Models of Stream Water Parameters Forecasting for Freshwater Lobster

Water quality parameters such as dissolved oxygen, pH, and mineral content are important factors for aquaculture. Predictive analytics can predict water conditions in aquaculture and significantly reduce the mortality probability of aquaculture products. This paper applied stream predictive analytics to the freshwater lobster farming dataset where its real-time data supplied by End Node Unit (ENU) which integrated with dissolved oxygen (DO), potential hydrogen (pH), electrical conductivity (EC), and total dissolved solids (TDS). The real-time data of ENU in Structured Query Language (SQL) is normally displayed for remote monitoring and the analytics will only be done after in different processing platform called batch analytics. Instead of batch, this paper demonstrates capability of stream analytics where the real-time data query from ENU streaming through Structured Query Language (SQL) right into R Studio and Autoregressive Integrated Moving Average (ARIMA) predictions executed on the query table simultaneously on the same processing platform. Previously, ARIMA, Neural Network Autoregressive (NNETAR), and Naïve Bayes, were run and evaluated in R Studio to identify the best algorithm for stream analytics. Prediction procedure in R studio start with importing real-time data stored in SQL database and stream into R Studio using command of “dbGetQuery(con,sql)”. These three models evaluated the performance of freshwater lobster water conditions, dissolved oxygen (DO), potential hydrogen (pH), electrical conductivity (EC), and total dissolved solids (TDS). The data was collected for six months, and 70% was used as training data and 30% as test data. Compared to NNETAR and Naïve Bayes, ARIMA fits the entire data set well for 7 days; the ARIMA model exhibited lower absolute errors for pH and electrical conductivity, with errors ranging from 0.04 to 1.7 across days, while the NNETAR model had generally lower errors for TDS, with errors ranging from 0.3 to 0.7; however, the Naïve Bayes model's performance varied, with the lowest error for DO on day (5) 0.15 but higher errors for other parameters and days, including the highest error for electrical conductivity on day (6) 6.2. In conclusion, the average absolute errors for DO, pH, EC, and TDS are 0.163, 0.064, 0.705, and 0.498, respectively. Our findings underscore the efficacy of ARIMA for comprehensive water quality via stream prediction while highlighting the nuanced strengths and weaknesses of each model in forecasting specific parameters. This study contributes to the aquaculture literature by providing a nuanced comparative analysis of predictive models tailored to freshwater lobster farming, emphasizing the imperative role of stream predictive modelling. It enables real-time monitoring of water quality parameters, ensuring prompt interventions to maintain optimal conditions, thereby minimizing risks, enhancing aquaculture productivity, and ultimately contributing to sustainable and efficient freshwater lobster farming practices.

Concurrency versus consistency in NoSQL databases

<p>With the advent of cloud services, the proliferation of data has reached unprecedented levels. The load distribution across multiple servers, driven by web and mobile applications, has become a defining characteristic of contemporary data management. In contrast to this surge in data complexity, traditional relational databases have proven inadequate in handling vast amounts of unstructured data due to their inherent focus on structured data models. Additionally, the concept of clustering, vital for efficient unstructured data management, eluded relational databases, rendering them ill-equipped for customized clustering techniques and the optimal execution of queries. SQL (Structured Query Language) databases earlier emerged as a groundbreaking solution, introducing the relational database model that organized data into structured tables. They employed ACID (atomicity, consistency, isolation, durability) properties to maintain data integrity and enabled intricate querying through SQL. However, as applications grew in complexity, SQL databases encountered hurdles in handling various data types, rapid data expansion, and concurrent workloads. The limitations of SQL databases propelled the rise of NoSQL (Not Only Structured Query Language) databases, which prioritized adaptability, scalability, and performance. NoSQL databases embraced diverse data models such as documents, key-values, column families, and graphs, enabling effective management of structured, semi-structured, and unstructured data. The transition to NoSQL databases was justified by several factors; horizontally scaled across nodes, handling extensive read-write operations effectively, Agile development of accommodating changing data structures without schema constraints, optimization for specific tasks, providing low-latency access and high throughput, dynamic schemas aligned with modern iterative development, promoting adaptability, and adeptly managed diverse data types, spanning text, geospatial, time-series, and multimedia data. These databases are purposefully designed to accommodate the escalating demands of data storage. Notably, this data emanates from diverse nodes and is susceptible to concurrent access by numerous users. However, a critical challenge surfaces as the data present on one node may diverge from its counterpart on another node replica. In this context, the simultaneous execution of database operations, while preserving the integrity of the data, emerges as a pivotal concern. Maintaining data consistency amid concurrent access hinges upon the synchronization of operations across all replica nodes. Achieving this synchronization necessitates the adoption of a robust concurrency control technique. Concurrency control acts as the linchpin for upholding accuracy and reliability within a system where operations unfold concurrently. Hence, the focal point of this investigation lies in examining the assorted concurrency control methodologies employed by NoSQL systems. The objective is to dissect the intricate interplay between concurrency and consistency, shedding light on the strategies these systems employ to strike an optimal balance between the two. In summation, as the landscape of data management witnesses an era of exponential growth catalyzed by cloud services, the dynamics of load distribution and unstructured data have necessitated a departure from traditional relational databases. NoSQL databases have risen to the fore, demonstrating the ability to grapple with these challenges. However, the quest for concurrent data access without compromising data consistency propels the exploration of various concurrency control methods. The aim of this study is to look at some of the different concurrency control approaches employed by NoSQL systems, highlighting how they priorities concurrency and consistency.</p>

GBIF-Compliant Data Pipeline for the Management and Publication of a Global Taxonomic Reference List of Pests in Natural History Collections

There is a growing demand for monitoring pests in natural history collections (NHCs) and establishing integrated pest management (IPM) solutions (Crossman and Ryde 2022). In this context, up-to-date taxonomic reference lists and controlled vocabularies following standard schemes are crucial and facilitate recording organisms detected in collections. The data pipeline described here results in the publication of a taxon reference list based on information from online resources and standard IPM literature. Most of the over 140 pest taxa on species level and above are insects, the rest belong to other animal groups and fungi. The complete taxon names, synonyms, English and German common names, and the hierarchical classification (parent-child relationships) are organised in a client-server installation of DiversityTaxonNames (DTN) at the Bavarian Natural History Collections (SNSB). DTN is a Microsoft Structured Query Language (MS SQL) database tool of the Diversity Workbench (DWB) framework with a published Entity Relation (ER) diagram (Hagedorn et al. 2019). The management is done using the Global Biodiversity Information Facility (GBIF) backbone taxonomy as external name resource, with linkage to the respective Wikidata Q item ID as a external persistent identifier (PID). Moreover, information on pest occurrence in NHCs is given, distinguishing the Consortium of European Taxonomic Facilities (CETAF) major NHC collection types affected (i.e., heritage sciences, life sciences and earth sciences) and the object categories, e.g., natural objects/specimens damaged. The data management in DTN enables the long-running curation, done by list curators. The generic data pipeline for the management and publication of a Global Taxonomic Reference List of Pests in NHCs is based on the DTN taxon lists concept and architecture and described under About "Taxon list of pest organisms for IPM at natural history collections compiled at the SNSB". It includes four steps (A–D) with significant results for best practices of data processing (Fig. 1). A. The data is managed and processed for publication by list curators in the database DiversityTaxonNames (DTN). As a result, the list can be kept up-to-date and is—without transformation—ready to be used for IPM solutions at any NHC with a DiversityCollection installation and as part of the DWB cloud services. B. The up-to-date data is publicly available via the DTN REST Webservice for Taxon Lists with machine-readable Application Programming Interface (API). As a result, the dynamic list publication service can be used as a reference backbone for establishing IPM solutions for pest monitoring at any NHC. C. The data is provided via the GBIF checklist data publication pipeline of the SNSB through GBIF validation tools and Darwin Core Archive in DwC-A (zip format) for GBIF. As a result, the checklist information becomes part of the GBIF network with GBIF ChecklistBank and GBIF Global Taxonomy. This ensures future compliance of data with the Findability, Accessibility, Interoperability, and Reuse (FAIR) guiding principles. D. The DTN REST Web service for Taxon Lists (currently 60 lists) is registered and accessible through the German Federation for Biological Data (GFBio) Terminology service. As a result, the lists with external PIDs and other information are available as a service (see DTN lists overview). In the upcoming Research Data Commons of the German National Research Data Infrastructure (NFDI) Initiative (Diepenbroek et al. 2021), it will be part of a standardized layer of APIs with an agreed interface scheme for improved accessibility. The provided tools, API and data are part of the upcoming NFDI4Biodiversity service portfolio. Future scenarios include the use of the list items and properties as classes for diagnosis purposes with DiversityNaviKey (Triebel et al. 2021) including the publication of images for identifying pests.

A mental model approach to teaching database querying skills with SQL and Alteryx

This paper describes an instructional approach and exercises to introduce a data analytics tool into an existing course that covers relational databases and structured query language (SQL). SQL and relational databases are traditional topics in many AIS classes and are frequently taught with established technologies such as Microsoft Access. With the onset of newer data analytics tools, educators are faced with the challenge of incorporating emerging technologies while still covering basic foundational concepts. In this paper, we position SQL as the mental model for learning the data preparation and data blending features of a popular analytics tool, Alteryx. We split the traditional SQL content from our course into four increments that increase in complexity. For each increment, we use a multi-step approach to first develop students’ mental model of the SQL query code and then apply the mental model to the new environment of Alteryx. For each increment, students solve the same set of problems in SQL and Alteryx, so that the understanding of SQL eases the transition to Alteryx, and the practice in Alteryx reinforces the understanding of SQL.

Scalable Analysis of Untargeted LC-HRMS Data by Means of SQL Database Archiving.

Liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is widely used to detect chemicals with a broad range of physiochemical properties in complex biological samples. However, the current data analysis strategies are not sufficiently scalable because of data complexity and amplitude. In this article, we report a novel data analysis strategy for HRMS data founded on structured query language database archiving. A database called ScreenDB was populated with parsed untargeted LC-HRMS data after peak deconvolution from forensic drug screening data. The data were acquired using the same analytical method over 8 years. ScreenDB currently holds data from around 40,000 data files, including forensic cases and quality control samples that can be readily sliced and diced across data layers. Long-term monitoring of system performance, retrospective data analysis for new targets, and identification of alternative analytical targets for poorly ionized analytes are examples of ScreenDB applications. These examples demonstrate that ScreenDB makes a significant improvement to forensic services and that the concept has potential for broad applications for all large-scale biomonitoring projects that rely on untargeted LC-HRMS data.

Effective Two-Lane Traffic Management at the University of Ibadan, Nigeria Main Gate Using Multiple Vehicle Recognition Systems

Traffic congestion and management have posed a major challenge to many cities in the world. Intelligent traffic management system plays an important role in monitoring and enforcing traffic laws with reduced labor. This paper uses vehicle information recognition to identify unpermitted lane shunting at the University of Ibadan main gate. The vehicle recognition system captures three main details of the vehicle; its license plate, make, and colour to ensure the system, which is named UiScope, is robust enough. Machine learning and deep learning algorithms including Convolutional Neural Network (CNN), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) algorithms are used to train classifiers for vehicle make, license plate, and colour recognition. The captured details are uploaded on a Structured Query Language (SQL) database to create a blacklist of vehicles that are shunted. The querying of the database is used to determine the shunted vehicle. The success rate for plate identification is 92%, character segmentation is 87%, character recognition is 75%, and vehicle colour recognition is 78%.

The refined management of medical finance combined with information technology construction

AbstractBased on the information construction and taking a hospital as an example, this paper analyzes the current situation, existing problems, and reasons for financial management. This paper puts forward the scheme of optimizing hospital financial management and points out that accounting, asset management, cost management, and performance are all based on accounting. Even though with the continuous improvement and development of hospital informatization, various infrastructures have been built, the problems are more prominent. This can be solved by improving the focus on the products, and processes by reducing waste, or by increasing the quality. In hospitals, the core layer of hospital information technology is the financial management construction and development, and it is also an indispensable factor. It is better to improve the hospital's financial fine management level and promote the hospital's sustainable development. It is not enough to improve the assessment management and comprehensive budget management. It is necessary to integrate the perfect Health Information System, enterprise resource planning system, scientific research system, and structured query language database, establish a comprehensive financial management platform, and obtain data information and indicators of various economic operations in hospitals. Experiments show that the consumables sorting optimization algorithm in this paper can improve the equipment management of medical finance to some extent. In other aspects, this system also incorporates information technology to make enhancements to the original financial management.

Design and development of embedded/SQL home based upper limb rehabilitation

ABSTRACT Rehabilitation systems are devices for rehabilitating the wounded limbs of patients in place of therapies. Innovative needs for new technological applications in areas like healthcare care and rehabilitation have arisen as a result of changes in global population patterns. Recent advancements in robotics for upper-limb rehabilitations have produced a slew of data demonstrating its use as a supplement to traditional physiotherapies. One main attraction of these technologies is their ability to install in-house rehabilitation tools that allow individuals to take control of their therapies more regularly using these technologies. Specialized user interfaces provide patient benefits with workout feedback as well as inputs from healthcare practitioners. These specialized interfaces allow healthcare professionals to view involved patients’ exercise routine performance charts. The product includes mechanical parts, sensors and motor drives, databases, and interactive interfaces. The mechanical design of these devices uses 3DPs (3 Dimensional Printing) on key components and implementation controls are obtained by DSPIC30F4011 and stepper motors. Healthcare professionals and patients interface using windows-based apps developed in C# with Visual Studio while patient performance data is stored in SQL (Structured Query Language) Databases and services are completely managed database services that can be deployed in cloud-native applications.

Development of a Knowledge-sharing Website for Hypersensitivity Pneumonitis Exposures.

BackgroundIdentification of potential inciting antigens known to be associated with hypersensitivity pneumonitis (HP) is integral to achieving a confident diagnosis, its management, and prognostication. A barrier to achieving diagnostic confidence is that evidence for exposure assessment is not presented in an easily accessible or useful format.ObjectiveTo develop a freely available comprehensive living repository of contemporaneous exposures associated with HP.MethodsElectronic searches were performed through Medline, Embase, and Web of Science from January 1, 2002, to December 31, 2019, using the following broad MeSH headings: ([allerg* or hypersensitiv*], and adj [alveol* or pneumo*]).mp. Articles were included if they described adults or children with a diagnosis of HP (as defined by authors) and a description of the causative exposure. Website development used a structured query language database and was powered by WordPress.ResultsHPLung.com is a freely available online searchable repository that lists all exposures and antigens associated with HP and links to their parent studies. Eighty-five unique exposures from 447 citations have been included thus far. The website continues to be updated iteratively. Since its launch in 2019, HPLung.com has been visited over 4,500 times by users from 75 different countries.ConclusionHPLung.com is a novel knowledge-sharing tool that addresses barriers of accessibility, contemporaneity, efficient evidence synthesis, and making the best use of technological platforms to improve the exposure assessment of those suspected of HP.

Dynamic Data Infrastructure Security for Interoperable e-Healthcare Systems: A Semantic Feature-Driven NoSQL Intrusion Attack Detection Model.

The exponential rise in advanced software computing and low-cost hardware has broadened the horizon for the Internet of Medical Things (IoMT), interoperable e-Healthcare systems serving varied purposes including electronic healthcare records (EHRs) and telemedicine. However, being heterogeneous and dynamic in nature, their database security remains a challenge forever. Numerous intrusion attacks including bot-attack and malware have confined major classical databases towards e-Healthcare. Despite the robustness of NoSQL over the structured query language databases, the dynamic data nature over a heterogeneous environment makes it vulnerable to intrusion attacks, especially over interoperable e-Healthcare systems. Considering these challenges, this work proposed a first of its kind semantic feature-driven NoSQL intrusion attack (NoSQL-IA) detection model for interoperable e-Healthcare systems. This work assessed the efficacy of the different semantic feature-extraction methods like Word2Vec, Continuous Bag of Words, N-Skip Gram (SKG), Count Vectorizer, TF-IDF, and GLOVE towards NoSQL-IA prediction. Subsequently, to minimize computational exhaustion, different feature selection methods including Wilcoxon Rank Sum Test (WRST), significant predictor test, principal component analysis, Select K-Best, and variance threshold feature selection algorithms were employed. To alleviate the data imbalance problem, it applied different resampling methods including upsampling, downsampling, and synthetic minority oversampling technique (SMOTE) over the selected features. Later, Min–Max normalization was performed over the input feature vectors to alleviate any possibility of overfitting. Towards NoSQL-IA prediction, different machine learning methods like Multinomial Naïve Bayes, decision tree, logistic regression, support vector machine, k-NN, AdaBoost, Extra Tree Classifier, random forest ensemble, and XG-Boost were applied, which classified each input query as the regular query or the NoSQL-IA attack query. The depth performance assessment revealed that the use of Word2Vec features SKG in sync with VTFS feature selection and SMOTE resampling processed with the bootstrapped random forest classifier can provide the best performance in terms of high accuracy (98.86%), F-Measure (0.974), and area under the curve (AUC) (0.981), thus enabling it suitable for interoperable e-Healthcare database security.

LinkedImm: a linked data graph database for integrating immunological data

BackgroundMany systems biology studies leverage the integration of multiple data types (across different data sources) to offer a more comprehensive view of the biological system being studied. While SQL (Structured Query Language) databases are popular in the biomedical domain, NoSQL database technologies have been used as a more relationship-based, flexible and scalable method of data integration.ResultsWe have created a graph database integrating data from multiple sources. In addition to using a graph-based query language (Cypher) for data retrieval, we have developed a web-based dashboard that allows users to easily browse and plot data without the need to learn Cypher. We have also implemented a visual graph query interface for users to browse graph data. Finally, we have built a prototype to allow the user to query the graph database in natural language.ConclusionWe have demonstrated the feasibility and flexibility of using a graph database for storing and querying immunological data with complex biological relationships. Querying a graph database through such relationships has the potential to discover novel relationships among heterogeneous biological data and metadata.

Rapid Response to Drive COVID-19 Research in a Learning Health Care System: Rationale and Design of the Houston Methodist COVID-19 Surveillance and Outcomes Registry (CURATOR).

BackgroundThe COVID-19 pandemic has exacerbated the challenges of meaningful health care digitization. The need for rapid yet validated decision-making requires robust data infrastructure. Organizations with a focus on learning health care (LHC) systems tend to adapt better to rapidly evolving data needs. Few studies have demonstrated a successful implementation of data digitization principles in an LHC context across health care systems during the COVID-19 pandemic.ObjectiveWe share our experience and provide a framework for assembling and organizing multidisciplinary resources, structuring and regulating research needs, and developing a single source of truth (SSoT) for COVID-19 research by applying fundamental principles of health care digitization, in the context of LHC systems across a complex health care organization.MethodsHouston Methodist (HM) comprises eight tertiary care hospitals and an expansive primary care network across Greater Houston, Texas. During the early phase of the pandemic, institutional leadership envisioned the need to streamline COVID-19 research and established the retrospective research task force (RRTF). We describe an account of the structure, functioning, and productivity of the RRTF. We further elucidate the technical and structural details of a comprehensive data repository—the HM COVID-19 Surveillance and Outcomes Registry (CURATOR). We particularly highlight how CURATOR conforms to standard health care digitization principles in the LHC context.ResultsThe HM COVID-19 RRTF comprises expertise in epidemiology, health systems, clinical domains, data sciences, information technology, and research regulation. The RRTF initially convened in March 2020 to prioritize and streamline COVID-19 observational research; to date, it has reviewed over 60 protocols and made recommendations to the institutional review board (IRB). The RRTF also established the charter for CURATOR, which in itself was IRB-approved in April 2020. CURATOR is a relational structured query language database that is directly populated with data from electronic health records, via largely automated extract, transform, and load procedures. The CURATOR design enables longitudinal tracking of COVID-19 cases and controls before and after COVID-19 testing. CURATOR has been set up following the SSoT principle and is harmonized across other COVID-19 data sources. CURATOR eliminates data silos by leveraging unique and disparate big data sources for COVID-19 research and provides a platform to capitalize on institutional investment in cloud computing. It currently hosts deeply phenotyped sociodemographic, clinical, and outcomes data of approximately 200,000 individuals tested for COVID-19. It supports more than 30 IRB-approved protocols across several clinical domains and has generated numerous publications from its core and associated data sources.ConclusionsA data-driven decision-making strategy is paramount to the success of health care organizations. Investment in cross-disciplinary expertise, health care technology, and leadership commitment are key ingredients to foster an LHC system. Such systems can mitigate the effects of ongoing and future health care catastrophes by providing timely and validated decision support.

MeteoRead: Client Software for Inserting Observed Atmospheric Data into MySQL<sub>TM</sub> Database and Downloading them into Different File Format

The investigation of the tendency of climate change and its effects on ecology, economy and sociology is essential for long term policy making. The long-term measurement of the physical and chemical properties of the atmosphere with state-of-the-arts instruments provides high-quality data for these studies. The evaluated data are stored in really special file structures and formats that cannot be inserted in one common database. Moreover, the observed data usually available in ASCII format and the users sometimes need to convert them in other format. The file conversion is usually time consuming procedure and can contribute to the uncertainties. MeteoRead is a client database software that imports the observed atmospheric data e.g. wind direction, wind speed, aerosol particle concentration etc. and makes them available in different file formats, which are most commonly used in climate research. This JavaTM based program applies the Structured Query Language (SQL) functions such as table creation on a database server, data or figures insertion into the table and data selection via Graphical User Interface. The selected data can be stored in NetCDF, HDF5, DataBase or TXT file formats and the figures can be available in PNG, JPG, JPNG, PDF or GIF file formats. The program was tested on Linux and Windows platforms with different JavaTM Development Kit. The MeteoRead is planned to be developed to visualizing the annual, seasonal, monthly, daily or hourly average value of the selected data and to use the functionality of the SQL database to calculate various mathematical and statistical correlations.

EPID-07. ACCESS TO GLIOBLASTOMA CLINICAL TRIALS FOR RURAL PATIENTS IN THE UNITED STATES FROM 2010–2020

Abstract BACKGROUND Patients in rural communities have less access to optimal cancer care and clinical trials. For GBM, access to experimental therapies, and consideration of a clinical trial is embedded in national guidelines. Still, the availability of clinical trials to rural communities, representing 20% of the US population, has not been described. METHODS We queried ClinicalTrials.gov for glioblastoma interventional treatment trials opened between 1/2010 and 1/2020 in the United States. We created a Structured Query Language database and leveraged Google application programming interfaces (API) Places to find name and street addresses for the sites, and Google’s Geocode API to determine the county location. Counties were classified by US Department of Agriculture Rural-Urban Continuum Codes (RUCC 1–3 = urban and RUCC 4–9 = rural). We used z-ratios for rural-urban statistical comparisons. RESULTS We identified 406 interventional treatment trials for GBM at 1491 unique sites. 8.7% of unique sites were in rural settings. Rural sites opened an average of 1.7 trials/site and urban sites 2.8 trials/site from 1/2010–1/2020. Rural sites offered more phase II trials (63% vs 57%, p= 0.03) and fewer phase I trials (22% vs 28%, p= 0.01) than urban sites. Rural locations were more likely to offer federally-sponsored trials (p< 0.002). There were no investigator-initiated or single-institution trials offered at rural locations, and only 1% of industry trials were offered rurally. DISCUSSION Clinical trials for GBM were rarely open in rural areas, and were more dependent on federal funding. Clinical trials are likely difficult to access for rural patients, and this has important implications for the generalizability of research as well as how we engage the field of neuro-oncology and patient advocacy groups in improving patient access to trials. Increasing the number of clinical trials in rural locations may enable more rural patients to access and enroll in GBM studies.

SciServer: A science platform for astronomy and beyond

We present SciServer, a science platform built and supported by the Institute for Data Intensive Engineering and Science at the Johns Hopkins University. SciServer builds upon and extends the SkyServer system of server-side tools that introduced the astronomical community to SQL (Structured Query Language) and has been serving the Sloan Digital Sky Survey catalog data to the public. SciServer uses a Docker/VM based architecture to provide interactive and batch mode server-side analysis with scripting languages like Python and R in various environments including Jupyter (notebooks), RStudio and command-line in addition to traditional SQL-based data analysis. Users have access to private file storage as well as personal SQL database space. A flexible resource access control system allows users to share their resources with collaborators, a feature that has also been very useful in classroom environments. All these services, wrapped in a layer of REST APIs, constitute a scalable collaborative data-driven science platform that is attractive to science disciplines beyond astronomy.

Improving the performance of big data databases

Real-time monitoring systems utilize two types of database, they are relational databases such as MySQL and non-relational databases such as MongoDB. A relational database management system (RDBMS) stores data in a structured format using rows and columns. It is relational because the values of the tables are connected. A non-relational database is a database that does not adopt the relational structure given by traditional. In recent years, this class of databases has also been referred to as Not only SQL (NoSQL). This paper discusses many comparisons that have been conducted on the execution time performance of types of databases (SQL and NoSQL). In SQL (Structured Query Language) databases different algorithms are used for inserting and updating data, such as indexing, bulk insert and multiple updating. However, in NoSQL different algorithms are used for inserting and updating operations such as default-indexing, batch insert, multiple updating and pipeline aggregation. As a result, firstly compared with related papers, this paper shows that the performance of both SQL and NoSQL can be improved. Secondly, performance can be dramatically improved for inserting and updating operations in the NoSQL database compared to the SQL database. To demonstrate the performance of the different algorithms for entering and updating data in SQL and NoSQL, this paper focuses on a different number of data sets and different performance results. The SQL part of the paper is conducted on 50,000 records to 3,000,000 records, while the NoSQL part of the paper is conducted on 50,000 to 16,000,000 documents (2GB) for NoSQL. In SQL, three million records are inserted within 606.53 seconds, while in NoSQL this number of documents is inserted within 67.87 seconds. For updating data, in SQL 300,000 records are updated within 271.17 seconds, while for NoSQL this number of documents is updated within just 46.02 seconds.

Online Road Accident Victim Identification System in Nigeria

Road accident is an unexpected event that needs a prompt action to save victims lives. Developing countries like Nigeria, does not have a standardized method of identifying victims of accident in order to give immediate needed attention. This research work is aimed at developing an online identification system for easy and immediate identification of road accident victims in Nigeria, using finger print biometrics. Most often, unconscious accident victims die as a result of not receiving immediate and progressive medical attention which is caused by difficulty in identifying the victims. An online biometric identification system is designed and implemented. The methodology employed to model the system is Object Oriented Analysis and Design Methodology (OOADM). Visual Basic.NET is used to implement the system and Microsoft Structured Query Language database is used for the development of the database. The system will assist the law enforcement agencies and other organizations identify accident victims both in their conscious and unconscious state. Also, the system will use the global system for mobile communication technology to send short message to the victims’ relative informing them of the accident and the victim’s hospital location for quick response in order to save the lives of the victims.

A linked data graph approach to integration of immunological data.

Systems biology involves the integration of multiple data types (across different data sources) to offer a more complete picture of the biological system being studied. While many existing biological databases are implemented using the traditional SQL (Structured Query Language) database technology, NoSQL database technologies have been explored as a more relationship-based, flexible and scalable method of data integration. In this paper, we describe how to use the Neo4J graph database to integrate a variety of types of data sets in the context of systems vaccinology. Specifically, we have converted into a common graph model diverse types of vaccine response measurement data from the NIH/NIAID ImmPort data repository, pathway data from Reactome, influenza virus strains from WHO, and taxonomic data from NCBI Taxon. While Neo4J provides a graph-based query language (Cypher) for data retrieval, we develop a web-based dashboard for users to easily browse and visualize data without the need to learn Cypher. In addition, we have prototyped a natural language query interface for users to interact with our system. In conclusion, we demonstrate the feasibility of using a graph-based database for storing and querying immunological data with complex biological relationships. Querying a graph database through such relationships has the potential to reveal novel relationships among heterogeneous biological data.

Influence of Network Speed on Structured Query Language (SQL) Database Data Transmission Performance in an Internet of Things (IoT) Sensing Device on Single Board Computers (SBC)

One of the crucial elements in an Internet of Things (IoT) environment is a database. IoT performance will be at stake if the wrong database is adopted. In this research, three Structured Query Language (SQL) databases were tested against multiple network speeds in an IoT device. Single board computers (SBC) were used as a media of testing instead of an ordinary computer. A controlled wireless sensor network (WSN) was developed with several uniform constants and variable network speeds. A new data transmission rate equation also was proposed and tested. From the result, it was found that not all SQL databases can cater IoT devices. Some of them showed that performance decreased along with network speed. The correlation, between 0.90 and 0.96, proved the strong influence between the research subjects. In addition, file-type database is the best option available for SQL-wise IoT storage.

Detection and Regulation of Soil Moisture and Nutrients Using Cloud Computing and Internet of Things in Agriculture

Deficiency in fresh water resources globally has been a serious problem in the last decade. For overcoming this major drawback in the irrigation systems, smart soil monitoring system is implemented. The paper focuses on detection of moisture and nutrient levels present within the soil using moisture sensors and electrochemical sensors. These sensors are connected to Arduino board which controls the automatic water supply system which operates automatically based on the signal received from the Arduino board. Data received from Arduino board is displayed on a Liquid Crystal Display screen. The data collected will be stored in a cloud via an Internet of Things gateway. The system will also suggest the kinds of crops, from the list of crops stored in an Structured Query Language database, that will be best suitable for that soil based on the levels of soil moisture and nutrients detected.