RESTful Application Programming Interface Research Articles

Implementasi REST API Web Services pada Aplikasi Sumber Daya Manusia

Human resources management is a vital component within any organization, serving as a valuable asset and a driving force. However, many companies face challenges when it comes to managing employee data, processing leave requests, and generating accurate salary reports, often relying on cumbersome manual processes. This research aims to address these challenges by implementing a REST API (Representational State Transfer Application Programming Interface) in a web-based human resources application. The REST API is carefully built using the Laravel framework, adhering to REST architecture principles to ensure security during internet-based information exchange. The development methodology employed for this application is the waterfall method, a sequential and structured approach to software development. Through these enhancements, this application is expected to significantly assist HR departments in efficiently managing employee data, expediting leave request processing, and simplifying the generation of comprehensive reports. The anticipated outcomes of this research are expected to equip HR professionals with the necessary tools to efficiently navigate these intricate tasks, ultimately enhancing organizational efficiency and effectiveness.

Biometric Authentication based on Liveness Detection Using Face Landmarks and Deep Learning Model

This paper describes the approach to active liveness detection of the face using facial features and movements. The project aims to create a better method for detecting liveness in real-time on an application programming interface (API) server. The project is built using Python programming with the computer vision libraries OpenCV, dlib and MediaPipe and the deep learning library Tensorflow. There are five modules in active liveness detection progress related to different parts or movements on the face: headshakes, nodding, eye blinks, smiles, and mouths. The functionality of modules runs through face landmarking through dlib and MediaPipe and detection of face features through Tensorflow Convolutional Neural Network (CNN) trained in two different approaches: smile detection and eye-blink detection. The result of implementing face landmarking shows an accurate result through the pre-trained model of MediaPipe and the pre-trained parameter of the dlib 68 landmarking model. And more than 90% classification model accuracy in precision, recall, and f1-score for both trained CNNs in detecting smiles and eyes blinking through the Scikit-Learn classification report. In addition, the prototype API is also implemented using the Python RESTful API library, FastAPI, to test the detection functionality in the prototype Android application. The prototype result is outstanding, as the model excellently requests and retrieves from the API server. The possible research path gives the success of real-time detection on API servers for easy implementation of liveness detection on low-spec client devices.

Optimal dwelling time prediction for package tour using K‐nearest neighbor classification algorithm

AbstractWe introduce a machine learning‐based web application to help travel agents plan a package tour schedule. K‐nearest neighbor (KNN) classification predicts the optimal tourists' dwelling time based on a variety of information to automatically generate a convenient tour schedule. A database collected in collaboration with an established travel agency is fed into the KNN algorithm implemented in the Python language, and the predicted dwelling times are sent to the web application via a RESTful application programming interface provided by the Flask framework. The web application displays a page in which the agents can configure the initial data and predict the optimal dwelling time and automatically update the tour schedule. After conducting a performance evaluation by simulating a scenario on a computer running the Windows operating system, the average response time was 1.762 s, and the prediction consistency was 100% over 100 iterations.

Rotation Error Detection of Gallium Nitride (GaN) Substrate in MBE Utilizing Ensemble Learning

A rotating substrate is essential for achieving high-quality and uniform growth of gallium nitride (GaN) thin films in molecular beam epitaxy (MBE). By rotating the substrate during growth, the deposited material can be distributed more uniformly across the surface, resulting in a more uniform and defect-free film. However, the substrate may stop rotating during fabrication, and it is a challenge for engineers to detect the rotation of the substrate using information from reflection high-energy electron diffraction (RHEED). The current method for analyzing rotation using RHEED data is manual, time-consuming, and error-prone. Therefore, this research proposes an automated machine learning-based rotation error detection (RED) model to determine wafer rotation and then alert the engineers in case of an anomaly. The proposed approach consists of (1) ensemble learning models such as random forest classifier and extreme gradient boosting (XGBoost) for rotation error detection, (2) prepared novel intensity data from RHEED videos, (3) comparison to select the best ensemble learning model at a small scale, (4) training XGBoost at a large scale, (5) deployment in production using MLflow, (6) development of a REST application programming interface (API) for inference, and (7) integrated KakaoTalk API to generate and send a detailed alert message to engineers so that they can take appropriate actions in time. The experimental results achieved F1 scores for normal and rotational errors of 98.9 and 83.1%, respectively. The proposed method can make a valuable contribution to epitaxial wafer growth by enhancing production efficiency, improving product quality, and reducing costs.

Abstract 5377: An integrative prognostic machine learning model in mantle cell lymphoma

Abstract Background: Mantle cell lymphoma (MCL) is an uncommon B-cell lymphoma. The clinical course is highly variable: some patients have aggressive disease and relapse after treatment, while others have indolent disease or respond exceptionally to frontline therapy. Prognostication of MCL patients is dynamic and continues to evolve as novel therapies develop. Current prognostic indicators, such as the MCL international prognostic index (MIPI), were primarily designed with patients treated with chemo-immunotherapies. Using machine learning (ML) and molecular data, we provide a novel predictive method to improve upon conventional clinical markers. Methods: We studied 785 MCL patients diagnosed at MD Anderson since 2014 and retrospectively classified them as “aggressive MCL” (n=311): relapsed or refractory to frontline treatment, and “mild MCL” (n=474): those who did not relapse after the first treatment (exceptional response) or had indolent disease never requiring treatment. After data extraction and feature engineering, 195 baseline features comprised of clinical, genomic, pathology, and cytogenetic data were integrated into an extreme gradient-boosted ensemble ML model (XGBoost). The dataset containing all patients was split (75/25) into a training and test set. Hyperparameters for the model were tuned using a grid-based, space-filling (Latin hypercube) technique and resampled 10-fold cross-validation from the training set. Training, validation, and testing sets were split using stratification of the classification variable to avoid class imbalance. Results: Our integrative model achieved area under the curve (AUC) = .82 and accuracy = .76 on the test set and outperformed an XGBoost model using only clinical features (AUC = .78, accuracy = .68). Additionally, the fully integrated model improved on metrics from a similar multivariate logistic model including all patients (AUC = .72, accuracy =.72). Univariate logistic models were fit on the classification variable using the MIPI and other prognostic indices. The integrated ML model significantly outperformed the MIPI (AUC = .62, accuracy = .60) and other indices in predicting patient class. Clinical, pathological, cytogenetic, and genomic data were all represented as impactful features in a variable importance plot (VIP) and Shapley (SHAP) additive values of the fully integrated ML model. This model was used to launch a rest application programming interface (API) in which important features could be entered and a prediction returned. Conclusion: Our study demonstrates that the current paradigm of using limited features in disease prognostics should be replaced with more advanced ML models that utilize genomic and other molecular data. Future work will include expanding the features included in the model and using the rest API to construct a graphical user interface accessible to clinicians and other researchers to make treatment decisions in precision oncology. Citation Format: Holly A. Hill, Preetesh Jain, Michael L. Wang, Ken Chen. An integrative prognostic machine learning model in mantle cell lymphoma. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5377.

Unique Device Identification–Based Linkage of Hierarchically Accessible Data Domains in Prospective Surgical Hospital Data Ecosystems: User-Centered Design Approach

The electronic health record (EHR) targets systematized collection of patient-specific, electronically stored health data. The EHR is an evolving concept driven by ongoing developments and open or unclear legal issues concerning medical technologies, cross-domain data integration, and unclear access roles. Consequently, an interdisciplinary discourse based on representative pilot scenarios is required to connect previously unconnected domains. We address cross-domain data integration including access control using the specific example of a unique device identification (UDI)-expanded hip implant. In fact, the integration of technical focus data into the hospital information system (HIS) is considered based on surgically relevant information. Moreover, the acquisition of social focus data based on mobile health (mHealth) is addressed, covering data integration and networking with therapeutic intervention and acute diagnostics data. In addition to the additive manufacturing of a hip implant with the integration of a UDI, we built a database that combines database technology and a wrapper layer known from extract, transform, load systems and brings it into a SQL database, WEB application programming interface (API) layer (back end), interface layer (rest API), and front end. It also provides semantic integration through connection mechanisms between data elements. A hip implant is approached by design, production, and verification while linking operation-relevant specifics like implant-bone fit by merging patient-specific image material (computed tomography, magnetic resonance imaging, or a biomodel) and the digital implant twin for well-founded selection pairing. This decision-facilitating linkage, which improves surgical planning, relates to patient-specific postoperative influencing factors during the healing phase. A unique product identification approach is presented, allowing a postoperative read-out with state-of-the-art hospital technology while enabling future access scenarios for patient and implant data. The latter was considered from the manufacturing perspective using the process manufacturing chain for a (patient-specific) implant to identify quality-relevant data for later access. In addition, sensor concepts were identified to use to monitor the patient-implant interaction during the healing phase using wearables, for example. A data aggregation and integration concept for heterogeneous data sources from the considered focus domains is also presented. Finally, a hierarchical data access concept is shown, protecting sensitive patient data from misuse using existing scenarios. Personalized medicine requires cross-domain linkage of data, which, in turn, require an appropriate data infrastructure and adequate hierarchical data access solutions in a shared and federated data space. The hip implant is used as an example for the usefulness of cross-domain data linkage since it bundles social, medical, and technical aspects of the implantation. It is necessary to open existing databases using interfaces for secure integration of data from end devices and to assure availability through suitable access models while guaranteeing long-term, independent data persistence. A suitable strategy requires the combination of technical solutions from the areas of identity and trust, federated data storage, cryptographic procedures, and software engineering as well as organizational changes.

Data storage model in low-cost mobile applications

<span lang="EN-US">Mobile applications that have data transactions between users require a database relational database management system (RDBMS) and RESTful API operating on the hosting service so that all users can access the data. Renting a hosting service is not cheap and creating a RESTful API takes plenty of time. As an alternative to hosting, a free version of the Cloud Firestore service gives full access rights to the database and has an application programming interface (API) to manage data or access data. However, the free version of Cloud Firestore has limitations in terms of storage capacity, read, write, and delete processes. Therefore, redesigning process of the database was carried out into a low-cost version of the database model consisting of SQLite database and a low-cost version of the NoSQL database to overcome this problem. The goal is to reduce storage space usage and read, write, and delete processing on Cloud Firestore. The low-cost version of the database was tested with 6,030 data. The results obtained were savings of 47.27% storage usage, 83.08% write usage, 91.26% read process usage, and 83.19% delete process usage compared to the test results of the relational database model.</span>

Fast Healthcare Interoperability Resources for Inpatient Deterioration Detection With Time-Series Vital Signs: Design and Implementation Study.

BackgroundVital signs have been widely adopted in in-hospital cardiac arrest (IHCA) assessment, which plays an important role in inpatient deterioration detection. As the number of early warning systems and artificial intelligence applications increases, health care information exchange and interoperability are becoming more complex and difficult. Although Health Level 7 Fast Healthcare Interoperability Resources (FHIR) have already developed a vital signs profile, it is not sufficient to support IHCA applications or machine learning–based models.ObjectiveIn this paper, for IHCA instances with vital signs, we define a new implementation guide that includes data mapping, a system architecture, a workflow, and FHIR applications.MethodsWe interviewed 10 experts regarding health care system integration and defined an implementation guide. We then developed the FHIR Extract Transform Load to map data to FHIR resources. We also integrated an early warning system and machine learning pipeline.ResultsThe study data set includes electronic health records of adult inpatients who visited the En-Chu-Kong hospital. Medical staff regularly measured these vital signs at least 2 to 3 times per day during the day, night, and early morning. We used pseudonymization to protect patient privacy. Then, we converted the vital signs to FHIR observations in the JSON format using the FHIR Extract Transform Load application. The measured vital signs include systolic blood pressure, diastolic blood pressure, heart rate, respiratory rate, and body temperature. According to clinical requirements, we also extracted the electronic health record information to the FHIR server. Finally, we integrated an early warning system and machine learning pipeline using the FHIR RESTful application programming interface.ConclusionsWe successfully demonstrated a process that standardizes health care information for inpatient deterioration detection using vital signs. Based on the FHIR definition, we also provided an implementation guide that includes data mapping, an integration process, and IHCA assessment using vital signs. We also proposed a clarifying system architecture and possible workflows. Based on FHIR, we integrated the 3 different systems in 1 dashboard system, which can effectively solve the complexity of the system in the medical staff workflow.

Enabling Community Curation of Biological Source Annotations of Molecular Data Through PlutoF and the ELIXIR Contextual Data Clearinghouse

The advancements in sequencing technologies have greatly contributed to the documentation of Earth’s biodiversity. However, for exploring the full potential of molecular resources for biodiversity, there needs to be a good linkage between sequence data and its biological source, contributing to a network of connected data in the biodiversity research cycle. This requires a foundation of well-structured and accessible annotations in the molecular sequence repositories. The International Nucleotide Sequence Database Collaboration (INSDC), of which the European Nucleotide Archive (ENA) is its European node, holds a large amount of annotations associated with sequence data, relating to its biological source (e.g., specimens in natural history collections). However, for a number of records, these annotations may be incomplete (e.g., missing voucher information), ambiguous or even inaccurate. Therefore, we have implemented a workflow that allows third-party annotations to be attached to sequence and sample records using two existing services, the PlutoF platform and the ELIXIR Contextual Data ClearingHouse. This work was developed within the scope of the BiCIKL (Biodiversity Community Integrated Knowledge Library) project, which aims to establish open science practices in the biodiversity domain. PlutoF is an online data management platform that also provides computing services for biology-related research. PlutoF features allow registered users to enter their own data and access public data at INSDC. Users can enter and manage a range of data, as taxonomic classifications, occurrences, etc. This platform also includes a module that allows the addition of third-party annotations (on material source, taxonomic identification, etc.) linked to specimens or sequence records. This module was already in use by the UNITE community for annotation of INSDC rDNA Internal Transcribed Spacer sequence datasets (Abarenkov et al. 2021). These UNITE annotations are displayed in the National Centre for Biotechnology Information (NCBI) records through links to the PlutoF platform. However, there was the need for an automated solution that allowed third-party annotations to any sequence or sample record at INSDC. This was implemented through the operation of the ELIXIR Contextual Data ClearingHouse (hereafter as Clearinghouse). The Clearinghouse holds a simple RESTful Application Programming Interface (API) to support the submission of additions and improvements to current metadata attributes, such as information on material sources, on records publicly available in the ELIXIR data resources. The Clearinghouse enables the submission of these corrected metadata from databases (such as the PlutoF platform) to the primary data repositories. The workflow developed is shown in Fig. 1 and consists of the following steps: i) users annotate sequence metadata that is regularly downloaded from INSDC using NCBI’s E-utilities; ii) an annotation proposal is created and a verification notification is sent to an assigned reviewer; iii) the reviewer evaluates the annotation proposal and accepts it or rejects it with comments; iv) if the annotation proposal is accepted, the annotated fields that may be mapped to ENA fields are then pushed to the Clearinghouse using their RESTful API. The annotations when received at ENA are then reviewed before being displayed. This workflow is implemented through a web interface in PlutoF, which allows user-friendly and effortless reporting of corrections or additions to biological source metadata in sequence records. Overall, we expect this tool to contribute to the enrichment of metadata associated with sequence records, and therefore increase the links between the molecular and biodiversity resources, and enable sequencing data to deliver their full potential for biodiversity conservation.

MiniDeep: A Standalone AI-Edge Platform with a Deep Learning-Based MINI-PC and AI-QSR System

In this paper, we present a new AI (Artificial Intelligence) edge platform, called “MiniDeep”, which provides a standalone deep learning platform based on the cloud-edge architecture. This AI-Edge platform provides developers with a whole deep learning development environment to set up their deep learning life cycle processes, such as model training, model evaluation, model deployment, model inference, ground truth collecting, data pre-processing, and training data management. To the best of our knowledge, such a whole deep learning development environment has not been built before. MiniDeep uses Amazon Web Services (AWS) as the backend platform of a deep learning tuning management model. In the edge device, the OpenVino enables deep learning inference acceleration at the edge. To perform a deep learning life cycle job, MiniDeep proposes a mini deep life cycle (MDLC) system which is composed of several microservices from the cloud to the edge. MiniDeep provides Train Job Creator (TJC) for training dataset management and the models’ training schedule and Model Packager (MP) for model package management. All of them are based on several AWS cloud services. On the edge device, MiniDeep provides Inference Handler (IH) to handle deep learning inference by hosting RESTful API (Application Programming Interface) requests/responses from the end device. Data Provider (DP) is responsible for ground truth collection and dataset synchronization for the cloud. With the deep learning ability, this paper uses the MiniDeep platform to implement a recommendation system for AI-QSR (Quick Service Restaurant) KIOSK (interactive kiosk) application. AI-QSR uses the MiniDeep platform to train an LSTM (Long Short-Term Memory)-based recommendation system. The LSTM-based recommendation system converts KIOSK UI (User Interface) flow to the flow sequence and performs sequential recommendations with food suggestions. At the end of this paper, the efficiency of the proposed MiniDeep is verified through real experiments. The experiment results have demonstrated that the proposed LSTM-based scheme performs better than the rule-based scheme in terms of purchase hit accuracy, categorical cross-entropy, precision, recall, and F1 score.

Development of an Interoperable and Easily Transferable Clinical Decision Support System Deployment Platform: System Design and Development Study.

BackgroundA clinical decision support system (CDSS) is recognized as a technology that enhances clinical efficacy and safety. However, its full potential has not been realized, mainly due to clinical data standards and noninteroperable platforms.ObjectiveIn this paper, we introduce the common data model–based intelligent algorithm network environment (CANE) platform that supports the implementation and deployment of a CDSS.MethodsCDSS reasoning engines, usually represented as R or Python objects, are deployed into the CANE platform and converted into C# objects. When a clinician requests CANE-based decision support in the electronic health record (EHR) system, patients’ information is transformed into Health Level 7 Fast Healthcare Interoperability Resources (FHIR) format and transmitted to the CANE server inside the hospital firewall. Upon receiving the necessary data, the CANE system’s modules perform the following tasks: (1) the preprocessing module converts the FHIRs into the input data required by the specific reasoning engine, (2) the reasoning engine module operates the target algorithms, (3) the integration module communicates with the other institutions’ CANE systems to request and transmit a summary report to aid in decision support, and (4) creates a user interface by integrating the summary report and the results calculated by the reasoning engine.ResultsWe developed a CANE system such that any algorithm implemented in the system can be directly called through the RESTful application programming interface when it is integrated with an EHR system. Eight algorithms were developed and deployed in the CANE system. Using a knowledge-based algorithm, physicians can screen patients who are prone to sepsis and obtain treatment guides for patients with sepsis with the CANE system. Further, using a nonknowledge-based algorithm, the CANE system supports emergency physicians’ clinical decisions about optimum resource allocation by predicting a patient’s acuity and prognosis during triage.ConclusionsWe successfully developed a common data model–based platform that adheres to medical informatics standards and could aid artificial intelligence model deployment using R or Python.

Exploring interoperability of distributed Ledger and Decentralized Technology adoption in virtual enterprises

The breakthrough of Distributed Ledger Technologies (DLT) has enabled the emergence and implementation of a wide range of digital platforms in Virtual Enterprises (VE) which collaborate to provide digital services. DLT has the potential to revolutionize VE by offering transparent, decentralized, trustworthy, data provenance, reliable, and auditable features. Yet, the full deployment of DLT systems and digital platforms is still limited since some systems are operating in isolation. Hence, DLT interoperability is one of the challenges inhibiting widespread adoption of DLT platforms. DLT interoperability represents the ability for one distributed ledger platform to interact and share data with other legacy digital applications. It is inevitable to orchestrate these digital platforms fragments by introducing a cross-DLT platform integration to govern data usage within VE. Presently, already proposed approaches for DLT interoperability such as naive relay, sidechain, oracle solutions notary scheme, or relay chain are mostly not employed as they are either resource-intensive or too expensive to operate. Therefore, this paper presents a layered architecture that aids interoperability of DLT, and digital platforms based on IOTA Tangle. Design science method is adopted, and case demonstration is carried out to show how IOTA Tangle enable VE to provide an innovative virtual asset payment platform for seamless electric mobility as a service to clients. IOTA was employed as the DLT platform due to its data traceability, immutability, and tamper-proof features which allow for verification of integrity of data. IOTA offers flexibility and performance to support a reliable digital solution. Findings from this study presents a layered architecture that aids IOTA Tangle to make requests, inter-communicate, and share data via RESTful application programming interface as gateway with other external digital platforms deployed by VE to achieve an interoperable eco-system.

Pemanfaatan Restful Api Pada Mobile based test Untuk Sertifikasi Karyawan

A test is something that is used to test the quality of intelligence, ability, learning outcomes and so on. The test is always associated with an assessment or evaluation of a person to determine the person's mastery of the material. The rapid development of the digital world makes it easier for people to complete their work, the use of mobile tests is one of the technologies used by using the Application Programming Interface (API). Restful API is one of the architectures in the API which has 4 important components, namely URL Design, HTTP Verbs, HTTP Response Code, and Response Format. Rest client will access data to the Rest server, each data is distinguished based on Global ID or Universal Resource Identifier (URI) in the form of formal text XML or JSON. From the test results using WhiteBox on the admin side, there are 4 paths for 3 days with 100% success. While on the client side there are 8 paths for 3 days with 100% success.

SynLethDB 2.0: a web-based knowledge graph database on synthetic lethality for novel anticancer drug discovery.

Two genes are synthetic lethal if mutations in both genes result in impaired cell viability, while mutation of either gene does not affect the cell survival. The potential usage of synthetic lethality (SL) in anticancer therapeutics has attracted many researchers to identify synthetic lethal gene pairs. To include newly identified SLs and more related knowledge, we present a new version of the SynLethDB database to facilitate the discovery of clinically relevant SLs. We extended the first version of SynLethDB database significantly by including new SLs identified through Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) screening, a knowledge graph about human SLs, a new web interface, etc. Over 16 000 new SLs and 26 types of other relationships have been added, encompassing relationships among 14 100 genes, 53 cancers, 1898 drugs, etc. Moreover, a brand-new web interface has been developed to include modules such as SL query by disease or compound, SL partner gene set enrichment analysis and knowledge graph browsing through a dynamic graph viewer. The data can be downloaded directly from the website or through the RESTful Application Programming Interfaces (APIs). Database URL: https://synlethdb.sist.shanghaitech.edu.cn/v2.

Implementation of a RESTful Web Service with JSON Web Token Authentication and AES-256 Cryptographic Algorithm for Mobile-Based Laboratory Loan Applications at Budi Luhur University

Leasing the lab room is one of the services provided by the Integrated ICT LAB for every citizen of the Budiluhur University campus. So far in the process of borrowing the computer laboratory, the borrower must come directly to the Integrated ICT LAB or use social networks such as WhatsApp to communicate in two directions in making loans lab. This is not effective enough to serve borrowers who are usually lecturers. In the process of borrowing the lab, there are many problems, for example, the collision between lecture schedule and lecturer activity units, student activity units and other activities that require laboratory facilities. Seeing this problem, an application is needed to facilitate lab borrowing and coordination to the mobile-based ICT LAB Assistant. Meanwhile, for integration with existing systems a web service is required as a backend system so that lab lending services can be accessed by various platforms. The architecture used in the web service uses the RESTFUL API (Application Programming Interface), but there are still some problems with the RESTFUL API, namely regarding security in the process of authentication and data encryption on borrower data. In the REST architecture, a security method is needed, namely using the JSON Web Token and the Advanced Encryption Standard-256 (AES-256) cryptographic method. On the Web Services client side that provides requests and receives responses from the server then a token will be generated for the user which becomes plain data to be encrypted then saved to the database, and pulls the cipher data to be decrypted and returned to the calling application so that it can make transactions between platforms

RESTful API Testing Methodologies: Rationale, Challenges, and Solution Directions

Service-oriented architecture has evolved to be the backbone for large-scale integration between different applications and platforms. This concept has led to today’s reality of cloud services. Many of the major business platforms are providing their services to end-users and other companies as well. Companies are crafting ways to allow other businesses fast service integration and to get on board quickly in the market. REST (representational state transfer) has emerged as the standard protocol for implementing and consuming these services, which are called RESTful application programming interfaces (APIs). As the internal details of the RESTful APIs are not completely available during consumption, thorough testing has been a major challenge. Any unprecedented change in the APIs can cause the major failure of service operations, which can cause an organization to face both financial and trust losses. Research efforts have been made to alleviate testing challenges by introducing different frameworks and auto-generating unit test approaches. However, there is still a lack of an overview of the state-of-the-art in RESTful API testing. As such, the objective of this article is to identify, analyze, and synthesize the studies that have been performed related to RESTful APIs’ testing methodologies and unit test generation. With this perspective, a systematic literature review (SLR) study was conducted. In total, 16 papers were retrieved and included based on study selection criteria for in-depth analysis. This SLR discusses and categorizes different problems and solutions related to RESTful APIs’ testing and unit test generation.

The Brain Observatory Storage Service and Database (BossDB): A Cloud-Native Approach for Petascale Neuroscience Discovery.

Technological advances in imaging and data acquisition are leading to the development of petabyte-scale neuroscience image datasets. These large-scale volumetric datasets pose unique challenges since analyses often span the entire volume, requiring a unified platform to access it. In this paper, we describe the Brain Observatory Storage Service and Database (BossDB), a cloud-based solution for storing and accessing petascale image datasets. BossDB provides support for data ingest, storage, visualization, and sharing through a RESTful Application Programming Interface (API). A key feature is the scalable indexing of spatial data and automatic and manual annotations to facilitate data discovery. Our project is open source and can be easily and cost effectively used for a variety of modalities and applications, and has effectively worked with datasets over a petabyte in size.

Implementation of Data Mining on a Secure Cloud Computing over a Web API using Supervised Machine Learning Algorithm

Ever since the era of internet had ushered in cloud computing, there had been increase in the demand for the unlimited data available through cloud computing for data analysis, pattern recognition and technology advancement. With this also bring the problem of scalability, efficiency and security threat. This research paper focuses on how data can be dynamically mine in real time for pattern detection in a secure cloud computing environment using combination of decision tree algorithm and Random Forest over a restful Application Programming Interface (API). We are able to successfully Implement data mining on cloud computing bypassing or avoiding direct interaction with data warehouse and without any terminal involve by using combination of IBM Cloud storage facility, Amazing Web Service, Application Programming Interface and Window service along with a decision tree and Random Forest algorithm for our classifier. We were able to successfully bypass direct connection with the data warehouse and cloud terminal with 94% accuracy in our model.

Introducing the NIAGADS Alzheimer's GenomicsDB API: A toolkit for remote exploration of Alzheimer's disease genetics.

The Alzheimer's Genomics Database (NIAGADS GenomicsDB) provides public access to GWAS summary statistics datasets deposited at the NIA Genetics of Alzheimer's Disease Data Storage Site (NIAGADS). The NIAGADS GenomicsDB makes available 69 summary statistics datasets and reports >150 million annotated variants from 23 GWAS studies of Alzheimer's disease and related dementias (AD/ADRD). Programmatic access to the database is essential for many bioinformatics applications dealing with AD/ADRD genetics. The NIAGADS GenomicsDB is powered by a big data optimized relational database system. It uses ontologies to consistently annotate datasets, facilitating data harmonization and efficient real-time data mining. AD/ADRD-risk associated variants from GWAS datasets are annotated using the Alzheimer's Disease Sequencing Project's (ADSP) annotation pipeline (Butkiewicz et al. 2018). Variants are also mapped to proximal and co-located genes and regulatory regions using the NIAGADS FILER functional genomics data repository (https://lisanwanglab.org/FILER). We have created a RESTful Application Programming Interface (API) that facilitates the integration of NIAGADS GenomicsDB datasets and annotations into software applications. The web service supports queries that return annotated GenomicsDB entries (genes, variants, genomic loci) and GWAS summary statistics results in the form of JSON documents. Results may contain functional annotations, genetic evidence for AD/ADRD risk, and/or sequence information associated with an entry. Calls to the API are based on simple URLs that specify the entry type and data required. The API also provides endpoints for querying variant linkage disequilibrium blocks, variant tracks (annotations and summary statistics) compatible with the IGV.js genome browser (https://github.com/igvteam/igv.js/), and LocusZoom.js (https://github.com/statgen/locuszoom/) renderings of the GWAS summary statistics datasets. The NIAGADS GenomicsDB API establishes a simple framework that enables sharing and integration of annotated NIAGADS GenomicsDB entries and datasets. Use of the API requires only a HTTPS library and JSON parser, permitting large-scale programmatic access and analysis of AD/ADRD-linked variants independent of any specific programming language. The NIAGADS GenomicsDB API enables the easy retrieval of a wide range of genetic evidence for AD/ADRD, promoting data sharing and reuse among the AD/ADRD research community. Information about accessing the REST API as it becomes available can be found on the NIAGADS GenomicsDB website (https://www.niagads.org/genomics).

API Development Teaching- Learning Platform

Abstract: API (Application Programming Interface) is a sequence of commands, rules, or regulations that enables a software or app to leverage attributes from another application, system, or gadget to improve its own services. In a nutshell, it's a feature that allows apps to connect with one another. The IT industry has a high demand for REST API Development, hence this article provides an interactive teaching and learning platform for REST API Development. Using the LBD (Learning By Doing) concept, the suggested platform seeks to assist users in learning REST API Development. This tool would assist users in learning to design their own APIs by requiring them to practise (either through coding or MCQs). The platform would be based on selfevaluation and reflection models of self-regulated learning, allowing users to learn more effectively. The platform would also try to provide some scaffolding for users that fail test cases, such as assistance in debugging and why they might be failing the test cases. Keywords: REST-API, Teaching Learning Platform, Self-Regulated Learning(SRL), Human Computer Interaction(HCI), Dashboard, Learning by doing(LBD)