REST API Research Articles

A Cloud-Based Data Storage and Visualization Tool for Smart City IoT: Flood Warning as an Example Application

Collecting, storing, and providing access to Internet of Things (IoT) data are fundamental tasks to many smart city projects. However, developing and integrating IoT systems is still a significant barrier to entry. In this work, we share insights on the development of cloud data storage and visualization tools for IoT smart city applications using flood warning as an example application. The developed system incorporates scalable, autonomous, and inexpensive features that allow users to monitor real-time environmental conditions, and to create threshold-based alert notifications. Built in Amazon Web Services (AWS), the system leverages serverless technology for sensor data backup, a relational database for data management, and a graphical user interface (GUI) for data visualizations and alerts. A RESTful API allows for easy integration with web-based development environments, such as Jupyter notebooks, for advanced data analysis. The system can ingest data from LoRaWAN sensors deployed using The Things Network (TTN). A cost analysis can support users’ planning and decision-making when deploying the system for different use cases. A proof-of-concept demonstration of the system was built with river and weather sensors deployed in a flood prone suburban watershed in the city of Charlottesville, Virginia.

PandasGWAS: a Python package for easy retrieval of GWAS catalog data

BackgroundSince the NHGRI-EBI Catalog of human genome-wide association studies was established by NHGRI in 2008, research on it has attracted more and more researchers as the amount of data has grown rapidly. Easy-to-use, open-source, general-purpose programs for accessing the NHGRI-EBI Catalog of human genome-wide association studies are in great demand for current Python data analysis pipeline.ResultsIn this work we present pandasGWAS, a Python package that provides programmatic access to the NHGRI-EBI Catalog of human genome-wide association studies. Instead of downloading all data locally, pandasGWAS queries data based on input criteria and handles paginated data gracefully. The data is then transformed into multiple associated pandas.DataFrame objects according to its hierarchical relationships, which makes it easy to integrate into current Python-based data analysis toolkits.ConclusionspandasGWAS is an open-source Python package that provides the first Python client interface to the GWAS Catalog REST API. Compared with existing tools, the data structure of pandasGWAS is more consistent with the design specification of GWAS Catalog REST API, and provides many easy-to-use mathematical symbol operations.

Development of a mobile application for forest and land fire patrol using Prototype method

A previous study has developed a mobile application for recording field data during Sumatra, Indonesia’s forest and land fires patrol. After users tested the usability, the user’s need for the system increased due to the new environmental characteristics in the forest and land fire patrol area. This study aims to develop a mobile application that is easy to use by the patrol team to support forest and land fire prevention patrols. The system development continues the mobile application proposed in the previous study. The system development method used is Prototyping, with stages starting from communication; quick design; rapid design modelling; prototyping; dissemination, delivery, and feedback. The application was built using the Ionic 3 framework using the REST API from the back-end module. The development was conducted in two iteration stages, namely the third and fourth iterations. The third and fourth iterations resulted in changes in the user interface, added auto-fill features, and downloaded reports. Applications developed in each iteration were tested using the Black-Box method. The test results from each iteration show that all functions in the application work properly and meet the user requirements.

Designing Service-Oriented Approach for Animal Welfare

Abstract: REST principles and RESTful data architecture are topics that web developers constantly discuss because they are essential to contemporary development and many other things. REST is a technique for building APIs with specific organizational principles, not a technology on its own. These guidelines and ideas are intended to help developers and establish a uniform environment for handling API requests. We are developing a Restful API for the developers, Organizations to develop websites, applications, Desktop applications by using our data. Restful APIs are handy because a number of applicationscan be built over them. In this paper, we emphasized the idea of design and implementation that Instead of approaching every NGO, we are making a rest API so that data can be easily accessible and can be used to make many websites or many applications.

Development of Service-Oriented Architecture-Based Microservices Management as a Data Integration Service (Case Study: Udayana University)

Udayana University has developed numerous information systems to accommodate all types of academic and non-academic activities. Because the process of retrieving data from other systems still employs the traditional method with a joint scheme between databases, the resulting data quality becomes increasingly inaccurate over time. Some systems have also established simple web services that do not adhere to the Restful API authoring rules. Based on these issues, modifications were made to the data integration process using Microservices Management, Service Oriented Architecture, and OAuth 2.0 as the security protocol. The results indicate that the microservices management system is highly effective and efficient for improving process performance, has a low risk level, a good control test validation of 93,33 %, and a usability grade of B, indicating that the system is deemed good and usable.

Desain dan Implementasi Aplikasi Absensi Kepegawaian Berbasis Web Menggunakan Framework Laravel di Diskominfo Provinsi Jawa Barat

Abstract—In all parts of the work division at Diskominfo, West Java Province, many information systems have been involved to support a series of work activities and information asset processing. As in one part of the division, namely the Employee and Public Relations Subdivision, where all staffing activities are not far from processing employee data. But the existing Employee Information System, especially in the employee attendance data processing feature, is still not well organized and not equipped with features that are more supportive for recording the attendance data. Some features that are not perfect, for example, are the daily attendance data feature, employee leaves data, and daily late and weekly late. In addition, further processing of data also still uses the help of Microsoft Excel or manually entering data and recapitulating data, so the processing takes a long time and is not efficient in the process. Given these problems, the authors designed a system design and implemented it into a new Employee Attendance Application. The purpose of designing this application is to create an information system that further optimizes the process of processing employee attendance data at West Java Diskominfo using system development technologies such as the Laravel Framework, PostgreSQL for database management, and using the Rest API for data integration.

Unified access to up-to-date residue-level annotations from UniProtKB and other biological databases for PDB data

More than 61,000 proteins have up-to-date correspondence between their amino acid sequence (UniProtKB) and their 3D structures (PDB), enabled by the Structure Integration with Function, Taxonomy and Sequences (SIFTS) resource. SIFTS incorporates residue-level annotations from many other biological resources. SIFTS data is available in various formats like XML, CSV and TSV format or also accessible via the PDBe REST API but always maintained separately from the structure data (PDBx/mmCIF file) in the PDB archive. Here, we extended the wwPDB PDBx/mmCIF data dictionary with additional categories to accommodate SIFTS data and added the UniProtKB, Pfam, SCOP2, and CATH residue-level annotations directly into the PDBx/mmCIF files from the PDB archive. With the integrated UniProtKB annotations, these files now provide consistent numbering of residues in different PDB entries allowing easy comparison of structure models. The extended dictionary yields a more consistent, standardised metadata description without altering the core PDB information. This development enables up-to-date cross-reference information at the residue level resulting in better data interoperability, supporting improved data analysis and visualisation.

Bootstrapping Automated Testing for RESTful Web Services

Modern RESTful services expose RESTful APIs to integrate with diversified applications. Most RESTful API parameters are weakly typed, which greatly increases the possible input value space. Weakly-typed parameters pose difficulties for automated testing tools to generate effective test cases to reveal web service defects related to parameter validation. We call this phenomenon the type collapse problem. To remedy this problem, we introduce FET (Format-encoded Type) techniques, including the FET, the FET lattice, and the FET inference to model fine-grained information for API parameters. Inferred FET can enhance parameter validation, such as generating a parameter validator for a certain RESTful server. Enhanced by FET techniques, automated testing tools can generate targeted test cases. We demonstrate Leif, a trace-driven fuzzing tool, as a proof-of-concept implementation of FET techniques. Experiment results on 27 commercial services show that FET inference precisely captures documented parameter definitions, which helps Leif discover 11 new bugs and reduce <inline-formula><tex-math notation="LaTeX">$72\% - 86\%$</tex-math></inline-formula> fuzzing time compared to state-of-the-art fuzzers. Leveraged by the inter-parameter dependency inference, Leif saves 15% fuzzing time.

Streamlining PySpark Data Pipelines: Integrating External Data Sources and APIs

In the ever-expanding domain of big data analytics, integrating external data sources and APIs into data processing workflows is essential for enriching analyses and enhancing decision-making. PySpark, a prominent component of the Apache Spark ecosystem, offers robust capabilities for high-performance data processing but often requires extension to utilize external databases, web services, and third-party APIs effectively. This paper explores diverse methodologies for importing and managing data from these varied sources directly into PySpark. Techniques discussed include utilizing JDBC for database connections, employing HTTP clients for accessing RESTful APIs, and leveraging cloud storage APIs compatible with Hadoop. While integrating external data sources into PySpark pipelines significantly enriches the available data landscape, it also introduces challenges such as scalability of ingestion processes, maintenance of data integrity, and real-time data processing overhead. This study aims to provide a detailed examination of these integration techniques, highlighting both the strategic advantages and potential complications. By addressing these challenges, the paper presents a pathway towards more dynamic and comprehensive data analytics platforms, enabling businesses to leverage real-time insights and drive more nuanced analyses through PySpark.

Cloud Based Password Wallet and Manager

Although various solutions have been suggested for password management systems, most methods require the support of external physical hardware infrastructure. Due to the increase in complexity and cost of set-up of supporting hardware requirements, scalability will always be an issue with such systems. In this paper, we present the design of a Cloud based password management system. The proposed method uses Cryptography for better encryption and is supported by a web-based architecture, for easily managing passwords and providing an indoor cloud based password manager system . Keyword: Cloud Computing Restful API Cyber Security Password Manager

Implementation of Pddikti Neo Feeder Web Service in Recording of Independent Campus Activities

Independent Learning-Independent Campus Program (MBKM) is a policy of granting the right for students to be able to take study activities outside the study program as many as three semesters with the division of two semesters of study outside the college and one semester in different study programs in one college. As well as teaching and learning activities, universities must report Independent Learning-Independent Campus activities to DIKTI every semester through the Neo Feeder PDDIKTI application. The Neo Feeder PDDIKTI application has a feature to enter the activities the operator will carry out. The operator enters this data individually on the Neo Feeder PDDIKTI application. This is a significant problem because the data entry process will take quite a lot of time, even though PDDIKTI has provided web service access to universities to optimize the data reporting process using the Neo Feeder PDDIKTI application. Building an application that can be used for recording MBKM activities by utilizing web services provided by PDDIKTI is the primary purpose of this study. The development of an application certainly requires a method as a framework or guide in facilitating the manufacturing process. The extreme Programming (XP) method becomes essential for applications with variable or non-fixed needs. This method has four working elements: planning, designing, coding, testing and software increment. The output generated by this study is an application for recording MBKM activities that use web service restful API technology so that data entry can be done en masse and not one by one.

Precision information extraction for rare disease epidemiology at scale

BackgroundThe United Nations recently made a call to address the challenges of an estimated 300 million persons worldwide living with a rare disease through the collection, analysis, and dissemination of disaggregated data. Epidemiologic Information (EI) regarding prevalence and incidence data of rare diseases is sparse and current paradigms of identifying, extracting, and curating EI rely upon time-intensive, error-prone manual processes. With these limitations, a clear understanding of the variation in epidemiology and outcomes for rare disease patients is hampered. This challenges the public health of rare diseases patients through a lack of information necessary to prioritize research, policy decisions, therapeutic development, and health system allocations.MethodsIn this study, we developed a newly curated epidemiology corpus for Named Entity Recognition (NER), a deep learning framework, and a novel rare disease epidemiologic information pipeline named EpiPipeline4RD consisting of a web interface and Restful API. For the corpus creation, we programmatically gathered a representative sample of rare disease epidemiologic abstracts, utilized weakly-supervised machine learning techniques to label the dataset, and manually validated the labeled dataset. For the deep learning framework development, we fine-tuned our dataset and adapted the BioBERT model for NER. We measured the performance of our BioBERT model for epidemiology entity recognition quantitatively with precision, recall, and F1 and qualitatively through a comparison with Orphanet. We demonstrated the ability for our pipeline to gather, identify, and extract epidemiology information from rare disease abstracts through three case studies.ResultsWe developed a deep learning model to extract EI with overall F1 scores of 0.817 and 0.878, evaluated at the entity-level and token-level respectively, and which achieved comparable qualitative results to Orphanet’s collection paradigm. Additionally, case studies of the rare diseases Classic homocystinuria, GRACILE syndrome, Phenylketonuria demonstrated the adequate recall of abstracts with epidemiology information, high precision of epidemiology information extraction through our deep learning model, and the increased efficiency of EpiPipeline4RD compared to a manual curation paradigm.ConclusionsEpiPipeline4RD demonstrated high performance of EI extraction from rare disease literature to augment manual curation processes. This automated information curation paradigm will not only effectively empower development of the NIH Genetic and Rare Diseases Information Center (GARD), but also support the public health of the rare disease community.

Detection of Inappropriate Tweets Linked to Fake Accounts on Twitter

It is obvious that one of the most significant challenges posed by Twitter is the proliferation of fraudulent and fake accounts, as well as the challenge of identifying these accounts. As a result, the primary focus of this paper is on the identification of fraudulent accounts, fake information, and fake accounts on Twitter, in addition to the flow of content that these accounts post. The research utilized a design science methodological approach and developed a bot account referred to as “Fake Account Detector” that assists with the detection of inappropriate posts that are associated with fake accounts. To develop this detector, previously published tweets serve as the datasets for the training session. This data comes from Twitter and are obtained through the REST API. The technique of machine learning with random forest (RF) is then used to train the data. The high levels of accuracy (99.4%) obtained from the RF detection results served as the foundation for the development of the bot account. This detector tool, developed using this model, can be utilized by individuals, businesses, and government agencies to assist in the detection and prevention of Twitter problems related to fake news and fake accounts.

MeganServer: facilitating interactive access to metagenomic data on a server.

Metagenomic projects often involve large numbers of large sequencing datasets (totaling hundreds of gigabytes of data). Thus, computational preprocessing and analysis are usually performed on a server. The results of such analyses are then usually explored interactively. One approach is to use MEGAN, an interactive program that allows analysis and comparison of metagenomic datasets. Previous releases have required that the user first download the computed data from the server, an increasingly time-consuming process. Here, we present MeganServer, a stand-alone program that serves MEGAN files to the web, using a RESTful API, facilitating interactive analysis in MEGAN, without requiring prior download of the data. We describe a number of different application scenarios. MeganServer is provided as a stand-alone program tools/megan-server in the MEGAN software suite, available at https://software-ab.cs.uni-tuebingen.de/download/megan6. Source is available at: https://github.com/husonlab/megan-ce/tree/master/src/megan/ms. Supplementary data are available at Bioinformatics online.

AMAZON S3 STATIC WEBSITE HOSTING

Nowadays, especially in period of energetic crisis, it is very important to get reliable data storage and uninterrupted access to websites. Considering that the world’s mainstream is using the clouds services for educational, scientific, business purposes it is important to explore features of hosting websites in major cloud service providers. One of the leader’s vendors in cloud servicing is Amazon with its Web Services (AWS). AWS offers hosting solutions such as Lightsail, Amplify Console, Simple Storage Service (S3) and Elastic Cloud Computing, from simple static websites to complicated dynamic, The article deals with the method of hosting static websites. Simple Storage Service was used for this purpose. The method has many benefits: the service is simple, cheap, scales well, manages everything and has great integration with other AWS services. This method is the best for websites that do not contain server-side scripting, like PHP or ASP.NET. It is ideal for websites that change infrequently, such as personal, promo, startup websites, websites of small businesses and organizations. The article focuses on how set permissions, properties and policies of AWS S3 bucket for hosting a website. But usage of S3 bucket alone provides only http connection. That’s why to deploy secure connection and speed up delivery of the static content was proposed to use AWS CloudFront service. CloudFront is content delivery network service, that uses the latest version of Transport Layer Security Protocol and saves files in the cache for 24 hours. The article shows how to configure CloudFront distribution to serve https requests for an Amazon S3 bucket. Configuration S3 bucket REST API endpoint was used as the most secure case. As a result, latency was reduced, security was improved due to traffic encryption and the content of S3 bucket was kept private.

Implementation of Automated Test Case Generation in REST API on Android-Based Koperasi Application

This study is focused on developing a data collection system to enhance the performance of Koperasi, an organization with complex data collection. An Android application was created to automate the processing of member and transaction data, significantly improving data processing efficiency. However, building a quality system takes time and requires error-free data processing. To achieve this, Automated Test Case Generation with EvoMaster was used to test the REST API and identify errors. The testing process went through several iterations until almost no errors were found. EvoMaster generated over 19.5 million scenarios and found 78 errors in the REST API in 58 hours, which were promptly fixed between iterations. The use of EvoMaster not only reduced development time but also helped maintain code quality.

Implementation of Message Service Queue Using Rabbit MQ

A distributed system is a software application that makes use of a collection of protocols to manage the activities of numerous processes running on a communication network, so that each part cooperates to finish a single or a condensed number of related tasks. Representational State Transfer (REST) API is also integrated into this, making it simple to access online services without the need for additional processing. The server sends a client-side representation of the requested resource whenever a RESTful API is used. It is also integrated with microservices through the open-source message-broker programme RabbitMQ. With little developer involvement, the configuration file must automatically network new messaging services that join the distributed system.

The scalable precision medicine open knowledge engine (SPOKE): a massive knowledge graph of biomedical information.

Knowledge graphs (KGs) are being adopted in industry, commerce and academia. Biomedical KG presents a challenge due to the complexity, size and heterogeneity of the underlying information. In this work, we present the Scalable Precision Medicine Open Knowledge Engine (SPOKE), a biomedical KG connecting millions of concepts via semantically meaningful relationships. SPOKE contains 27 million nodes of 21 different types and 53 million edges of 55 types downloaded from 41 databases. The graph is built on the framework of 11 ontologies that maintain its structure, enable mappings and facilitate navigation. SPOKE is built weekly by python scripts which download each resource, check for integrity and completeness, and then create a 'parent table' of nodes and edges. Graph queries are translated by a REST API and users can submit searches directly via an API or a graphical user interface. Conclusions/Significance: SPOKE enables the integration of seemingly disparate information to support precision medicine efforts. The SPOKE neighborhood explorer is available at https://spoke.rbvi.ucsf.edu. Supplementary data are available at Bioinformatics online.

A Secure IoT Based Wireless Sensor Network Data Aggregation and Dissemination System

Cloud computing can greatly benefit web applications that have specific computation and storage requirements. With a scalable and adaptable design, the cloud is coupled with wireless sensor networks. Numerous application domains can be integrated directly with REST-based web services. An IP-based WSN testbed was used to develop a proof-of-concept REST API for remote data access. A user receives notifications via email or Twitter when monitoring data exceeds thresholds.

Penerapan Rest API Pada QR Code Untuk Validasi Surat Administrasi Desa

As one of the lower-level government agencies, the village has an important role in a government. One of the important roles of the village is serving the community in making administrative letters, for example in making death certificates, business, domiciles and others. Currently the process of making administrative letters is not optimal which causes the process of making administrative letters takes quite a long time and the problem is often queues and there is no certainty that the administrative letter will be completed on that day besides that the community cannot fill out the village head's signature on the letter due to the busyness of the village head which requires him to be often out of the office or have many activities so a system is needed that can make it easier for village officials to get legitimacy letters and serving the community in making administrative letters, this study uses the Rest API on the QR Code for validation in administrative letters in the village, where Rest api uses the http protocol where the API link will be entered with a QR Code and this Rest API as link the QR Code to the web. By implementing the Rest API on the QR code for validating administrative letters in the village, a feasibility test has been carried out and obtaining a value of 82.5%, which means that the application is very feasible, and can help process administrative letters in the village .