Data Web Services Research Articles

Research on Road Geological Hazard Risk Assessment Model Based on Computational Deep Learning Algorithm Integrated With GIS

This paper studies the geological disaster emergency command and management system based on geographic information system. This system is for the province, city, county land resources and other aspects of the comprehensive application, the realization of real-time information display, meteorological early warning, group measurement and mass prevention, monitoring and early warning, major disaster management, prevention plan, quick report processing, emergency investigation, emergency disposal, emergency consultation and other functions. Through the GSM network, the monitoring data is sent to the monitoring center through the mobile phone, and then the monitoring center processes, stores, displays and alarms the monitoring data. Finally, B/S structure is used to build a remote Web data service publishing platform, so that it can carry out real-time monitoring at any place, and improve the speed, efficiency and intuition of high-speed slope disaster. It is proved that the method has the advantages of stable operation, accurate data, fast transmission speed and high monitoring accuracy through the practical application of a highway project foundation pit slope monitoring.

Forecast earthquake precursor in the Flores Sea

<span>Artificial intelligence (AI) can use seismic training data to discover relationships between inputs and outcomes in real-world applications. Despite this, particularly when using geographical data, it has not been used to predict earthquakes in the Flores Sea. The algorithm will read the seismic data as a pattern of iterations throughout the operation. The output data is created by grouping based on clusters using the most effective WCSS analysis, while the input features are derived from the original international resource information system (IRIS) web service data. Given that earthquake prediction is an effort to reduce seismic disasters, this research is essential. By generating predictions, it can reduce the devastation caused by earthquakes. Using the support vector machine (SVM), hyperparameter support vector machine (HP-SVM), and particle swarm optimization support vector machine (PSO-SVM) algorithms, this study seeks to forecast the Flores Sea earthquake. According to the estimation results, the SVM algorithm’s evaluation value is less precise than that of the HP-SVM, especially the linear HP-SVM and HP-SVM Polynomial models. However, the HP-SVM RBF model’s accuracy rating is identical to that of the traditional SVM model. <br /> The improvement of the PSO-SVM model, which has the finest gamma position and a value of 9.</span>

Technical Note—Cloud Cost Optimization: Model, Bounds, and Asymptotics

A Near-Optimal Capacity and Scheduling Policy for Cloud Users In “Cloud Cost Optimization: Model, Bounds, and Asymptotics,” Qu, Dawande, and Janakiraman study a long-term, dynamic resource-optimization problem for firms managing various cloud resources to process incoming computing tasks over time. Cloud resources vary in attributes, such as computing speed, memory, accelerators, and storage, tailored to different computing tasks, such as data warehousing, scientific computing, machine learning, and data processing. Firms can choose reserved resources for long-term commitments at lower costs or on-demand resources for flexibility at a higher price. Firms face three key trade-offs: the cost disparity between reserved and on-demand resources, the variation in resource attributes affecting performance and cost, and the challenge of balancing delay and resource costs. We propose an asymptotically optimal policy, demonstrating its effectiveness through a detailed numerical study based on Amazon Web Services data.

Gated Multi-task Learning for QoS-aware Service Recommendation

In the ever-increasing diversity of cloud environment, the recommendation of web service-related applications based on service quality is a basic tasks that both service providers and web developers are very interested in. A key challenge is how to get personalized and accurate QoS values of web services from the multi-source information of users/services. In addition, complex contexts and multi-tasking attributes also make the collaborative services quality prediction difficult to meet. Inspired by the progress of multi-task deep learning, this paper proposes a new framework of service quality prediction called multi-task gated expert network (MGEN), which is based on gating mechanism and multi-task learning. The MGEN consists of three parts: expert-based context-source feature extraction, gated interaction learning and information fusion. First, multiple expert networks are used to capture the latent representation in raw data of web services, including both implicit features in the QoS matrix and explicit features of service descriptions. In the process, several independent experts extract features from different dimensions. Second, output embedding of different expert networks is transmitted to each task layer through different weights, learning both global and local high-order feature interactions. Third, the interactive information of different tasks is fused by embedding splicing, and then fed back to the prediction module for final service recommendation. Results of the comparative experiment demonstrate that the proposed MGEN significantly outperforms the state-of-the-art models of service recommendation, where both the implicit and explicit information of various multi-source features and interaction sequences are effectively extracted. In addition, the gating mechanism performs well in the QoS prediction by independently modeling each task.

Estimation of Flores Sea Aftershock Rupture Data Based on AI

The earthquake catalog notes that there have been earthquakes with Mw > 7 that hit the Flores area, three of which occurred in the Flores Sea in 1992, 2015, and 2021. Revealed that the seismic activity of Eastern Indonesia is thought to be influenced by the isolated thrust fault segment of the island of Flores and the island of Wetar. The study of the rising fault segment on Flores Island and Wetar Island helps in further understanding the fault behavior, earthquake pattern, and seismic risk in the Flores Sea region. In earthquakes with giant magneto, an aftershock can occur due to the interaction of ground movements. This research analyzes and compares the data from the evaluation of the classification algorithm and the regression algorithm. The initial stages of this research include requesting IRIS DMC Web Service data. The data is then subjected to a cleaning process to obtain the expected feature extraction. The next stage is to perform the clustering process. This stage is carried out to label dependent data by adding new features as data clusters. The following procedure divides the validation value, which consists of training and test data. The estimation results show that the classification algorithm's evaluation value is better than that of the regression algorithm. The evaluation value of several algorithms indicates this, with an accuracy rate between 80% and 100%.

Analysis of information providing for antihypertensive medicines according to the data of Ukrainian web services

The digitalization of the pharmaceutical field provides new opportunities to meet patient’s needs and to improve communications, which is important for pharmacies. According to the Law of Ukraine dated 17.09.2020 «On Amendments to Article 19 of the Law of Ukraine "On Medicines" regarding the implementation of electronic retail trade of medicinal products» and Article 81 of the Law of Ukraine «On Medicines» dated 28.07.2022), pharmacies were allowed to carry out electronic retail trade of pharmaceuticals on the condition that information about the right to online trade is entered in the Licensing Register for the manufacture of medicines
 The aim was the analysis and detailed study of the information content of sections of web services of pharmacies and web portals of Ukraine using the example of drugs for the treatment of arterial hypertension and substantiating the need for unification of information for patients. The basis of our analysis was information from 18 web services of pharmacy chains and 5 Internet portals for booking medicines.
 The analysis of the information support revealed that the most common filters for searching drugs for the treatment of arterial hypertension on resources were: medicine price scale, manufacturer, form of release and dosage.
 The results of the structural analysis of information showed that the following parameters are available on all sites and platforms: trade name of the medicinal product, price, form of release, dosage and quantity in the package. The information about the manufacturer is presented on 66% of pharmacy web services and 60% of web platforms. Only 28% of the pharmacy web services and 60% web platforms inform patients about the dispensing of prescription drugs.
 Identified differences in the information content of websites and web platforms require the unification of information for patients with arterial hypertension according to such parameters as: prescription leave; dispensing by e-prescription, with an emphasis on application features; the participation of pharmacies in social programs, especially reimbursement, which will contribute to the optimization of information and educational provision of patients for the rational use of drugs for chronic non-infectious diseases.

An algorithm for deriving the topology of belowground urban stormwater networks

Abstract. Belowground urban stormwater networks (BUSNs) are critical for removing excess rainfall from impervious urban areas and preventing or mitigating urban flooding. However, available BUSN data are sparse, preventing the modeling and analysis of urban hydrologic processes at regional and larger scales. We propose a novel algorithm for estimating BUSNs by drawing on concepts from graph theory and existing, extensively available land surface data, such as street network, topography, and land use/land cover. First, we derive the causal relationships between the topology of BUSNs and urban surface features based on graph theory concepts. We then apply the causal relationships and estimate BUSNs using web-service data retrieval, spatial analysis, and high-performance computing techniques. Finally, we validate the derived BUSNs in the metropolitan areas of Los Angeles, Seattle, Houston, and Baltimore in the US, where real BUSN data are partly available to the public. Results show that our algorithm can effectively capture 59 %–76 % of the topology of real BUSN data, depending on the supporting data quality. This algorithm has promising potential to support large-scale urban hydrologic modeling and future urban drainage system planning.

Hiplot: a comprehensive and easy-to-use web service for boosting publication-ready biomedical data visualization.

Complex biomedical data generated during clinical, omics and mechanism-based experiments have increasingly been exploited through cloud- and visualization-based data mining techniques. However, the scientific community still lacks an easy-to-use web service for the comprehensive visualization of biomedical data, particularly high-quality and publication-ready graphics that allow easy scaling and updatability according to user demands. Therefore, we propose a community-driven modern web service, Hiplot (https://hiplot.org), with concise and top-quality data visualization applications for the life sciences and biomedical fields. This web service permits users to conveniently and interactively complete a few specialized visualization tasks that previously could only be conducted by senior bioinformatics or biostatistics researchers. It covers most of the daily demands of biomedical researchers with its equipped 240+ biomedical data visualization functions, involving basic statistics, multi-omics, regression, clustering, dimensional reduction, meta-analysis, survival analysis, risk modelling, etc. Moreover, to improve the efficiency in use and development of plugins, we introduced some core advantages on the client-/server-side of the website, such as spreadsheet-based data importing, cross-platform command-line controller (Hctl), multi-user plumber workers, JavaScript Object Notation-based plugin system, easy data/parameters, results and errors reproduction and real-time updates mode. Meanwhile, using demo/real data sets and benchmark tests, we explored statistical parameters, cancer genomic landscapes, disease risk factors and the performance of website based on selected native plugins. The statistics of visits and user numbers could further reflect the potential impact of this web service on relevant fields. Thus, researchers devoted to life and data sciences would benefit from this emerging and free web service.

Multi-Layer Web Services Discovery Using Word Embedding and Clustering Techniques

We propose a multi-layer data mining architecture for web services discovery using word embedding and clustering techniques to improve the web service discovery process. The proposed architecture consists of five layers: web services description and data preprocessing; word embedding and representation; syntactic similarity; semantic similarity; and clustering. In the first layer, we identify the steps to parse and preprocess the web services documents. In the second layer, Bag of Words with Term Frequency–Inverse Document Frequency and three word-embedding models are employed for web services representation. In the third layer, four distance measures, namely, Cosine, Euclidean, Minkowski, and Word Mover, are considered to find the similarities between Web services documents. In layer four, WordNet and Normalized Google Distance are employed to represent and find the similarity between web services documents. Finally, in the fifth layer, three clustering algorithms, namely, affinity propagation, K-means, and hierarchical agglomerative clustering, are investigated for clustering of web services based on observed similarities in documents. We demonstrate how each component of the five layers is employed in web services clustering using randomly selected web services documents. We conduct experimental analysis to cluster web services using a collected dataset consisting of web services documents and evaluate their clustering performances. Using a ground truth for evaluation purposes, we observe that clusters built based on the word embedding models performed better than those built using the Bag of Words with Term Frequency–Inverse Document Frequency model. Among the three word embedding models, the pre-trained Word2Vec’s skip-gram model reported higher performance in clustering web services. Among the three semantic similarity measures, path-based WordNet similarity reported higher clustering performance. By considering the different word representations models and syntactic and semantic similarity measures, we found that the affinity propagation clustering technique performed better in discovering similarities among Web services.

A Hybrid Service Ranking Based Collaborative Filtering Model on Cloud Web Service Data

INTRODUCTION: Trust is an important indicator in the cloud computing environment for service selection and recommendation. It is a difficult task to create a composite value-added service from several candidate services for the desired objectives due to the dramatic gr

Design and implementation of IoT based environment pollution monitoring system: A case study of Iraq

Researches confirm the existence of widespread pollution in Iraq, where a large number of citizens suffer from symptoms related to environmental pollutants. This is accompanied by the lack of an integrated monitoring system that provides wide-scale, real-time monitoring of air pollution for pollutants (Particulate Matter (PM), Sulfur Dioxide (SO2), Ground-Level Ozone (O3), Carbon Monoxide (CO), and Nitrogen Dioxide (NO2), ionizing radiation). In this paper, an integrated system was designed and implemented to meet the requirements of air pollution monitoring in Iraq on a wide-scale and provide its results in real-time to the end-users in community service as a significant step towards smart cities. Distributed computing concepts and design patterns were used to realize the system in the form of web services along with the embedded sensing units. Where the readings from the former are sent in real-time to the later to be aggregated and analysed in database systems. Reporting services are run on the data in web-services to inform the citizens with the air quality, radioactively polluted areas, and provide real-time alerts and recommendations to those with respiratory diseases.

Resolving Interoperability Issues of Date with Null Value and Collection of Complex Data Types by Using JADE-WSIG Framework

In today’s world, the internet and distributed computing make things so convenient that web services can be easily built and fetch from any platform. Web services are loosely coupled, interoperable, and heterogeneous hence they help to connect web applications of different languages. Interoperability is the major factor while transferring web services from one platform to another platform.WS-I basic profile 1.0/1.1 organization provides guidelines to achieve interoperability of web services at a basic level but still, issues arise at a complex level. In this paper, we have discussed and shown the result of two interoperability issues i.e. Date with Null value and Collection of Complex data types of web services using JAVA and.NET environment. In the first issue.NET treat NULL as a value type and JAVA treats NULL as reference type in date-time data type. So, whenever a JAVA client fetches a web service built in.NET it will show a parsable error. In the second issue, a web service data structure contains elements of any type. Whenever, a web service built with the ‘ArrayOfAnyType’ data structure, it can be easily mapped to a.NET client but not to JAVA client. Hence, a data type mismatch issue arises here. To resolve these inter-platform issues, we have used JADE-WSIG as middleware between web services and agent-technology.

Simulated dataset collection method of dynamic quality of services (QoS) metrics

The dynamic behavior of web services is critical for researchers to determine their performance at different times. Many existing studies present the static information of web services that are not so effective for estimating web services' future performance. This research article reveals the acquisition of throughput metric dataset for thirty timestamps by a simulation technique. A simulated setup has been proposed to acquire the dynamic throughput data of benchmark web services from workload modeling. The proposed method is aimed to collect dynamic web services metrics data of five web services (WS1–WS5), which are randomly selected from WS-Dream dataset. Additionally, the collected data has been verified by measuring computer resource usage metrics in four experiments. Experiment results indicate that the varying ‘Central Processing Unit’ (CPU) resource usage metrics do not show changes in the collected ‘Quality of Service’ QoS metric values. The experiment results showed that the proposed method achieves the consistent average response time and throughput metric values. The collected simulated data can be further applied to improve the quality of web services.

A Topic-Sensitive Method for Mashup Tag Recommendation Utilizing Multi-Relational Service Data

Tagging systems have been widely used as a major way of managing Web service resources. Many portals such as ProgrammableWeb and BioCatalogue allow users to create manual tags annotating Web services and their compositions (e.g., mashups). This is extremely helpful for managing and retrieving enormous Web service data. In the past few years, many tag recommendation approaches have been proposed for Web services that contain few or no tags. Most of them only exploit the textual content or tag service matrix information. Sometimes those approaches suffer from the data sparsity problem, especially when Web services have only few tags or their auxiliary textual contents are hard to be obtained. In real world, a plenty of relationships are available in recommendation systems, e.g., the composition relationship between services and the annotation relationship between mashups and tags. These multi-relational data can be utilized as additional features to improve the recommendation performance. In this paper, we exploit various types of relationships as features and propose a novel topic-sensitive approach based on the Factorization Machines for mashup tag recommendation. Factorization Machines is utilized to model the pair-wise interactions between all features and predict adequate tags for mashups. In this approach, we first obtain the latent topics of all tags as well as the description documents for mashups and APIs based on a novel probabilistic topic model. Then, a multi-relational network by mining various relationships from the Web service data is constructed. Various auxiliary informations are subsequently extracted from the network to train the Factorization Machines. The proposed model is evaluated on three real-world datasets and the experimental results show that it outperforms several state-of-the-art methods.

Wind speed monitoring and alert system using sensor and weather forecast

Strong wind allows uproot the trees and causes human victims to be injured or dead. Unfortunately, the strong wind comes suddenly to the trees on the street and many people and traffic are around there. It needs wind speed monitoring and strong wind early warning to avoid the victims due to fallen tree. This paper explains the development of wind monitoring and early warning system using wind speed sensor and weather forecasting. The steps involve investigating the wind speed, designing the system, developing the system, testing, and validating. The result shows that the system can measure the wind speed on the spot in real time and trigger the alert to the people around if the strong wind was occurred. On the other hand, it also informs the forecast of wind speed according to weather web services data few hours before. Applying this system will help the government to prevent the victims of fallen tree due to strong wind.

RETRACTED: Smart Japanese listening course design based on data intelligent analysis and FPGA

Japan's lack of English communication opportunities proves to be a challenge for the learner, as there is no consistent training in listening and speaking English and will not provide significant improvement as a foreign language. A Japanese faculty teacher will launch a mobile-assisted language learning shadow application to overcome these difficulties. Students can cultivate their ears during shadow training. It is officially accepted that technology can be useful in improving speaking and listening. It then considers the change in motivation, influence, oral communication in English, English proficiency of first-year college students, and most science and engineering significant activities. Referral services based on user activities on the Internet, such as data analysis, can benefit Internet service providers and users. Although these services use a lot of statistical data that can be obtained through content and online transactions, they also face the challenge of ensuring that service providers do not infringe on their services' privacy. However, the large amount of data received by a network imposes a high predictable cost of the new implementation. This article focuses on accelerating its use in web service data anonymization and enabling data anonymization in Field Programmable Gate Teams (FPGA). Anonymizer proposes RAM and uses real network traffic to evaluate how it completes the test network usage.

A BERT Based Approach to Measure Web Services Policies Compliance With GDPR

Data confidentiality is an issue of increasing importance. Several authorities and regulatory bodies are creating new laws that control how web services data is handled and shared. With the rapid increase of such regulations, web service providers face challenges in complying with these evolving regulations across jurisdictions. Providers must update their service policies regularly to address the new regulations. The challenge is that regulatory documents are large text documents and require substantial human effort to comprehend and enforce. On the other hand, web service provider privacy policies are relatively short compared to the regulatory texts, so it is hard to determine if an organization’s policy document addresses the regulation’s essential elements. We have developed a framework to automatically compare web service policies with regulatory policies to measure how closely the web service provider complies with a regulation. In this paper, we present our framework’s details along with the results of analyzing a corpus of 3,000 privacy policies against GDPR. Our framework uses BiLSTM multi-class classification and a BERT extractive summarizer. We evaluate the framework’s efficacy by checking the context similarity score between summarized GDPR and web service provider privacy policies.

Service Partition Method Based on Particle Swarm Fuzzy Clustering

It is difficult to accurately classify a service into specific service clusters for the multirelationships between services. To solve this problem, this paper proposes a service partition method based on particle swarm fuzzy clustering, which can effectively consider multirelationships between services by using a fuzzy clustering algorithm. Firstly, the algorithm for automatically determining the number of clusters is to determine the number of service clusters based on the density of the service core point. Secondly, the fuzzy c‐means combined with particle swarm optimization algorithm to find the optimal cluster center of the service. Finally, the fuzzy clustering algorithm uses the improved Gram‐cosine similarity to obtain the final results. Extensive experiments on real web service data show that our method is better than mainstream clustering algorithms in accuracy.

Application of a technology based on JavaEE in National Rural Environmental Health Monitoring Information Management System

To establish a national rural environmental health monitoring information management system supporting the application and management of four levels of data at the county, municipal, provincial and national levels, to realize the inter-departmental and cross-platform interconnection service of the national environmental health monitoring information management system. JavaEE and BS architecture platform, Web Service, XML and other data application and exchange service technologies were used to set data entry and audit functions, such as logic verification, extreme value verification and inter-table relationship verification for key variables, and also used to make the computer interface automatically configured. The functions of data entry, audit, exchange, historical data query and download, system management and maintenance, monitoring system feedback, data reporting progress of the national rural environmental health monitoring information management system across different departments and platforms can be realized by the JavaEE technology. The national rural environmental health monitoring information management system was simple in operation and friendly in interface, and had a good application prospect in the field of public health monitoring data management.

Arquitetura para integração de bibliotecas digitais geográficas por meio de mecanismos de geoprocessamento no contexto da ciência da informação

Objetivo: Construir uma arquitetura conceitual, com elementos para a criação de uma Biblioteca Digital Geográfica, utilizando os padrões e os conceitos da Ciência da Informação em conjunto com o Geoprocessamento.Método: Por meio de um estudo teórico, exploratório e bibliográfico nas áreas de Ciência da Informação e Geoprocessamento, foi possível desenvolver um modelo conceitual de arquitetura para a Biblioteca Digital Geográfica. A proposta foi estruturada em três camadas: a Cliente, responsável pelo processo de visualização; a Aplicação, que possui os processos de gerenciamento e análise; e a camada de dados, que contempla os serviços Web de dados, com foco na recuperação de metadados, via protocolo PMH (Protocol Metadata Harvesting).Resultado: A arquitetura conceitual criada atendeu os requisitos de representação da informação, as formas de comunicação com o protocolo de coleta de metadados e objetos digitais, possibilitando assim, o compartilhamento dos acervos informacionais geográficos distribuídos em diferentes Bibliotecas Digitais Geográficas ao redor do mundo. Os elementos informacionais enfocados no Geoprocessamento e as formas de representação temática e descritiva, de organização e recuperação de informação da Ciência da Informação confirmou-se o potencial de utilização recíproca e compartilhada de conceitos e ferramentas destas duas áreas.Conclusões: Os principais aspectos da pesquisa foram: com a implantação das três camadas e quatro processos, é possível a utilização de sistema de informações geográficas e aplicativos de interface ao usuário para facilitar o processo de compartilhamento e recuperação da informação. A utilização do gerenciador e do padrão de metadados proporciona a recuperação de informação precisa, juntamente com o editor de geo-ontologia único para todas as bibliotecas participantes.