Large-scale Services Research Articles

Research on Parallel Task Scheduling Algorithm of SaaS Platform Based on Dynamic Adaptive Particle Swarm Optimization in Cloud Service Environment

To efficiently realize the parallel task scheduling of SaaS platform in large-scale cloud service environment, this paper studies the parallel task scheduling algorithm of SaaS platform based on dynamic adaptive particle swarm optimization in cloud service environment. Users access the cloud through the user access interface module, and issue task scheduling instructions or send task scheduling requests. After the service management module provides diversified application service support according to the scheduling requirements, the core service module determines the SaaS platform parallel scheduling objective function, and uses dynamic adaptive particle swarm optimization to solve the objective function to obtain the SaaS platform parallel task scheduling results. The test results show that the algorithm has better multi-objective solving ability and can obtain higher quality objective solutions, and the test results of the total execution time of parallel scheduling tasks and the total transmission time of task data on SaaS platform are all within 30 s. The results of virtual machine resource load balancing degree are all below 15%; the utilization rate of virtual machine resources is above 92.2%.

Sacred Meets Synthetic: A Multi-Method Study on the First AI Church Service

Artificial Intelligence (AI) is transforming society, including religious practices and experiences of religious communities. The first large-scale AI church service took place in 2023 in Germany as a Protestant Christian worship event within a biannual religious celebration. Studying this religious service illustrates how AI was developed as a religious agent and provides insights into the experiences of attendees. This research note presents data from a quantitative survey and two qualitative questionnaires of participants conducted at the AI worship service. Findings show diversity of opinion and a range of attendee experiences. By presenting the results of qualitative and quantitative analysis, this research note highlights the possibilities and limitations of incorporating AI into the religious sphere. While most of the responses were skeptical, participants also reported having spiritual experiences. Nevertheless, attendees also reported conflicting feelings regarding experiences of community within an artificial setting. The main concerns were technological limitations, fear of replacing humans, biases in the theology of the underlying large language model, and lack of personality and emotion. Age-based differences include younger attendees finding the AI service more attractive while older attendees found the AI service more stimulating. Implications are drawn for practical theology and AI implementation within religious settings.

Symptom vs context: lessons learned from a large-scale implementation of the Cultural Formulation Interview.

Mental health services in multicultural societies require culturally sensitive approaches to reduce health disparities. The Cultural Formulation Interview (CFI) is thought to enhance shared decision making and to facilitate culturally and contextually informed treatment. There is, however, little known regarding its implementability in large-scale psychiatric services. The current paper reports on (a) efforts to implement the CFI in a large organization for mental health services in the Netherlands, and (b) two studies that evaluated this implementation process and identified barriers to CFI adoption in clinical practice. Implementation of the CFI was facilitated by developing an online course, an advanced training of "team coaches", (digital) resources, and integrating the CFI questions into the intake format. A preliminary evaluation revealed that the CFI was administered in only 13.2% of all intakes across the entire organization, with minimal utilization of training resources. Study 1 aimed to investigate how the CFI was perceived by clinicians and stakeholders. A survey of 150 clinicians found a great lack of familiarity with the CFI and its purpose. While 67% reported partial CFI use, 50% saw no added value, and 61% deemed it relevant only for ethnic minorities. Study 2 examined which patient and clinician variables were associated with adequate CFI use (i.e., correct documentation of the CFI information in initial intake reports). The sample consisted of 112 intakes of patients conducted by ten clinicians. Regression analysis showed a significant association between clinicians' cultural competences and adequate CFI use, meaning that more culturally competent clinicians tended to generate better cultural assessments using the CFI. In addition, the CFI information was documented more adequately among patients who were unemployed at the time of assessment. In conclusion, implementation of the CFI requires a fundamental rethinking of the entire intake assessment, shifting it from a symptom-oriented approach towards a context- and person-centered one. Future trainings may benefit from embedding the CFI within a broader cultural competency training, rather than solely focusing on the CFI, which is currently the common practice.

Research on Big Data Capability and Medical Service Performance

With the rapid development of information technology, Big data technology has been widely applied in various fields.In the field of medical services, Big data capabilities not only help improve the efficiency and quality of medical services, but also promote the innovative development of the medical industry.This article deeply analyzes the connotation of Big data capabilities and their application in improving medical service performance, and combines the practical work of large-scale general hospitals and community medical service institutions to analyze the specific value and application effect of Big data in medical service practice. At the same time, based on medical reform, it summarizes the challenges, coping strategies, and development trends of Big data in medical service performance practice, with the ultimate goal of providing reference and guidance for practical work.

An expandable and cost-effective data center network

With the rapid growth of data volume, the escalating complexity of data businesses, and the increasing reliance on the Internet for daily life and production, the scale of data centers is constantly expanding. The data center network (DCN) is a bridge connecting large-scale servers in data centers for large-scale distributed computing. How to build a DCN structure that is flexible and cost-effective, while maintaining its topological properties unchanged during network expansion has become a challenging issue. In this paper, we propose an expandable and cost-effective DCN, namely HHCube, which is based on the half hypercube structure. Further, we analyze some characteristics of HHCube, including connectivity, diameter, and bisection bandwidth of the HHCube. We also design an efficient algorithm to find the shortest path between any two distinct nodes and present a fault-tolerant routing scheme to obtain a fault-tolerant path between any two distinct fault-free nodes in HHCube. Meanwhile, we present two local diagnosis algorithms to determine the status of nodes in HHCube under the PMC model and MM* model, respectively. Our results demonstrate that despite the presence of up to 25% faulty nodes in HHCube, both algorithms achieve a correct diagnosis rate exceeding 90%. Finally, we compare HHCube with state-of-the-art DCNs including Fat-Tree, DCell, BCube, Ficonn, and HSDC, and the experimental results indicate that the HHCube is an excellent candidate for constructing expandable and cost-effective DCNs.

StarCross: Redactable blockchain-based secure and lightweight data sharing framework for satellite-based IoT

Due to limitations in network coverage and capacity, terrestrial networks cannot meet the growing demand for widespread intelligent applications in recent years. The increasingly popular Satellite-based Internet of Things (S-IoT), with its extensive and boundless coverage, has emerged as the optimal solution. However, the large-scale distributed communication and services have led to significant challenges in scalability and security. Blockchain can help mitigate security and privacy issues in data sharing of S-IoT. To address these challenges, we propose StarCross, a secure and lightweight data sharing framework for S-IoT based on redactable blockchain. Firstly, we develop a space-ground collaborative sharding blockchain network architecture. Low Earth Orbit (LEO) satellite network serves as the communication intermediary between shards. Secondly, StarCross employs an improved Proof of Work (PoW) consensus based on dynamic difficulty to mitigate the challenges of uneven computing power and centralization within each shard. To ensure the atomicity of cross-shard transactions, StarCross introduces a blockchain rewriting mechanism called RBCVC, which combines chameleon hash and voting-based consensus. Thirdly, we conducted theoretical analysis and experimental evaluations of StarCross. The results indicate that compared to existing solutions, StarCross achieves better scalability and security in S-IoT. StarCross’s TPS increased to 260.9% of the baseline, while the transaction confirmation latency decreased by 79.7%.

Data analysis method for power big data

This paper discusses a data quality analysis and evaluation mode based on power big data. Its basic content is to use the analysis platform template to conduct acquisition control and process rule analysis, and then use the data quality management module to call out the platform database system that realizes the inspection and analysis function, and retrieve and generate inspection data from the entity base. The analysis program will classify, count, sort, and sort the data, The final generated quantitative indicator data reflecting the project implementation status and data quality will be saved in the analysis result table. The analysis result report called from the middle platform can obtain a more detailed data quality analysis and evaluation report reflecting the data quality of various quantitative projects. This paper has improved the intelligent degree of information quality management and evaluation, realized the intelligent control of information service quality, adapted to the needs of large-scale information service management, and completed the quantitative research and evaluation of information completeness, timeliness, accuracy, accuracy and other important parameters.

Digital innovation for cancer risk assessment allows large-scale service redevelopment of regional cancer genetics service delivery.

Family-history assessment can identify individuals above population-risk for cancer to enable targeted Screening, Prevention and Early Detection (SPED). The online patient-facing cancer Family History Questionnaire Service (cFHQS) is a digitalised, resource efficient tool for family history data capture to facilitate this. The capturing of digital data from cFHQS allows for data interrogation of patients referred to Clinical Genetics for the purposes of service improvement. Digital data from 4,044 cFHQS respondents over a three-year period was collected and interrogated with respect to the number and type of familial tumour diagnoses to enable service improvement and streamlining of referral pathways. 81% of colorectal and 71% of breast screening assessments were population- or moderate-risk. Most patients who completed cFHQS reported more than one diagnosis of cancer/tumour/polyps in their family. 2.5% of family history assessment patients had a second indication that required assessment that would have been missed if single tumour type assessment was undertaken. Implementation of an innovative, digital family history data collection pathway has allowed large scale interrogation of referral patterns and assessment outcomes to enable service development. The high volume of inappropriate referrals to Clinical Genetics for population and moderate risk patients highlighted the need for dedicated secondary care pathway provision for these patients. The use of cFHQS streamlined family history assessment allows for redistribution of resources to improve equity and access to genetic cancer risk assessment.

Dynamic heatmap pyramid computation for massive high-parallel spatial streaming in urban environments

ABSTRACT It is necessary to integrate data and information effectively in order to build an integrated digital and refined urban system. A multi-granularity and multi-view urban heat map formed by integrating multi-source urban information flow can assist in immediate decision-making. Managing urban big data in the form of streams requires a stable operating environment that can efficiently handle millions of sensors and devices connected to the Internet of Things (IoT) in a highly concurrent environment. Urban management data contain spatiotemporal multi-dimensional information that presents the complexity of spatiotemporal dynamic associations, further increasing the difficulty of data streaming. To overcome these challenges, we have proposed a spatiotemporal-pyramid (ST-pyramid) model that organizes multidimensional and dynamic data streams logically using a data partition strategy based on geographical grid subdivision. Accordingly, we have proposed a load-balanced heatmap pyramid computation framework that can be used to build streaming processing procedures in a distributed environment. In addition, we implemented an urban traffic heatmap prototype system based on an open-source Flink framework. The experimental results show that the real-time heatmap pyramid construction algorithm proposed in this paper has high throughput, low latency, and flexible scalability and can provide large-scale public services in time in digital earth construction.

Single Window Search System for Libraries using VuFind

The main objective of this study is to develop methods for enhancing the search capabilities of library collections by integrating various technological elements. Implementing this functional enhancement will provide users access to modern services and resources. The single-window search mechanism is created and built on the VuFind open-source discovery tool. The common access window has been created after properly configuring ILS-OPAC Koha, CONTENTdm, Digital Commons, DSpace, EPrints, Fedora, Greenstone, Omeka, and OPUS. It is based on LAMP architecture and uses Apache Solr, Tomcat, and Apache Tika to create indexes for easy document search through VuFind. This study suggests a new way to easily search and collect data from different domain-specific cluster tools using a single interface. This domain-specific framework provides an ideal environment for libraries to develop large-scale library discovery services. The originality of this paper lies in creating a prototype integrated framework to facilitate the easy installation and configuration of VuFind for integrating domain-specific software in both ILS and IDR.

Efficiently localizing system anomalies for cloud infrastructures: a novel Dynamic Graph Transformer based Parallel Framework

Cloud environment is a virtual, online, and distributed computing environment that provides users with large-scale services. And cloud monitoring plays an integral role in protecting infrastructures in the cloud environment. Cloud monitoring systems need to closely monitor various KPIs of cloud resources, to accurately detect anomalies. However, due to the complexity and highly dynamic nature of the cloud environment, anomaly detection for these KPIs with various patterns and data quality is a huge challenge, especially those massive unlabeled data. Besides, it’s also difficult to improve the accuracy of the existing anomaly detection methods. To solve these problems, we propose a novel Dynamic Graph Transformer based Parallel Framework (DGT-PF) for efficiently detect system anomalies in cloud infrastructures, which utilizes Transformer with anomaly attention mechanism and Graph Neural Network (GNN) to learn the spatio-temporal features of KPIs to improve the accuracy and timeliness of model anomaly detection. Specifically, we propose an effective dynamic relationship embedding strategy to dynamically learn spatio-temporal features and adaptively generate adjacency matrices, and soft cluster each GNN layer through Diffpooling module. In addition, we also use nonlinear neural network model and AR-MLP model in parallel to obtain better detection accuracy and improve detection performance. The experiment shows that the DGT-PF framework have achieved the highest F1-Score on 5 public datasets, with an average improvement of 21.6% compared to 11 anomaly detection models.

Taiwan national lung cancer early detection program for heavy smokers and non-smokers with family history of lung cancer.

8009 Background: Lung cancer incidence in Taiwan has been rising with the epidemiological profiles distinct from Western country. Notably, nonsmoking lung cancers accounts for more than 60% and half of lung cancers were diagnosed in advanced stages. For early detection, a national program is therefore initiated to offer biennial low-dose computed tomography (LDCT) screening for heavy smokers and subjects with lung cancer family history (LCFH). Here, we report basic screening findings, analyze relevant effectiveness in reducing advanced lung cancer, and project the potential benefit of mortality reduction. Methods: Taiwan National Lung Cancer Early Detection Program was launched on July 1, 2022 to offer heavy smokers (30 pack-years) and nonsmokers (including light smokers) with a LCFH aged 45-74 years for female and aged 50-74 years for male. This is a continuous screening program using before and after study design for evaluation. We provided the overall first-year basic screening findings until Dec 31, 2023 and did per protocol analysis of stage II+ cancer reduction in comparison with tumor stage distribution in the year preceding the introduction of the national program. The corresponding findings were presented for subjects with LCFH and heavy smokers. The result for heavy smoker was compared with those per-protocol findings from previous randomized controlled trials. The potential mortality reduction was further projected. Results: A total of 78,000 individuals participated in this program, male 44,381 (56.9%) female 33,619 (43.1%); 31,111 participants (39.9%) were heavy smokers, 43,853 (56.2%) nonsmokers but with a LCFH, and 3,036 (3.9%) had both risk factors. Lung cancer confirmed in 956 subjects by surgery or biopsy, 85.0% were stage 0-1. The lung cancer detection rate was 1.2%. Subjects with a LCFH exhibited a higher detection rate (overall: 1.6%, nonsmokers: 1.7% and light smokers 0.9%) compared to heavy smokers (0.7%) and both (1.1%). Early detection of LDCT led to a statistically significant 77% (51%-89%) of reducing advanced stage of lung cancer, 53% (-3%-78%) reduction for heavy smokers, consistent with the corresponding 36%-64% reduction derived from the NLST trial and the Nelson trial, and 85% (67%-93%) reduction for nonsmokers with a LCFH. The benefit of reducing stage II or severe in conjunction with five-year stage-specific survival rate further projected 55% (10%-78%) of mortality reduction from lung cancer. Conclusions: This is the first large-scale LC screening service enrolling 40% heavy smokers and 60% non-smoker with a LCFH. The potential effectiveness of early detection was demonstrated for both targets. As the results based on per-protocol analysis for heavy smoker were consistent with those of previous evidence-based studies the similar and significant intention-to-treat results as before while waiting for the subsequent follow-up would be expected.

EFACTLS: Effective Active TLS Fingerprinting for Large-Scale Server Deployment Characterization

EFACTLS: Effective Active TLS Fingerprinting for Large-Scale Server Deployment Characterization

Health Care Digital Strategy: Designing the New Norm

The time has arrived when oral and maxillofacial surgery (OMS) teams must redesign patient care by offering high-quality, easy, and cost-effective virtual care services through digital tools and telemedicine. Only a few pilot programs have been established to date, but now, the new norm requests large-scale routine services. The healthcare institutions should build this strategy based on their long-term goals. A prime example of its successful implementation is our unit at the National Institute of Children's Diseases (Bratislava, Slovak Republic), where a reliable telemedicine program was implemented for the outpatient follow-up visits of patients operated on for craniosynostosis. This program was instrumental in maintaining the long-lasting and high-impact character of craniosynostosis care during the coronavirus disease 2019 (COVID-19) crisis. The parents of the children were surveyed to evaluate the new program's effectiveness. The results were overwhelmingly positive, with an overall parental satisfaction rate of 72.3% and a 67.2% convenience rate for the services. The returned questionnaires revealed that 79.3% of parents were satisfied with physicians' communication, and 88.7% agreed that the virtual meetings adequately addressed their clinical needs. Moving forward, healthcare centers and physicians should organize their time and resources to experiment with the rapidly growing list of digital tools. The organizations should prioritize adapting their clinical information technology (IT) systems for optimal telemedicine practices. With the rapid technological advancements, new digital clinical systems should incorporate the capabilities of generative artificial intelligence (AI) in sophisticated virtual systems. This will require an even closer collaboration between clinicians, scholars, allied healthcare providers, healthcare leaders, and digital system designers. It is clear that in today´s complex healthcare ecosystem, the present and future of care delivery is digital. Will modern OMS teams adopt these strategies? It is something that all of us anticipate with huge interest.

Research on design strategy of public space service of commercial complex based on internet of things

The purpose of this study is to explore the design strategy of public space service of commercial complex based on the Internet of Things. By constructing a three-tier model of environmental energy consumption monitoring and management system for commercial complex, the application of network sensor technology is analyzed, and a data access interface based on Windows Communication Foundation (WCF) service is designed to realize batch hosting of WCF services and reduce the complexity of large-scale publishing services. In addition, this paper proposes a clustering method based on probabilistic topics (PTCM), which divides the service search space into probabilistic topics and effectively narrows the service search scope. The simulation results show that the algorithm shortens the shortest distance between mobile nodes and the average network distance, and improves the perceived service quality of the Internet of Things. This study not only provides new ideas and methods for the design of public space services in commercial complexes, but also provides useful reference for the application and promotion of Internet of Things technology in the field of public services.

Towards Intercity Mobility System – Insights into the Spatial Interaction Gravity Model and Determination Approach

The current development of urban agglomeration greatly promotes the intercity connection and elevates the significance of intercity mobility system. However, intercity mobility often exhibits extreme spatiotemporal imbalances due to the diverse urban characteristics. This poses a huge challenge for traffic management and reveals the necessity on understanding the urban attractiveness for intercity mobility, which is represented as spatial interaction gravity in this study. While recent works have explored relevant aspects, they failed to provide insights into temporal variations in spatial interaction gravity or capture the determining factors from multiple perspectives. To fill this gap, this study proposed a two-phase framework to measure the urban spatial interaction gravity and developed determination approaches utilising the large-scale location-based services (LBS) dataset. Specifically, the inverse gravity model was adopted for the measure within multiple urban agglomerations and city sets during weekdays, weekends and holidays. Then, we developed the fitting equations of spatial interaction gravity by incorporating the correlated features associated with social, economic, network accessibility and land use. The findings present spatial interaction gravity across different periods and substantiate the distinct determination effects of features, with a high fitting accuracy. They provide promising supports for the intercity mobility prediction and pre-emptive traffic management.

The Influence of News Agencies on Print and Online Publications/Newspapers

The purpose of the study is to analyse the dynamics of the influence of news agencies on the content of print and online publications within the media environment of Kazakhstan. The following methods were used: hypothetical and deductive, content analysis, and comparative. The results of the study have determined that rapid and intensive changes have been taking place in the Kazakh mass media and communications market in recent years, which are especially evident in the market of printed and online publications. It was found that news agencies, which are the standard of reliable data, are the main sources for newspaper materials. However, they still have the greatest influence on state publications, unlike private regional ones, which use publications of large-scale services more often. Content analysis of publications has determined that the number of citations by newspaper publications directly depends on the scale of events. The dynamics of news data from 2020 to 2023 highlighted that the number of mentions of the news agency in online and print publications is decreasing over time. In turn, all subjects of the media environment of Kazakhstan are reorienting themselves to the audience, actively using various platforms, social media, and digital opportunities.

Financial risk tolerance profiling from text

Traditionally, individual financial risk tolerance information is gathered via questionnaires or similar structured psychometric tools. Our abundant digital footprint, as an unstructured alternative, is less investigated. Leveraging such information can potentially support large-scale and cost-efficient financial services. Therefore, I explore the possibility of building a computational model that distills risk tolerance information from user texts in this study, and discuss the design principles discovered from empirical results and their implications. Specifically, a new quaternary classification task is defined for text mining-based risk profiling. Experiments show that pre-trained large language models set a baseline micro-F1 of circa 0.34. Using a convolutional neural network (CNN), the reported system achieves a micro-F1 of circa 0.51, which significantly outperforms the baselines, and is a circa 4% further improvement over the standard CNN configurations (micro-F1 of circa 0.47). Textual feature richness and supervised learning are found to be the key contributors to model performances, while other machine learning strategies suggested by previous research (data augmentation and multi-tasking) are less effective. The findings confirm user texts to be a useful risk profiling resource and provide several insights on this task.

Intelligent Pooling: Proactive Resource Provisioning in Large-scale Cloud Service

Intelligent Pooling: Proactive Resource Provisioning in Large-scale Cloud Service

Performance Evaluation of Noninvasive Prenatal Testing in Screening Chromosome Disorders: A Single-Center Observational Study of 15,304 Consecutive Cases in China.

This study was to evaluate the performance of noninvasive prenatal testing (NIPT) in detecting fetal chromosome disorders in pregnant women. From October 1st, 2017, to December 31th, 2022, a total of 15,304 plasma cell free DNA-NIPT samples were collected for fetal chromosome disorders screening. The results of NIPT were validated by confirmatory invasive testing or clinical outcome follow-up. Further, NIPT performance between low-risk and high-risk groups, as well as singleton pregnancy and twin pregnancy groups was compared. Besides, analysis of 111 false-positive cases was performed. Totally, NIPT was performed on 15,086 eligible venous blood samples, of which 179 (1.19%) showed positive NIPT results and 68 were further validated to be true positive samples via confirmatory invasive testing or follow-up of clinical outcomes. For common chromosome aneuploidies, sex chromosome abnormalities (SCA) and other chromosomal aneuploidies, the detection sensitivities of NIPT were all 100%, the specificities were 99.87%, 99.70%, and 99.68% and the positive predictive values (PPVs) were 65.45%, 31.82%, and 10.91%, respectively. No statistically significant variance in detection performance was observed among 2987 high-risk and 12,099 low-risk subjects, as well as singleton and twin pregnancy subjects. The concentration of cell-free fetal DNA of 111 false-positive cases ranged from 5.5% to 33.7%, which was higher than the minimum requirement of NIPT. With stringent protocol, NIPT shows high sensitivity and specificity for detecting fetal chromosome disorders in a large-scale clinical service, helping improving overall pregnancy management.