Large language models (LLMs) have shown remarkable capabilities in various natural language tasks and are increasingly being applied in healthcare domains. This work demonstrates a new LLM-powered disease risk assessment approach via streaming human-AI conversation, eliminating the need for programming required by traditional machine learning approaches. In a COVID-19 severity risk assessment case study, we fine-tune pre-trained generative LLMs (e.g., Llama2-7b and Flan-t5-xl) using a few shots of natural language examples, comparing their performance with traditional classifiers (i.e., Logistic Regression, XGBoost, Random Forest) that are trained de novo using tabular data across various experimental settings. We develop a mobile application that uses these fine-tuned LLMs as its generative AI (GenAI) core to facilitate real-time interaction between clinicians and patients, providing no-code risk assessment through conversational interfaces. This integration not only allows for the use of streaming Questions and Answers (QA) as inputs but also offers personalized feature importance analysis derived from the LLM's attention layers, enhancing the interpretability of risk assessments. By achieving high Area Under the Curve (AUC) scores with a limited number of fine-tuning samples, our results demonstrate the potential of generative LLMs to outperform discriminative classification methods in low-data regimes, highlighting their real-world adaptability and effectiveness. This work aims to fill the existing gap in leveraging generative LLMs for interactive no-code risk assessment and to encourage further research in this emerging field.

R-GMA (Relational Grid Monitoring Architecture) [1] is a grid monitoring and information system that provides a global view of data distributed across a grid system. R-GMA creates the impression of a single centralised repository of information, but in reality the information can be stored at many different locations on the grid. The Registry and Schema are key components of R-GMA. The Registry matches queries for information to data sources that provide the appropriate information. The Schema defines the tables that can be queried. Without the combined availability of these components, R-GMA ceases to operate as a useful service. This paper presents an overview of R-GMA and describes the Registry replication design and implementation. A replication algorithm for the Schema has also been designed.

This article describes the implementation of an accounting tool in the LHC Computing Grid (LCG): a distributed computing grid project consisting of over 100 resource centres and more than 10,000 CPUs. APEL (Accounting Processor for Event Logs) parses batch, system and gatekeeper logs generated by a site and builds accounting records, which provide a summary of the resources consumed based on attributes such as CPU time, Wall Clock Time, Memory and grid user DN. The accounting data is published into the R-GMA information and monitoring system, and archived for processing by a graphical front-end utilised by the accounting web tool. 1. LCG Accounting Overview In the LCG [1] grid environment, the computing resources, the application data and the grid users belonging to Virtual Organisations (VO) are distributed. Jobs submitted by these users may be sent to computing resources where the data is stored locally, or may go to remote resources where there are available job slots to reduce queue times. Jobs that run on LCG resources must be properly accounted for, so that the resources consumed by VOs and the resources provided by sites (resource centres), as a function of time can be determined. In this paper we distinguish between two flavours of grid accounting; “Grid Job accounting” in which a job usage record provides a complete description of resource consumption and, “Real-time Grid accounting”, which is based on an incremental determination of resource value while the job is being executed. In the former, the cost of computing is determined on the basis of what was done (after job execution), whilst in the latter, data feeds into a pricing model on which a cost for consumption is determined in real time. An implementation of real-time accounting (DGAS [2]) is under development within the gLite [3] middleware framework of the EGEE [4] grid-computing project. Below we summarise the implementation of a web-based job accounting tool that collects usage records from distributed resources using R-GMA [5] to a central point, and consolidates the information for presentation on the web. 2. Data Collection Mechanism and

The R-GMA (Relational Grid Monitoring Architecture) was developed within the EU DataGrid project, to bring the power of SQL to an information and monitoring system for the grid. It provides producer and consumer services to both publish and retrieve information from anywhere within a grid environment. Users within a Virtual Organization may define their own tables dynamically into which to publish data. Within the DataGrid project R-GMA was used for the information system, making details about grid resources available for use by other middleware components. RGMA has also been used for monitoring grid jobs by members of the CMS and D0 collaborations where information about jobs is published from within a job wrapper, transported across the grid by R-GMA and made available to users. An accounting package for processing PBS logging data and sending it to one or more Grid Operation Centres using R-GMA has been written and is being deployed within LCG. There are many other existing and potential applications. R-GMA is currently being re-engineered to fit into a Web Service environment as part of the EU Enabling Grids for E-science in Europe (EGEE) project. Improvements being developed include fine grained authorization, an improved user interface and measures to ensure superior scaling behaviour.

We describe how the R-GMA (Relational Grid Monitoring Architecture) can be used to allow for instrument monitoring in a Grid environment. The R-GMA has been developed within the European DataGrid Project (EDG) as a Grid Information and Monitoring System. It is based on the Grid Monitoring Architecture (GMA) from the Global Grid Forum (GGF), which is a simple Consumer-Producer model. The special strength of this implementation comes from the power of the relational model. It offers a global view of the information as if each Virtual Organisation had one large relational database. It provides a number of different Producer types with different characteristics; for example some support streaming of information. We describe the R-GMA component that allows for instrument monitoring, the CanonicalProducer. We also describe an example use of this approach in the European CrossGrid project, SANTA-G, a network monitoring tool.

We have developed and implemented the Relational Grid Monitoring Architecture (R-GMA) as part of the DataGrid project, to provide a flexible information and monitoring service for use by other middleware components and applications.

R-GMA is a realization of the Grid Monitoring Architecture (GMA) that also exploits the power of the relational data model and the SQL query language. The biggest challenge during the development of R-GMA was to ensure that it could be scaled to operate in a large grid reliably. The system is being used in areas as diverse as resource discovery, job logging and bookkeeping, network monitoring and accounting. A secure, reliable and scalable web services version of R-GMA is being developed within the follow-on European project EGEE. Work continues within GGF to define information services for OGSA on the basis of experience with R-GMA.

We describe R-GMA (Relational Grid Monitoring Architecture) which has been developed within the European DataGrid Project as a Grid Information and Monitoring System. Is is based on the GMA from GGF, which is a simple Consumer-Producer model. The special strength of this implementation comes from the power of the relational model. We offer a global view of the information as if each Virtual Organisation had one large relational database. We provide a number of different Producer types with different characteristics; for example some support streaming of information. We also provide combined Consumer/Producers, which are able to combine information and republish it. At the heart of the system is the mediator, which for any query is able to find and connect to the best Producers for the job. We have developed components to allow a measure of inter-working between MDS and R-GMA. We have used it both for information about the grid (primarily to find out about what services are available at any one time) and for application monitoring. R-GMA has been deployed in various testbeds; we describe some preliminary results and experiences of this deployment.

Computational Grids are distributed systems that provide access to computational resources in a transparent fashion. Collecting and providing information about the status of the Grid itself is called Grid monitoring.We describe R-GMA (Relational Grid Monitoring Architecture) as a solution to the Grid monitoring problem. It uses a local as view approach to information integration and will be a component of the European Union's DataGrid.The R-GMA architecture and mechanisms are general and could be used in other areas where there is a need for publishing and querying information in a distributed fashion.

We describe R-GMA (Relational Grid Monitoring Architecture) which is being developed within the European DataGrid Project as a Grid Information and Monitoring System for both the grid itself and for use by applications. Is is based on the GMA from GGF, which is a simple Consumer-Producer model. The special strength of this implementation comes from the power of the relational model. We offer a global view of the information as if each VO had one large relational database. We provide a number of different Producer types with different characteristics; for example some support streaming of information. We also provide combined Consumer/Producers, which are able to combine information and republish it. At the heart of the system is the mediator, which for any query is able to find and connect to the best Producers to do the job. In addition to having some of our own sensors able to publish information, we are able to invoke MDS info-provider scripts and publish the resulting information via R-GMA.

We describe R-GMA (Relational Grid Monitoring Architecture) which is being developed within the European DataGrid Project as an Grid Information and Monitoring System. Is is based on the GMA from GGF, which is a simple Consumer-Producer model. The special strength of this implementation comes from the power of the relational model. We offer a global view of the information as if each VO had one large relational database. We provide a number of different Producer types with different characteristics; for example some support streaming of information. We also provide combined Consumer/Producers, which are able to combine information and republish it. At the heart of the system is the mediator, which for any query is able to find and connect to the best Producers to do the job. We are able to invoke MDS info-provider scripts and publish the resulting information via R-GMA in addition to having some of our own sensors. APIs are available which allow the user to deploy monitoring and information services for any application that may be needed in the future. We have used it both for information about the grid (primarily to find what services are available at any one time) and for application monitoring. R-GMA has been deployed in Grid testbeds, we describe the results and experiences of this deployment.