In our previous blog post, we explored how useful sentiment analysis and opinion mining can be in policymaking. Focusing on how to involve citizens in the policymaking process and use their feedback for local governments to develop more citizen-centric policies, the AI4PublicPolicy team has designed a...
The use of Natural Language Processing (NLP) and opinion-mining methods have been utilized throughout the last years through sentiment analysis to detect, obtain, compute, and examine information by organizations, in order to evaluate and improve their customer feedback (Soussan T. & Trovati M., 2022). There...
In the first part of our blog series on Sequence Diagrams for policy and data management, we explored two key diagrams: the "Definition of a Dataset Schema" and the "List of Datasets." These diagrams provided a detailed visual representation of the processes involved in defining...
This blog presents the sequence diagrams that show the interaction between the user and the different sub-components of the Policy and Dataset Management component. Definition of a Dataset Schema The following sequence diagram shows the step-by-step process when a user defines a new dataset schema. It starts...
Policy and Data Management Component Architecture The Policy and Data Management component is made out of five subcomponents: the Web Interface, the Dataset Management component, the Policy Management component, a Database (DB) and EGI DataHub as depicted in the following figure.The Web Interface allows non-technical...
The Policy and Data Management component provides two different user interfaces: the web interface and the REST API interfaces. Users can select the one that is more suitable/intuitive for them. The REST APIs are also used for the integration of this component with other components...
The Data Management component is in charge of the collection and management of the datasets from different stakeholders in the AI4PublicPolicy. The dataset management component loads and stores the data to be analyzed by the Virtualized Policy Management Environment (VPME). Data can be collected from...
In the second and final part of the blog series on the Infrastructure of AI4PublicPolicy services, light is shed on the Compute and Data Federation layer and the Platforms layer. Compute and Data Federation The Compute and Data Federation layer provides advanced solutions to manage the...
The AI4PublicPolicy services are built on top of the multi-layered EOSC Compute Platform Its main layers are the Federated Resource Providers layer, the Compute and Data Federation layer, the Platforms layer, and the vertical Service Management Tools layer. The vertical Service Management Tools layer includes Helpdesk, Monitoring,...
In our previous blog post about the main components of the AI4PublicPolicy platform, the AI Security, AutoML and Text and Sentiment Analysis components have been described – find the 4th blog post on the platform components here. In this 5th and final part of the AI4PublicPolicy...