1,721,159 research outputs found
URBE: Web service Retrieval based on Similarity Evaluation
No full textIn this work, we present Uddi Registry By Example (Urbe), a novel approach for Web service retrieval based on the evaluation of similarity between Web service interfaces. Our approach assumes that the Web service interfaces are defined with Web Service Description Language (WSDL) and the algorithm combines the analysis of their structures and the analysis of the terms used inside them. The higher the similarity, the less are the differences among their interfaces. As a consequence, Urbe is useful when we need to find a Web service suitable to replace an existing one that fails. Especially in autonomic systems, this situation is very common since we need to ensure the self-management, the self-configuration, the self-optimization, the self-healing, and the self-protection of the application that is based on the failed Web service. A semantic-oriented variant of the approach is also proposed, where we take advantage of annotations semantically enriching WSDL specifications. Semantic Annotation for WSDL (SAWSDL) is adopted as a language to annotate a WSDL description. The Urbe approach has been implemented in a prototype that extends a Universal Description, Discovery and Integration (UDDI) compliant Web service registry
Adopting Data Mesh principles to Boost Data Sharing for Clinical Trials
No full textAn effective clinical research requires the availability of relevant data and tools that make possible their efficient analysis. Among the several possibilities, data mesh, a distributed data architecture that is organized around specific domains and provides a self-service platform for accessing and using the data, is gaining the attention of the data and software engineering communities, mainly because of its ability to reduce the tension between the platform that manages the data and the teams that are in charge of managing them. Nevertheless, data mesh mainly focuses on how to manage data in a single organization by defining the sphere of responsibilities in the data management. Conversely, the continuous increase of data produced by hospitals calls for new approaches that enable the data sharing between clinical research centers.Goal of this paper is to extend the data mesh approach by considering the sharing of data among organizations which are members of a federation. Under the umbrella of a clinical trial which defines a temporary agreement among hospitals, a federated data mesh solution is designed to support the data management when data products from different organizations are considered. This implies the study on how the data ownership defined in the data mesh somehow becomes data sovereignty when data is shared with other organizations
A methodology for developing trusted information systems: the security requirements analysis phase
No full textTrustworthy Collaborative Business Intelligence Using Zero-Knowledge Proofs and Blockchains
No full textIn the era of data-driven decision-making, the ability to securely and reliably exchange analytical data among organizations (collaborative business intelligence) is becoming increasingly important. This paper envisions a novel framework for trustworthy data exchange, leveraging Zero-Knowledge Proofs (ZK-Proofs) to maintain data privacy and integrity, and the blockchain for reliable auditing. Our framework emphasizes enhancing business intelligence capabilities through non-operational analytics, particularly in the generation of aggregated insights for strategic decision-making among different organizations, without exposing the underlying raw data, thus preserving data sovereignty. We introduce a methodology to perform operations on data cubes using ZK-Proofs, allowing for the generation of more aggregated data cubes from initial raw data hypercubes. The framework exploits the Data-Fact Model to identify the available transformation paths on raw data
Improving Content-Based Data Product Retrieval in Federated Environments with LLM and Sampling
No full textData products have emerged as a powerful paradigm for managing data in both intra-enterprise and federated environments, providing structured data assets that include not only the data itself but also services, metadata, and access policies. However, a key challenge in federated environments is the discovery of relevant data products. Traditional discovery mechanisms are heavily dependent on metadata, which is often inconsistent, incomplete, or not standardized across organizations. This lack of metadata quality significantly limits the effectiveness of discovery, making it difficult for consumers to identify and retrieve the data they need. To address this challenge, we propose a content-based discovery framework that shifts the focus from metadata to the actual content of data products. Our approach uses sampling techniques to extract meaningful data representations and a tabular retrieval model for natural language queries. Directly interacting with data improves discovery accuracy, enabling effective data access in federated environments
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