Nowadays, various frameworks are emerging for supporting distributed tracing techniques over microservices-based distributed applications. The objective is to improve observability and management of operational problems of distributed applications, considering bottlenecks in terms of high latencies in the interaction among the deployed microservices. However, such frameworks provide information that is disjoint from the management information that is usually collected by cloud computing orchestration platforms. There is a need to improve observability by combining such information to easily produce insights related to performance issues and to realize root cause analyses to tackle them. In this paper, we provide a modern observability approach and pilot implementation for tackling data fusion aspects in edge and cloud computing orchestration platforms. We consider the integration of signals made available by various open-source monitoring and observability frameworks, including metrics, logs and distributed tracing mechanisms. The approach is validated in an experimental orchestration environment based on the deployment and stress testing of a proof-of-concept microservices-based application. Helpful results are produced regarding the identification of the main causes of latencies in the various application parts and the better understanding of the behavior of the application under different stressing conditions.

The integration of Knowledge Graphs (KGs) with Question Answering (QA) systems is transforming the landscape of Artificial Intelligence (AI). Through the combination of these technologies, novel features can be provided for the translation of questions in natural language into database queries. Even if a lot of work is emerging in this domain, this is not the case when we refer to translation of text to Cypher queries, where Cypher is one of the dominant query languages used for the development of KGs (e.g., based on the Neo4j technology). In this context, this paper provides a robust and efficient framework to systematically assess the efficiency of Large Language Models (LLMs) to support Text-to-Cypher conversion, focusing on the evaluation of open-source LLMs. The framework utilizes metrics and validators offered by an open-source software library that we developed, called CyVer. This study also assesses the impact of different schema representations of the KG on schema-aware query generation and the performance of LLMs on questions of different complexity requiring a depth of reasoning on the KG. A case study is described based on the application of the detailed framework in a KG with a large and complex schema that hosts data to track information related to the Sustainable Development Goals (SDGs). The experimental results demonstrate the effectiveness of the proposed framework, highlight the importance of the size of open-source models in the semantic comprehension of questions and the generation of valid Cypher queries, and stress the challenge for the generation of accurate queries in the case of questions requiring complex Cypher logic.

The way towards sustainable development is paved through the commitment to the 17 Sustainable Development Goals (SDGs), which encompass a wide range of global challenges. The successful progress of these goals depends on the identification and understanding of their interconnected nature. A plethora of data is made available for tracking targets related to the SDGs at country, regional and urban levels. However, various challenges are identified to semantically align and homogeneously represent such data to improve their interoperability, comparability and analysis. In the current work, we provide an innovative solution for analyzing SDG-oriented data based on the development of a Knowledge Graph that provides access to semantically aligned data for the SDGs. We consider Knowledge Graphs as a suitable technology for the representation of data related to the interlinkages among SDGs, since they provide a structured representation of knowledge that incorporates entities, relationships and attributes, organized in a graph format. We examine the interlinkages among indicators of the same SDG, as well as across indicators of the various SDGs. Such interlinkages are further evaluated as synergies or trade-offs. Our analysis is applied in country and regional levels, considering various constraints in terms of data quality and availability. In total 476 synergies are identified at the national level among the SDGs, compared to 140 trade-offs. The SDGs that mostly participate in the synergies are SDGs 17, 10, 9 and 8, while SDGs 7 and 16 participate in most of the trade-offs. At the regional level, SDGs 8, 4 and 9 are more active in terms of interlinkages.