Knowledge Graphs (KGs) are powerful tools for organizing and extracting knowledge across various domains. However, KGs scaling in size and complexity make operations such as query answering and machine learning more challenging. These challenges are especially pressing in resource-constrained environments, where sustainability and efficiency become critical design goals, and can be addressed through controlled KG reduction that preserves semantic integrity. In this context, this paper introduces a novel application of approximate computing through controlled entity ablation for efficient KG reduction. We systematically evaluate multiple ablation strategies with different KG embedding techniques, analysing their impact on link prediction accuracy, semantic integrity, and sustainability metrics. The results demonstrate that selective and controlled ablation (of up to 20% of the entities) preserves the core semantic structure of KGs while yielding reductions in energy consumption (of up to 11,5%) and computational overhead (up to 10% savings in total training time). Furthermore, we propose a machine learning-based framework to dynamically assess and optimise ablation strategies based on the KG characteristics and the desired quality thresholds. This work underscores the potential of approximate computing paradigms to provide sustainable solutions that meet the demands of modern data-intensive applications.

Knowledge graphs (KGs) play a crucial role in the integration and organization of heterogeneous data and knowledge, enabling advanced data analytics and decision-making across various industries. This vision paper addresses critical challenges in managing KGs, emphasizing their relevance in integrating information from disparate sources. We propose the concept of knowledge graph ecosystems and life cycles to systematically manage tasks, e.g., data integration, standardization, continuous updates, efficient querying, and provenance tracking. By adopting our approach, organizations can enhance the accuracy, consistency, and reliability of KGs, thus improving knowledge management, enabling the extraction of valuable insights, and ensuring transparency and accountability.

Abstract Data play a key role in AI systems that support decision-making processes. Data-centric AI highlights the importance of having high-quality input data to obtain reliable results. However, well-preparing data for machine learning is becoming difficult due to the variety of data quality issues and available data preparation tasks. For this reason, approaches that help users in performing this demanding phase are needed. This work proposes DIANA, a framework for data-centric AI to support data exploration and preparation, suggesting suitable cleaning tasks to obtain valuable analysis results. We design an adaptive self-service environment that can handle the analysis and preparation of different types of sources, i.e., tabular, and streaming data. The central component of our framework is a knowledge base that collects evidence related to the effectiveness of the data preparation actions along with the type of input data and the considered machine learning model. In this paper, we first describe the framework, the knowledge base model, and its enrichment process. Then, we show the experiments conducted to enrich the knowledge base in a particular case study: time series data streams.

The presence of accounts managed by cybersecurity experts, professionals, and organizations makes social media a valuable source for computer security awareness. By regularly capturing and analyzing the posts on emerging cyber threats, individuals and organizations can understand potential dangers in a timely manner and effectively implement mitigation strategies. However, retrieving relevant and informative posts from a social network is challenging due to the high percentage of posts containing uninformative content. This paper proposes a novel approach based on supervised classifiers for selecting relevant social media posts and categorizing them according to different types of vulnerabilities. To accomplish this task, we designed a pipeline combining text classifiers in cascade, training them on manually labelled data. We analyzed various neural network techniques, leveraging language-agnostic sentence-level embeddings and past user activity, validating these techniques in a cross-validation setup. With an achieved accuracy of 87%, our approach offers effective filtering and classification of social media posts, empowering cybersecurity professionals to stay informed and take appropriate measures.

Conversational agents are emerging as channels for a natural and accessible interaction with digital services. Their benefits span across a wide range of usage scenarios and address visual impairments and any situational impairments that may take advantage of voice-based interactions. A few works highlighted the potential and the feasibility of adopting conversational agents for making the Web truly accessible for everyone. Yet, there is still a lack of concrete guidance in designing conversational experiences for browsing the Web. This paper illustrates a human-centered process that involved 26 blind and visually impaired people to investigate their difficulties when using assistive technology for accessing the Web, and their attitudes and preferences on adopting conversational agents. In response to the identified challenges, the paper introduces patterns for conversational Web browsing. It also discusses design implications that can promote Conversational AI as a technology to enhance Web accessibility.

The combination of data and technology is having a high impact on the way we live. The world is getting smarter thanks to the quantity of collected and analyzed data. However, it is necessary to consider that such amount of data is continuously increasing and it is necessary to deal with novel requirements related to variety, volume, velocity, and veracity issues. In this paper we focus on veracity that is related to the presence of uncertain or imprecise data: errors, missing or invalid data can compromise the usefulness of the collected values. In such a scenario, new methods and techniques able to evaluate the quality of the available data are needed. In fact, the literature provides many data quality assessment and improvement techniques, especially for structured data, but in the Big Data era new algorithms have to be designed. We aim to provide an overview of the issues and challenges related to Data Quality assessment in the Big Data scenario. We also propose a possible solution developed by considering a smart city case study and we describe the lessons learned in the design and implementation phases.

As cities are becoming green and smart, public information systems are being revamped to adopt digital technologies. There are several sources (official or not) that can provide information related to a city. The availability of multiple sources enables the design of advanced analyses for offering valuable services to both citizens and municipalities. However, such analyses would fail if the considered data were affected by errors and uncertainties: Data Quality is one of the main requirements for the successful exploitation of the available information. This paper highlights the importance of the Data Quality evaluation in the context of geographical data sources. Moreover, we describe how the Entity Matching task can provide additional information to refine the quality assessment and, consequently, obtain a better evaluation of the reliability data sources. Data gathered from the public transportation and urban areas of Curitiba, Brazil, are used to show the strengths and effectiveness of the presented approach.

The increasing utilization of cloud resources raises several issues related to their environmental impact and, more in general, sustainability. Recently, most of the contributions have focused on energy efficiency achieved through a better physical and virtual resource management. The present paper considers instead the application level, extending the focus to the reduction of CO2emissions related to the execution of applications. We aim to exploit adaptivity through the design of an Application Controller that, enacting the right adaptation strategy for a given context, allows the improvement of the trade off between QoS and CO2emission reduction. The effectiveness of the approach has been shown running an HPC application in a federated cloud infrastructure.

With the increasing popularity of the cloud computing model and rapid proliferation of cloud infrastructures there are increasing concerns about energy consumption and consequent impact of cloud computing as a contributor to global CO2emissions. To date, little is known about how to incorporate energy consumption and CO2concerns into cloud application development and deployment decision models. In this respect, this paper describes an eco-aware approach that relies on the definition, monitoring and utilization of energy and CO2metrics combined with the use of innovative application scheduling and runtime adaptation techniques to optimize energy consumption and CO2footprint of cloud applications as well as the underlying infrastructure. The eco-aware approach involves measuring or quantifying the energy consumption and CO2at different levels of cloud computing, using that information to create scheduling and adaptation techniques that contribute towards reducing the energy consumption and CO2emissions, and finally testing and validating the developed solutions in a multi-site cloud environment with the help of challenging case study applications. The experimental and validation results show the potential of the eco-aware approach to significantly reduce the CO2footprint and consequent environmental impact of cloud applications.

In recent years, models, composition paradigms, and tools for mashup development have been proposed to support the integration of information sources, services and APIs available on the Web. The challenge is to provide a gate to a “programmable Web,” where end users are allowed to construct easily composite applications that merge content and functions so as to satisfy the long tail of their specific needs. The approaches proposed so far do not fully accommodate this vision. This article, therefore, proposes a mashup development framework that is oriented toward the End-User Development. Given the fundamental role of user interfaces (UIs) as a medium easily understandable by the end users, the proposed approach is characterized by UI-centric models able to support a WYSIWYG (What You See Is What You Get) specification of data integration and service orchestration. It, therefore, contributes to the definition of adequate abstractions that, by hiding the technology and implementation complexity, can be adopted by the end users in a kind of “democratic” paradigm for mashup development. This article also shows how model-to-code generative techniques translate models into application schemas, which in turn guide the dynamic instantiation of the composite applications at runtime. This is achieved through lightweight execution environments that can be deployed on the Web and on mobile devices to support the pervasive use of the created applications.

Internet of Things (IoT) involves heterogeneous technologies (i.e., WSN, RFID, actuators) able to exchange data acquired from the environment in order to provide services to the requesting users. In such a scenario the privacy and the quality (i.e., in terms of accuracy, timeliness, completeness) of the handled information represent critical issues. In fact, the provided services must be customized according to the users preferences and habits and have to manage both users personal information and data from different sources, therefore it needs to guarantee privacy and data quality level. This work proposes a UML general conceptual model, which defines the entities involved in the IoT context, their relationships, facing privacy policies definition and data quality assessment. Such a model should represent a starting point for the development of IoT privacy-aware solutions, handling data with a well-defined quality.

Service identification is one of the main phases in the design of a service-oriented application. The way in which services are identified may influence the effectiveness of the SOA architecture. More specifically, the granularity of the services is very important in reaching flexibility and reusing them. Such properties are crucial in interorganizational interactions based on collaborative business processes. In fact, collaboration is facilitated by ensuring a homogeneous description of services at the right level of granularity. In this paper, we provide a detailed description of P2S (Process-to-Services), a computer-aided methodology to enable the identification of services that compose a collaborative business process. The methodology is based on metrics defined to setup service granularity, cohesion, coupling, and reuse. A prototype tool based on the methodology is also described with reference to a real case scenario.

The development and maintenance of cloud sites are often characterized by energy waste and high CO2 emissions. Energy efficiency and the decrease of the CO2 emissions in cloud-based systems can be only obtained by adopting suitable actions and techniques (e.g., utilization of green energy sources, reduction of the number of physical and virtual machines, usage of the greener machines). In order to evaluate the suitability of these different actions, it is necessary to define a measure for greenness of the whole system. For this reason, this paper defines a set of metrics to assess the greenness of a cloud infrastructure. In order to provide a detailed view of the behaviour of the system and to facilitate the identification of the causes of the energy waste, metrics have been defined at different layers of the system (i.e., application, virtualization, infrastructure layers). The monitoring infrastructure that is necessary to retrieve all the data required for the assessment of the identified set of metrics is also described.

SUMMARY Mobile devices with their more and more powerful resources allow the development of mobile information systems in which services are not only provided by traditional systems but also autonomously executed and controlled in the mobile devices themselves. Services distributed on autonomous mobile devices allow both the development of cooperative applications without a back‐end infrastructure and the development of applications blending distributed and centralized services. In this paper, we propose MicroMAIS: an integrated platform for supporting the execution of Web service‐based applications natively on a mobile device. The MicroMAIS platform is composed of mAS and μ‐BPEL. The former allows the execution of a single Web service, whereas the latter permits the orchestration of several Web services according to the WS‐BPEL standard. Copyright © 2011 John Wiley & Sons, Ltd.