Energy optimization is crucial for environmental sustainability, as it reduces resource consumption, minimizes greenhouse gas emissions, and promotes the use of renewable energy. Efficient energy use helps combat climate change and preserves natural ecosystems for future generations. In this paper, a system to support the distribution of photovoltaic energy for Emilia Romagna Energy Communities is proposed. The system will manage and integrate large amounts of data and offer innovative services based on them for calculating climate and energy forecasts. To enable more reliable production estimates and efficient energy storage and distribution, the system will use a platform for managing and integrating data from Regional Climate Models. It will incorporate Machine Learning and Deep Learning models for accurate climate forecasts and optimize energy flows by considering consumption profiles, production forecasts, and storage characteristics. The application background, the proposed methodology, and the current challenges related to the domain will be discussed, with a particular focus on data sources and management operations.

Technological innovation, conservation and enhancement are key elements for promoting cultural heritage and attracting visitors worldwide. Cultural tourism represents a significant economic and social strategy, capable of stimulating regional development and revitalizing marginalized areas. In recent years, artificial intelligence (AI) has transformed the sector, offering new ways to engage with and preserve cultural assets. This study explores the application of advanced AI techniques, particularly Convolutional Neural Networks (CNNs), to improve the classification and recognition of images related to architectural heritage. A deep learning algorithm specifically designed for cultural heritage enhancement is presented, focusing on the automatic classification of images of buildings and monuments. The analysis includes a comparison between pre-trained models and custom models, highlighting the performance of different approaches. The experimental results demonstrate that our ensemble model achieved 90% accuracy in classifying architectural heritage elements across 10 categories, with the Majority Vote Ensemble approach outperforming individual models by 6-10%. This improved classification accuracy enables more reliable automated systems for cultural heritage documentation and interactive tourist experiences. The research addresses key challenges in architectural heritage classification including variations in preservation state, lighting conditions, and complex backgrounds with multiple elements. The study also investigates the impact of data augmentation and class balancing techniques on model performance, demonstrating how these methods can mitigate limitations in training data availability. The developed ensemble combines state-of-the-art CNN architectures like ResNet50 and EfficientNet with custom models, leveraging their complementary strengths to achieve robust classification across diverse architectural styles and conditions. The adopted approach is supervised, using a training dataset where images are pre-labeled according to specific categories. This allows the algorithm to learn and subsequently predict the categories of new images based on the acquired information. A practical application is proposed to improve visitor experiences and heritage management, paving the way for future technological advancements in the field.

Advances in artificial intelligence are revolutionizing agricultural diagnostics, particularly in addressing the critical challenge of cassava disease detection. Cassava, a vital food source for millions worldwide, faces significant yield losses due to various diseases that threaten food security in developing regions. This research presents a novel approach integrating transfer learning with explainable AI to create a robust disease detection system. Through extensive experimentation with multiple deep learning architectures, our ResNet-based model achieves a remarkable accuracy of 92% in distinguishing among four major cassava diseases and healthy specimens. The integration of SHAP (SHapley Additive exPlanations) technology provides unprecedented transparency in the model’s decision-making process, allowing stakeholders to understand how the neural network identifies disease-specific features. Our system demonstrates particular strength in identifying Cassava Mosaic Disease, achieving 98% accuracy, while maintaining robust performance across bacterial blight, brown spot and green mite detection. The methodology presented here not only advances the technical frontier of agricultural AI but also provides a practical tool for enhancing food security through early disease detection. This research establishes a foundation for developing accessible and interpretable AI systems that can be deployed in resource-limited agricultural settings, potentially transforming how farmers manage crop health in the digital age.

The growing disparity between maize crop demand and actual production is concerning for both the food industry and farmers. Worldwide production of 1147.7 million MT of maize is insufficient to meet the demand of approximately 1149.96 million MT. Diseases like Turcicum Leaf Blight and Rust significantly hamper maize production. Manual disease detection, classification, severity calculation, and estimating crop loss are time-consuming and demand specific expertise. Hence, there’s a pressing need for automatic disease detection, severity prediction, and crop loss estimation. Machine learning and deep learning techniques, known for their success in pattern recognition and data analysis, have encouraged researchers to apply them in detecting diseases and estimating crop losses in maize. While existing literature showcases potential in disease detection, there’s a lack of reliable, real-world labeled datasets for training these models. Also, the focus on severity prediction and crop loss estimation is lacking in previous works. The paper provides a comprehensive overview of deep-learning approaches for Crop Loss Estimation in Maize Agriculture.

The rapidly increasing demand for energy and the consequent depletion of non-renewable energy sources pose significant challenges. Seeking alternatives, renewable sources like solar cells come into focus. Nevertheless, their limited efficiency hinders practical application and motivates researchers to develop more efficient solar cells. Through an examination of effectiveness, design viability, and fabrication costs, Dye-Sensitized Solar Cells (DSSC) emerge as superior to other photovoltaic solar cells. In particular the dye component is crucial for how well the DSSC works as it absorbs light from the sun.The paper investigates the topic related to forecasting the absorption maxima (λmax) of dyes and presents an overview of the proposals in the literature that use neural networks to forecast it. In addition, it discusses the main challenges related to this relevant topic, evidencing the need to address these challenges.

Agriculture is a vital industry for both the world’s food supply and economic health. The increasing global population and the demand for sustainable food production have led to the emergence of Artificial Intelligence (AI) as a game-changing technology to tackle agricultural issues. In this article, we explore the opportunities and challenges of AI-driven agriculture and provide an overview of the most promising applications and related ethical and practical issues.

Diabetic Retinopathy (DR) is a complication of diabetes, caused by a damage to the blood vessels in the light-sensitive tissue of the retina. Since it affects the eyes, it can determine visual impairment or even blindness. Considering the number of diabetic patients worldwide, it is clear that effective screening of potential DR patients is of utmost importance. While direct and indirect ophthalmoscopy are the main methods for evaluating DR, artificial intelligence is on the rise in vision care. DR is detectable by analyzing data from patients’ fundus photographs, and is therefore a disease that artificial intelligence tools can effectively support. In this paper, we present some preliminary numerical results obtained in discriminating between eye fundi of healthy individuals and of people with severe diabetic retinopathy, by using a Multiple Instance Learning approach.

At the end of 2019, the World Health Organization (WHO) referred that the Public Health Commission of Hubei Province, China, reported cases of severe and unknown pneumonia, characterized by fever, malaise, dry cough, dyspnoea and respiratory failure, which occurred in the urban area of Wuhan. A new coronavirus, SARS-CoV-2, was identified as responsible for the lung infection, now called COVID-19 (coronavirus disease 2019). Since then there has been an exponential growth of infections and at the beginning of March 2020 the WHO declared the epidemic a global emergency. An early diagnosis of those carrying the virus becomes crucial to contain the spread, morbidity and mortality of the pandemic. The definitive diagnosis is made through specific tests, among which imaging tests play an important role in the care path of the patient with suspected or confirmed COVID-19. Patients with serious COVID-19 typically experience viral pneumonia.

Consistent query answering is a principled approach for querying inconsistent databases. It relies on two basic notions: the notion of a repair, that is, a consistent database that "minimally" differs from the original one, and the notion of a consistent query answer, that is, a query answer that can be derived from every repair. In general, an inconsistent database can admit multiple repairs, each corresponding to a different way of restoring consistency, and the consistent query answering framework does not make any discrimination among them. However, in many applications it is natural and desired to express preferences among the different choices that can be made to resolve inconsistency.

Among skin cancers, melanoma is the most aggressive and most lethal form. Despite these terrible premises, an excision treatment carried out thanks to an early diagnosis is almost always decisive, guaranteeing the patient's survival. The early detection of melanoma is hampered by the extreme similarity of melanoma with other skin lesions such as dysplastic nevi. The current research is aimed at defining software solutions that support the computerized diagnosis of lesions for the detection of melanoma. To date, the proposals, both in terms of algorithms and frameworks, have focused on the dichotomous distinction of melanoma from benign lesions. However, the current debate on Dysplastic Nevi Syndrome (DNS), makes issues relating to the nature of the lesions, central to subjects who present a large number of moles throughout the body. In fact, individuals with DNS have a greater chance of being attacked by melanoma. The classification task relating to the distinction of dysplastic nevi from common ones is totally unexplored. In this document, we consider the difficult task of applying multiple-instance learning (MIL) approaches to discriminate melanoma from dysplastic nevi and outline an even more complex challenge related to the classification of dysplastic nevi from common ones. In particular, we introduce the application of a MIL approach that uses spherical separation surfaces. Since the results seem promising, we conclude that a MIL technique could be the basis of more sophisticated tools useful for detecting skin lesions.

Artificial Intelligent Systems are increasingly used to support early diagnosis of multiple relevant diseases. The spread of these systems is boosted by the application of machine learning techniques on datasets (also in the form of videos and images) obtained from different information sources. A key role is played by artificial vision systems that are in charge of reasoning on data acquired from different devices, including smartphones. The facility to disseminate and share information let to the globalization of medical protocols previously used just in some world's areas. This is the case of tongue inspection, widely used in Traditional Chinese Medicine (TCM) to perform a diagnosis, which allows physicians to obtain useful indications on the state of internal organs by observing the color and the consistency of patient's tongue. The current interest in tongue's image analysis is also motivated by the possibility of performing a first self-analysis on a possible disease suggesting further medical investigation. The paper is a non-exhaustive overview of the features most frequently used in artificial vision systems contextualized to tongue analysis. It highlights shortcomings in some of the existing studies and provides insights for future research. Our work aims to provide a unifying view that can support the researchers working on Tongue Colored Image Analysis.

Correct interpretation of images may be crucial for early disease detection. A growing number of medical instruments are image-oriented and produce a large quantity of image data, typically in the DICOM format, which contain spatio-temporal features together with alpha-numeric information regarding patients. Dealing with this high-dimensional datasets is a complex and time-consuming task. In addition the diffusion of smartphones and tablets requires the development of technological features enabling the medical team to check on helthcare processes on-the-go and freely access and send image and data for case analysis and collaborative diagnostic. This paper presents SIMPATICO 3D (Sistema Informativo Medico PATologIe COmplesse) a system supporting scientists and physicians by providing facilities for case studies analysis and diagnostic imaging in a shared virtual environment and details the features of SIMPATICO 3D Mobile (standing for Evolution Imaging System 3D for Mobile), that extends SIMPATICO 3D with dedicated functions for the mobile environment.

Extracting morphological features from DICOM images is useful to obtain numerical anatomic values for population-wide studies. Currently software tools on medical devices are able to extract some parameters that can indicate the presence of diseases. Nevertheless, there still is a lot of not exploited information contained in images which can be useful for research as well as to characterize human behavior. For instance, measures for lung volume compared with reference data sets can be studied starting from clinical images.

This paper proposes a logic based framework for data integration and query answering for deductive databases in a P2P environment. It is based on a special interpretation of mapping rules that leads to a declarative semantics for P2P systems defined in terms of preferred weak models. Under this semantics, only facts not making the local databases inconsistent can be imported, and the preferred weak models are the consistent scenarios in which peers import, by means of mapping rules, maximal sets of facts not violating (directly or indirectly) integrity constraints. The preferred weak models can be computed by means of a rewriting technique allowing to model a P2P system as a unique logic program whose stable models correspond to its preferred weak models. In the general case a P2P system may admit many preferred weak models and it has been shown that the complexity of their computation is prohibitive. Therefore, the paper looks for a more pragmatic solution assigning to a P2P system a new and more suitable semantics: the Well Founded Model Semantics. It allows to obtain a deterministic model whose computation is polynomial time. This model is a (partial) stable model obtained by evaluating with a three-value semantics the normal version of the rewriting of the P2P system. Finally, a distributed algorithm for the computation of the well founded model is proposed.

This paper proposes a system prototype for query answering in a Peer-to-Peer (P2P) network. As usual, a query can be posed to any peer in the system and the answer is provided by using locally stored data and all the information that can be consistently imported from its neighbors, i.e. the information satisfying integrity constraints collected using the semantic paths of mappings. The work stems from previous works of the same authors in which a declarative semantics for P2P systems is proposed. Under this semantics only facts not making the local databases inconsistent can be imported and the Preferred Weak Models are the consistent scenarios in which peers import maximal sets of facts not violating integrity constraints. In the general case, as a P2P system may admit many preferred weak models, the computational complexity results to be prohibitive, therefore this paper looks for a more pragmatic solution for assigning semantics to a P2P system. More specifically, it assigns to a P2P system its Well Founded Model, a partial deterministic model that captures the intuition that if an atom is true in a preferred weak model, but it is false in another one, then it is undefined in the well founded model. The paper presents a distributed algorithm for the computation of the well founded model and provides details on the implementation of a system prototype for query answering in P2P network based on the proposed semantics.

This paper aims to provide a contribution to the specific topic related to the integration of information and the computation of queries in an open ended network of distributed peers. Each peer joining a P2P system provides or imports data from its neighbors by using a set of mapping rules, i.e. a set of semantic correspondences to a set of peers belonging to the same environment. By using mapping rules, as soon as it enters the system, a peer can participate and access all data available in its neighborhood, and through its neighborhood it becomes accessible to all the other peers. In this setting two different types of mapping rules are possible: a first type allowing to import maximal sets of atoms and a second type allowing to import minimal sets of atoms from source peers to target peers. In the proposed setting, each peer can be thought of as a resource used either to enrich (integrate) the knowledge or to fix (repair) the knowledge. The declarative semantics of a P2P system is defined in terms of preferred weak models. The specific contributions of the present paper, that extends previous works of the same authors, consists in extending the classical notion of consistent answer by allowing the presence of partially defined atoms, i.e. atoms with "unknown" value due to the presence of tuples in different weak models which disagree on the value of one or more attributes.

This paper proposes a logic framework for modeling the interaction among incomplete and inconsistent deductive databases in a P2P environment. It stems from the work in [9, 13, 14, 16, 17] in which the declarative semantics of a P2P system is defined in terms of maximal weak models and the work in [15] in which the declarative semantics of a P2P system is defined in terms of minimal weak models. The maximal weak models semantics is based on the idea to use the mapping rules to import in each peer as much knowledge as possible without violating local integrity constraints. On the other hand under the Minimal Weak Models Semantics each peer uses its mapping rules to import minimal sets of mapping atoms allowing to satisfy its local integrity constraints. This latter behavior turns out to be useful in real world P2P systems in which peers often use the available import mechanisms to extract knowledge from the rest of the system only if this knowledge is strictly needed to repair an inconsistent local database. Therefore, each peer can be thought of as a resource used either to enrich (integrate) the knowledge (Maximal Weak Model Semantics) or to fix (repair) the knowledge (Minimal Weak Model Semantics). This paper extends previous works in [9, 13, 14, 16, 17] and in [15] by proposing a rewriting technique that allows modeling a P2P system, PS, as a unique logic program whose stable models correspond either to the maximal or minimal weak models of PS.

This paper proposes a logic framework for modeling the interaction among incomplete and inconsistent deductive databases in a P2P environment. Each peer joining a P2P system provides or imports data from its neighbors by using a set of mapping rules, i.e. a set of semantic correspondences to a set of peers belonging to the same environment. By using mapping rules, as soon as it enters the system, a peer can participate and access all data available in its neighborhood, and through its neighborhood it becomes accessible to all the other peers in the system. Two different types of mapping rules are defined: a first type allowing to import maximal sets of atoms and a second type allowing to import minimal sets of atoms from source peers to target peers. In the proposed setting, each peer can be thought of as a resource used either to enrich (integrate) the knowledge or to fix (repair) the knowledge. The declarative semantics of a P2P system is defined in terms of preferred weak models. An equivalent and alternative characterization of preferred weak model semantics, in terms of prioritized logic programs, is also introduced. The paper also presents preliminary results about complexity of P2P logic queries.

This paper investigates the data exchange problem among distributed independent sources. It is based on previous works of the authors [11, 12, 14] in which a declarative semantics for P2P systems has been presented and a mechanism to set different degrees of reliability for neighbor peers has been provided. The basic semantics for P2P systems defines the concept of Maximal Weak Models (in [11, 12, 14] these models have been called Preferred Weak Models. In this paper we rename them and use the term Preferred for the subclass of Weak Model defined here) that represent scenarios in which maximal sets of facts not violating integrity constraints are imported into the peers [11, 12]. Previous priority mechanism defined in [14] is rigid in the sense that the preference between conflicting sets of atoms that a peer can import only depends on the priorities associated to the source peers at design time. In this paper we present a different framework that allows to select among different scenarios looking at the properties of data provided by the peers. The framework presented here allows to model concepts like "in the case of conflicting information, it is preferable to import data from the neighbor peer that can provide the maximum number of tuples" or "in the case of conflicting information, it is preferable to import data from the neighbor peer such that the sum of the values of an attribute is minimum" without selecting a-priori preferred peers. To enforce this preference mechanism we enrich the previous P2P framework with aggregate functions and present significant examples showing the flexibility of the new framework.

This paper presents a technique that builds a layer of virtual sensors over a sensor network. The virtual sensors are able to infer and provide data for the physical sensors that do not work. The key assumption of our approach is that the physical quantities sensed by the sensors are related. The relations among sensors are unknown, but during a learning phase the layer of virtual sensors infers an approximation of them by means of fuzzy rules. The inferred fuzzy rules capture these relations in a simple way even when the corresponding mathematical models are complex. The set of fuzzy rules inferred for a node can be used to obtain virtual values when the real ones are not available. In order to develop our technique we improved the Tree Routing Protocol in charge to deliver data from the nodes to the base station and used Snlog, a Datalog-like language that supports the implementation of distributed algorithms for Wireless Sensor Network in a declarative way. We developed a system prototype and performed preliminary experiments that prove the validity of our approach.

Virology research is nowadays a discipline involving a broad number of researchers gathered in different institutes and cooperating on defined issues. An example of such an endeavor is the research tackling anti-HIV treatment problems [7] conducted within the Virolab project. The main objective of the ViroLab project is to develop a Virtual Laboratory for Infectious Diseases that facilitates medical knowledge discovery and decision support for HIV drug resistance. Large, high quality in-vitro and clinical patient databases which can be used to relate genotype to drug-susceptibility phenotype have become available. The core of the ViroLab Virtual Laboratory is a rule-based ranking system. More specifically, using a Grid-based service oriented architecture, Virolab vertically integrates the biomedical information from viruses (proteins and mutations), patients and literature (drug resistance experiments), resulting in a rule-based decision support system for drug ranking. This paper is a contribution to virologists, epidemiologists and clinicians in medical knowledge discovery and decision support. The final aim is reasoning on the properties of algorithm modeling the interaction among drugs and HIV virus and detecting its anomalies such as rules that can never be satisfied and subset of rules that are in contradiction.

This paper introduces active integrity constraints (AICs), an extension of integrity constraints for consistent database maintenance. An active integrity constraint is a special constraint whose body contains a conjunction of literals which must be false and whose head contains a disjunction of update actions representing actions (insertions and deletions of tuples) to be performed if the constraint is not satisfied (that is its body is true). The AICs work in a domino-like manner as the satisfaction of one AIC may trigger the violation and therefore the activation of another one. The paper also introduces founded repairs, which are minimal sets of update actions that make the database consistent, and are specified and ldquosupportedrdquo by active integrity constraints. The paper presents: 1) a formal declarative semantics allowing the computation of founded repairs and 2) a characterization of this semantics obtained by rewriting active integrity constraints into disjunctive logic rules, so that founded repairs can be derived from the answer sets of the derived logic program. Finally, the paper studies the computational complexity of computing founded repairs.

The paper proposes a logic framework for modeling the interaction among deductive databases and computing consistent answers to logic queries in a P2P environment. As usual, data are exchanged among peers by using logical rules, called mapping rules. The novelty of our approach is that only data not violating integrity constraints are exchanged. The (declarative) semantics of a P2P system is defined in terms of weak models. Under this semantics only facts not making the local databases inconsistent can be imported, and the preferred weak models are the consistent scenarios in which peers import maximal sets of facts not violating integrity constraints. A characterization of the preferred weak model semantics, allowing to model a P2P system with a prioritized logic program, is provided. The proposed framework is then extended in order to also take into account P2P system in which each peer may be locally inconsistent, i.e. its data does not satisfy some of its constraints. Finally, the complexity of P2P logic queries is investigated

This paper presents a logic language, called NP Datalog for NP search and optimization problems. The 'search' language extends stratified Datalog with constraints and partition rules defining (nondeterministically) partition of relations. NP optimization problems are then formulated by adding a max (or min) construct to select the solution (stable model) which maximizes (resp., minimizes) the result of a polynomial function applied to the answer relation. We show that NP Datalog queries can be easily evaluated by translating them into ILOG programs which are next solved by means of the ILOG OPL Studio suite. To prove the effectiveness of our proposal, we have implemented a module, written in Sicstus Prolog, which takes in input a NP Datalog query and outputs an equivalent ILOG program. Several experiments comparing the computation of queries by different logic systems have been also performed.

The objective of this paper is to investigate the problems related to the extensional integration of information sources. In particular, we propose an approach for managing inconsistent databases, i.e. databases violating integrity constraints. The presence of inconsistent data can be resolved by "repairing" the database, i.e. by providing a computational mechanism that ensures obtaining consistent "scenarios" of the information or by consistently answering to queries posed on an inconsistent set of data. In this paper we consider preferences among repairs and possible answers by introducing a partial order among them on the base of some preference criteria. More specifically, preferences are expressed by considering polynomial functions applied to repairs and returning real numbers. The goodness of a repair is measured by estimating how much it violates the desiderata conditions and a repair is preferred if it minimizes the value of the polynomial function used to express the preference criteria. The main contribution of this work consists in the proposal of a logic approach for querying and repairing inconsistent databases that extends previous works by allowing to express and manage preference criteria. The approach here proposed allows to express reliability on the information sources and is also suitable for expressing decision and optimization problems. The introduction of preference criteria strongly reduces the number of feasible repairs and answers; for special classes of constraints and functions it gives a unique repair and answer.

The proliferation of information available on the World Wide Web and the new emerging technologies that have reduced the barriers in organizing and publishing documents, have made the support for navigation and personalization of Web sites an appealing and promising task for the Web community. One of the most challenging activities in the design of modern sites which goes beyond any particular domain consists of making the process of retrieving relevant documents easier. This paper proposes a new technique for Web navigation based on current algorithms used in recommendation systems. Our approach identifies really relevant documents adopting methodologies similar to those successfully used in current search engines. This approach has been effectively used for the implementation of a lightweight Web site personalization tool, that permits to navigate towards relevant Web pages regardless of the original Web site structure.

In this paper, we address the problem of managing inconsistent databases, i.e., databases violating integrity constraints. We propose a general logic framework for computing repairs and consistent answers over inconsistent databases. A repair for a possibly inconsistent database is a minimal set of insert and delete operations which makes the database consistent, whereas a consistent answer is a set of tuples derived from the database, satisfying all integrity constraints. In our framework, different types of rules defining general integrity constraints, repair constraints (i.e., rules defining conditions on the insertion or deletion of atoms), and prioritized constraints (i.e., rules defining priorities among updates and repairs) are considered. We propose a technique based on the rewriting of constraints into (prioritized) extended disjunctive rules with two different forms of negation (negation as failure and classical negation). The disjunctive program can be used for two different purposes: to compute "repairs" for the database and produce consistent answers, i.e., a maximal set of atoms which do not violate the constraints. We show that our technique is sound, complete (each preferred stable model defines a repair and each repair is derived from a preferred stable model), and more general than techniques previously proposed.

In the last few years, a lot of research has been devoted to developing new techniques for improving the recall and precision of current Web search engines. Few works deal with the interesting problem of identifying the communities to which pages belong. Most previous approaches tried to cluster data by means of spectral techniques or traditional hierarchical algorithms. The main problem with these techniques is that they ignore the fact that Web communities are social networks with distinctive statistical properties. We analyze Web communities on the basis of the evolution of an initial set of hubs and authoritative pages. The evolution law captures the behaviour of page authors with respect to the popularity of existing pages for topics of interest. Assuming such a model, we have found interesting properties of Web communities and have proposed a technique for computing relevant properties for specific topics. Several experiments have confirmed the validity of both the model and the identification method.

The World Wide Web (WWW) is becoming the most important system for delivering information. Search services on the WWW are becoming increasing popular among users because of the huge amount of data available and consequently it is difficult to retrieve and filter it. Several works have argued that traditional term-based search engines are not very useful since the resulting ranking depends on the precision of the user in expressing the query. However, usually, users are unclear about the information they need and so they do not give much thought to query formulation. Moreover, if the query pertains to topics which are abundant on the Web, search services become unusable because of the huge number of pages obtained. For instance, at the time of this work, AltaVista returned more than 18,000,000 pages in reply to the query asking for the documents related to the word "java".

The authors discuss data models and query languages for XML data. They propose a data model, called XDT, which takes care of the structural features of XML data. XDT represents XML data by means of labeled oriented graphs. With respect to other previously proposed models, XDT handles links definable in both XML and XLL. Queries are made through an SQL-like language which uses weighted path queries, i.e. path queries based on weighted regular languages and gives, as a result, a set of pairs (node/weight) where node is an 'element' of an XML document and weight gives information about the relevance of the node.