The increasing adoption of assisted gait systems necessitates intelligent architectures capable of adapting to diverse movement styles while ensuring equitable performance. The paper introduces AdaptiveGait, an innovative edge-cloud architecture for fall detection that integrates fairness-by-design principles and adaptability to various movement patterns. The system first classifies actions into four categories (idle, fall, step, motion) and then performs a double classification at two levels of granularity, through a KMeans-based clustering mechanism which utilizes Gravity Deviation and Angular Deviation metrics to determine the degree of criticality according to the user's gait style. Using the Walker Fall Detection Dataset (2,480 samples), we demonstrate that AdaptiveGait achieves an F1-Score of 99%, outperforming traditional approaches by 5–7% when classifying both actions and criticality, considering every action type, while maintaining inter-group fairness above 99% according to established fairness metrics. This result represents a significant advancement over existing systems in the state of the art literature that typically achieve 90–95 % accuracy but show performance disparities across different gait patterns. The architecture is composed of lightweight modules enabling deployment on edge devices without large resource requirements, while ensuring fairness regardless of patients' walking style.

In real-world scenarios, numerous phenomena generate a series of events that occur in continuous time. Point processes provide a natural mathematical framework for modeling these event sequences. In this comprehensive survey, we aim to explore probabilistic models that capture the dynamics of event sequences through temporal processes. We revise the notion of event modeling and provide the mathematical foundations that underpin the existing literature on this topic. To structure our survey effectively, we introduce an ontology that categorizes the existing approaches considering three horizontal axes: modeling , inference and estimation , and application . We conduct a systematic review of the existing approaches, with a particular focus on those leveraging deep learning techniques. Finally, we delve into the practical applications where these proposed techniques can be harnessed to address real-world problems related to event modeling. Additionally, we provide a selection of benchmark datasets that can be employed to validate the approaches for point processes.

Recommendation systems play a crucial role in modern e-commerce and streaming services. However, the limited availability of public datasets hampers the rapid development of more efficient and accurate recommendation algorithms within the research community. This work introduces a stream-based data generator designed to generate user preferences for a set of items while accommodating progressive changes in user preferences. The underlying principle involves using user/item embeddings to derive preferences by exploring the proximity of these embeddings. Whether randomly generated or learned from a real finite data stream, these embeddings serve as the basis for generating new preferences. We investigate how this fundamental model can adapt to shifts in user behavior over time; in our framework, changes correspond to alterations in the structure of the tripartite graph, reflecting modifications in the underlying embeddings. Through an analysis of real-life data streams, we demonstrate that the proposed model is effective in capturing actual preferences and the changes that they can exhibit over time. Thus, we characterize these changes and develop a generalized method capable of simulating realistic data, thereby generating streams with similar yet controllable drift dynamics.

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.

Popularity bias is the tendency of recommender systems to further suggest popular items while disregarding niche ones, hence giving no chance for items with low popularity to emerge. Although the literature is rich in debiasing techniques, it still lacks quality measures that effectively enable their analyses and comparisons. In this article, we first introduce a formal, data-driven, and parameter-free strategy for classifying items into low, medium, and high popularity categories. Then we introduce Balanced Quality Score (BQS) , a quality measure that rewards the debiasing techniques that successfully push a recommender system to suggest niche items, without losing points in its predictive capability in terms of global accuracy. We conduct tests of BQS on three distinct baseline collaborative filtering frameworks: one based on history-embedding and two on user/item-embedding modeling. These evaluations are performed on multiple benchmark datasets and against various state-of-the-art competitors, demonstrating the effectiveness of BQS.

Lung cancer is one of the most common cancers worldwide. In 2020, there were an estimated 2.2 million new cases of lung cancer. It is often diagnosed at an advanced stage because in the early stages, it does not present particularly abnormal symptoms, such as cough and chest pain, which are often underestimated. Typically, it is detected through pronounced symptoms that lead to more in-depth diagnostic examinations or through other routine tests that reveal its presence almost by chance. The tools used for detection include chest X-rays, CT scans, and PET scans. Analyzing these allows the detection of abnormal formations in the early stages. From this perspective, artificial intelligence has set the goal, in recent years, of providing tools for image analysis capable of automatically, rigorously, and above all, promptly providing an initial classification of the detected formations. In particular, in the literature, it is possible to find various solutions that make use of Federated Learning. This method involves training deep learning models on expansive datasets distributed across multiple data centers. Significantly, it safeguards privacy by obviating the need to transmit sensitive patient data. The objective of this paper is to examine existing state-of-the-art solutions, emphasizing workflows and essential strategies. It considers the datasets employed and architectural decisions. The analysis extends to common issues encountered in the studied works, with a specific focus on challenges typical of the medical domain. The paper concludes by exploring potential future solutions to address these issues.

Brain and other nervous system cancer ranks as the tenth most common cause of death. According to estimates, primary cancerous brain and central nervous system tumors will be the cause of 18.990 deaths in the United States in 2023, more precisely 11.020 men and 7.970 women. Primary cancerous brain and central nervous system tumors are estimated to have killed 251.329 people globally in 2020. Given the variety and potential danger, it becomes evident that it is necessary to promptly detect any abnormal formations within the head in order to proceed with timely therapy. In this regard, artificial intelligence has proven to be useful for constructing systems capable of detecting, from medical images, the presence of cancerous formations in a non-invasive and timely manner. Questions regarding how data are handled always spark discussions on ethical grounds and on privacy protection. Federated Learning is a specific protocol that manages data locally within the involved medical institutions, aiming to address privacy-related issues. The objective of the paper is to assess the existing state-of-the-art solutions by emphasizing their workflows and key components. This analysis takes into account the datasets employed and the architectural decisions made. Finally we scrutinize open challenges and potential future development to address these specific issues.

Breast cancer stands as the leading cause of mortality among women worldwide, encompassing all types of cancer. It can affect women of all age groups post-puberty in any country, with its incidence generally rising as women age. Early detection of breast cancer, prior to its spreading, enables more effective treatment and markedly improves survival rates. Employing techniques like mammography, ultrasound, and magnetic resonance imaging (MRI) for breast cancer screening can greatly improve a patient’s prognosis and overall outlook.In the realm of medical imaging, there is a growing interest in the utilization of Federated Learning (FL). This approach entails training deep learning models using expansive datasets dispersed across various data centers. Crucially, it upholds privacy by eliminating the requirement to transmit sensitive patient data. The paper aims to analyze current solutions in the state-of-the-art, highlighting workflows and key solutions, taking into consideration the datasets used and architectural choices. Finally, common problems encountered in the works and those typical of the medical domain are analyzed, focusing on possible future solutions to solve them.

This paper introduces a novel deep learning approach for predicting global solar radiation and temperature. We propose an architecture based on Gated Recurrent Unit (GRU) neural networks able to refine weather predictions returned by the MM5 Regional Climate Model. Measured values from a weather station and outputs from the MM5 system are used to train and validate the model. The forecasting capability is assessed for three-day estimations. The results demonstrate that the model, by correcting MM5’s periodic tendencies to underestimate or overestimate the outputs, leads to a more accurate forecasting of the weather variables.These more precise predictions are then adopted for calculating the electrical energy production of a photovoltaic cell. Also in this case, the proposed model allows better results than the traditional MM5 system, enabling adaptive adjustments in intelligent energy systems.

Abstract In real-world applications, audio surveillance is often performed by large models that can detect many types of anomalies. However, typical approaches are based on centralized solutions characterized by significant issues related to privacy and data transport costs. In addition, the large size of these models prevented a shift to contexts with limited resources, such as edge devices computing. In this work we propose conv-SPAD , a method for convolutional SPectral audio-based Anomaly Detection that takes advantage of common tools for spectral analysis and a simple autoencoder to learn the underlying condition of normality of real scenarios. Using audio data collected from real scenarios and artificially corrupted with anomalous sound events, we test the ability of the proposed model to learn normal conditions and detect anomalous events . It shows performances in line with larger models, often outperforming them. Moreover, the model’s small size makes it usable in contexts with limited resources, such as edge devices hardware.

In the recent decade, the amount of digital information recorded in electronic health records (EHRs) has increased dramatically. EHRs are no longer used to store basic patient information and administrative activities, but they can now store a wide range of data, from the patient's medical history to images. The issue currently is not so much gathering data as it is analyzing it, that is, converting data into knowledge, conclusions, and actions. The use of innovative technology instruments, such as artificial intelligence (AI) algorithms, to support medical inter-disciplinary collaboration among different teams, geographically distributed in the network, extracting useful information from EHRs, and integrating data from various data sources is a crucial, but still difficult task. SIMPATICO 3D (Sistema Informativo Medico PATologIe COmplesse) is a system that assists scientists and clinicians by offering tools for managing, organizing, analyzing, and disseminating medical data. The project SIMPATICO 3D originates from a collaboration between the software house eway Enterprise Business Solutions, the DIMES Department of the University of Calabria and the DMSC Department of the University Magna Graecia of Catanzaro and it has been selected for funding under the FESR 2014/2020.

Abstract This paper is a contribution to semantic data management in P2P systems. It is based on the previous works of the same authors in which a declarative semantics for P2P systems is defined: under this semantics, only facts not making the local databases inconsistent are imported (Weak Models) and the Maximal Weak Models are those in which peers import maximal sets of facts not violating integrity constraints. The proposal, presented in this paper, stems from the following two observations: (i) the Maximal Weak Model Semantics ensures that locally consistent P2P systems always admit a maximal weak model, but fails for locally inconsistent P2P systems; (ii) the self-esteem degree of a peer should impact on the interaction with other peers in the system so that driving the integration process. From the above-mentioned observations a more general framework is presented. Our proposal is able to manage local inconsistencies and allows to model a peer integration process driven by the self-esteem degree of each peer. Different self-esteem degrees could be considered; in this paper we focus our attention on three different scenarios in which a generic peer can declare an high, low or medium self-esteem degree, stating respectively that it trusts its own knowledge more, less or equally with respect to the knowledge that can be provided from the rest of the system. Three different basic semantics are proposed, High, Low, Medium Self-Esteem Semantics and results about the computational complexity of P2P logic queries are investigated by considering brave and cautions reasoning. The paper also presents an extension of the basic framework of the Self-Esteem Semantics that models a finer-grained self-esteem concept and allows each peer to exhibit different levels of self-esteem each of them defined with respect to a subset of its mapping rules.

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. A new coronavirus, SARS-CoV-2, was identified as responsible for the lung infection, called COVID-19 (coronavirus disease 2019). 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 a very important role. Achieving this goal cannot be separated from radiological examination, and chest X-ray is the most easily available and least expensive alternative. The use of X-ray chest radiographs, as an element that assists the diagnosis and that allows the follow up of the disease, is the subject of many publications that adopt machine learning approaches. This work focuses on the most adopted Convolutional Neural Network Techniques applied on chest X-ray images.

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.

Abstract This paper presents a logic framework for modeling the interaction among deductive databases in a peer-to-peer (P2P) environment. Each peer joining a P2P systemprovides or imports datafrom its neighbors by using a set ofmapping rules, that is, 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. A query can be posed to any peer in the system and the answer is computed by using locally stored data and all the information that can be consistently imported from the neighborhood. Two different types of mapping rules are defined: mapping rules allowing to import a maximal set of atoms not leading to inconsistency (calledmaximal mapping rules) and mapping rules allowing to import a minimal set of atoms needed to restore consistency (calledminimal mapping rules). Implicitly, the use of maximal mapping rules statesit is preferable to import as long as no inconsistencies arise; whereas the use of minimal mapping rules states thatit is preferable not to import unless a inconsistency exists. The paper presents three different declarative semantics of a P2P system: (i) theMax Weak Model Semantics, in which mapping rules are used to importas much knowledge as possiblefrom a peer’s neighborhood without violating local integrity constraints; (ii) theMin Weak Model Semantics, in which the P2P system can be locally inconsistent and the information provided by the neighbors is used to restore consistency, that is, to only integrate the missing portion of a correct, but incomplete database; (iii) theMax-Min Weak Model Semanticsthat unifies the previous two different perspectives captured by the Max Weak Model Semantics and Min Weak Model Semantics. This last semantics allows to characterize each peer in the neighborhood as a resource used either to enrich (integrate) or to fix (repair) the knowledge, so as to define a kind ofintegrate–repairstrategy for each peer. For each semantics, the paper also introduces an equivalent and alternative characterization, obtained by rewriting each mapping rule into prioritized rules so as to model a P2P system as a prioritized logic program. Finally, results about the computational complexity of P2P logic queries are investigated by consideringbraveandcautiousreasoning.

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.

Computer based support for clinical and health-related procedures is growing in the last decades. However, the vast majority of information systems adopted in health structures are legacy systems, which do not often allow to export data easily and also are usually desktop oriented. 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 eIMES 3D Mobile (standing for Evolution Imaging System 3D for Mobile), a system which is based on the eIMES 3D system and supports clinicians for images studies with dedicated functions for the mobile environment. The tool has been developed within a project called ReCaTuR for RAre Cancer Network (i.e., Network of Rare Cancer), aiming to define a network for the management, organization and distribution of medical information. Moreover, it has been implemented following the specifications by the oncology department of an Italian Hospital. eIMES 3D allows to start a medical interdisciplinary collaboration among different teams, geographically distributed in the network, so that obtaining the integration of skills, expertize, knowledge and experiences with the final aim of clinical case resolution. eIMES 3D provides an hardware infrastructure that allows to connect multiple devices, as well as to create workstations (WorkSpaces) that independently and asynchronously can request information to the central database containing the 3D imaging data. Il also allows to share information among a network of mobile devices. The ability to build plug-in modules enables to easily implement new features in eIMES 3D Mobile, thus ensuring its further development and its sustainability.

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.

Abstract We study the framework of abductive logic programming extended with integrity constraints. For this framework, we introduce a new measure of the simplicity of an explanation based on its degree of arbitrariness: the more arbitrary the explanation, the less appealing it is, with explanations having no arbitrariness — they are called constrained — being the preferred ones. In the paper, we study basic properties of constrained explanations. For the case when programs in abductive theories are stratified we establish results providing a detailed picture of the complexity of the problem to decide whether constrained explanations exist.

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.

Abstract We study active integrity constraints and revision programming, two formalisms designed to describe integrity constraints on databases and to specify policies on preferred ways to enforce them. Unlike other more commonly accepted approaches, these two formalisms attempt to provide a declarative solution to the problem. However, the original semantics of founded repairs for active integrity constraints and justified revisions for revision programs differ. Our main goal is to establish a comprehensive framework of semantics for active integrity constraints, to find a parallel framework for revision programs, and to relate the two. By doing so, we demonstrate that the two formalisms proposed independently of each other and based on different intuitions when viewed within a broader semantic framework turn out to be notational variants of each other. That lends support to the adequacy of the semantics we develop for each of the formalisms as the foundation for a declarative approach to the problem of database update and repair. In the paper, we also study computational properties of the semantics we consider and establish results concerned with the concept of the minimality of change and the invariance under the shifting transformation.

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