Our approach offers several advantages. During the guideline acquisition phase, it enables to represent temporal constraints, and to check their consistency. In the execution phase, it checks the consistency between the execution times of the actions and the constraints in the guidelines, and provides query answering and simulation facilities.

Now-relative temporal data play an important role in most temporal applications, and their management has been proved to impact in a crucial way the efficiency of temporal databases. Though several temporal relational approaches have been developed to deal with now-relative data, none of them has provided a whole temporal algebra to query them. In this paper we overcome such a limitation, by proposing a general algebra which is polymorphically adapted to cope with the MAX "reference" approach, and with our POINT approach (and that is easily adaptable to cope with other relational approaches to now-relative data in the literature, such as the NULL and the MIN ones). Besides being general enough to provide a query language for several approaches in the literature, our algebra has been designed in such a way to satisfy several theoretical and practical desiderata: closure with respect to representation languages, correctness with respect to the "consensus" BCDM semantics, reducibility to the standard non-temporal algebra (which involves interoperability with non-temporal relational databases), implementability and efficiency. Indeed, the experimental evaluation we have drawn on our implementation has shown that only a slight overhead is added by our treatment of now-relative data (with respect to an approach in which such data are not present).

We propose an innovative approach to the detection and analysis of interactions between CPGs considering different sources of temporal information (CPGs, ontological knowledge and execution logs), which is the first one in the literature that takes into account the temporal issues, and accounts for different application scenarios.

Abstract-Valid-time indeterminacy is "don't know when" indeterminacy, coping with cases in which one does not exactly know when a fact holds in the modeled reality. In this paper, we first propose a reference representation (data model and algebra) in which all possible temporal scenarios induced by valid-time indeterminacy can be extensionally modeled. We then specify a family of sixteen more compact representational data models. We demonstrate their correctness with respect to the reference representation and analyze several properties, including their data expressiveness. Then, we compare these compact models along several relevant dimensions. Finally, we also extend the reference representation and a representative of compact representations to cope with probabilities.

Abstract. In this paper, we present GLARE, a domain-independent system for acquiring, representing and executing clinical guidelines. GLARE is characterized by the adoption of Artificial Intelligence (AI) techniques at different levels in the definition and implementation of the system. First of all, a high-level and user-friendly knowledge representation language has been designed, providing a set of representation primitives. Second, a user-friendly acquisition tool has been designed and implemented, on the basis of the knowledge representation formalism. The acquisition tool provides various forms of help for the expert physicians, including different levels of syntactic and semantic tests in order to check the "well-formedness" of the guidelines being acquired. In particular, extended AI temporal reasoning techniques are used to check the consistency of temporal constraints. Third, a tool for executing guidelines on a specific patient has been made available. The tool relies on an "agenda" technique, which provides great flexibility, including the possibility of managing repeated and/or concurrent actions. The execution module also provides hypothetical reasoning facilities, to support physicians in the comparison of alternative diagnostic and/or therapeutic strategies. The GLARE approach has been successfully tested on clinical guidelines in different domains such as bladder cancer, reflux esophagitis, heart failure and stroke.

Today, there is considerable interest in personal healthcare. The pervasiveness of technology allows to precisely track human behavior; however, when dealing with the development of an intelligent assistant exploiting data acquired through such technologies, a critical issue has to be taken into account; namely, that of supporting the user in the event of any transgression with respect to the optimal behavior. In this paper we present a reasoning framework based on Simple Temporal Problems that can be applied to a general class of problems, which we called cake&carrot problems, to support reasoning in presence of human transgression. The reasoning framework offers a number of facilities to ensure a smart management of possible "wrong behaviors" by a user to reach the goals defined by the problem. This paper describes the framework by means of the prototypical use case of diet domain. Indeed, following a healthy diet can be a difficult task for both practical and psychological reasons and dietary transgressions are hard to avoid. Therefore, the framework is tolerant to dietary transgressions and adapts the following meals to facilitate users in recovering from such transgressions. Finally, through a simulation involving a real hospital menu, we show that the framework can effectively achieve good results in a realistic scenario.

Abstract-The cooperative construction of data/knowledge bases has recently had a significant impulse (see, e.g., Wikipedia [1]). In cases in which data/knowledge quality and reliability are crucial, proposals of update/insertion/deletion need to be evaluated by experts. To the best of our knowledge, no theoretical framework has been devised to model the semantics of update proposal/evaluation in the relational context. Since time is an intrinsic part of most domains (as well as of the proposal/evaluation process itself), semantic approaches to temporal relational databases (specifically, Bitemporal Conceptual Data Model (henceforth, BCDM) [2]) are the starting point of our approach. In this paper, we propose BCDM PV , a semantic temporal relational model that extends BCDM to deal with multiple update/insertion/deletion proposals and with acceptances/rejections of proposals themselves. We propose a theoretical framework, defining the new data structures, manipulation operations and temporal relational algebra and proving some basic properties, namely that BCDM PV is a consistent extension of BCDM and that it is reducible to BCDM. These properties ensure consistency with most relational temporal database frameworks, facilitating implementations.

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