ABSTRACT In the real world, many phenomena are time related and in the last three decades the dat... more ABSTRACT In the real world, many phenomena are time related and in the last three decades the database community has devoted much work in dealing with “time of facts” in databases. While many approaches incorporating time in the relational model have been already devised, most of them assume that the exact time of facts is known. However, this assumption does not hold in many practical domains, in which temporal indeterminacy of facts occurs. The treatment of valid-time indeterminacy requires in-depth extensions to the current relational approaches. In this paper, we propose a theoretically grounded approach to cope with this issue, overcoming the limitations of related approaches in the literature. In particular, we present a 1NF temporal relational model and propose a new temporal relational algebra to query it. We also formally study the properties of the new data model and algebra, thus granting that our approach is interoperable with pre-existent temporal and non-temporal relational approaches, and is implementable on top of them. Finally, we consider computational complexity, showing that only a limited overhead is added when moving from determinate to indeterminate time.
ABSTRACT Now-related temporal data play an important role in the medical context. Current relatio... more ABSTRACT Now-related temporal data play an important role in the medical context. Current relational temporal database (TDB) approaches are limited since (i) they (implicitly) assume that the span of time occurring between the time when facts change in the world and the time when the changes are recorded in the database is exactly known, and (ii) do not explicitly provide an extended relational algebra to query now-related data. We propose an approach that, widely adopting AI symbolic manipulation techniques, overcomes the above limitations.
The user has requested enhancement of the downloaded file. All in-text references underlined in b... more The user has requested enhancement of the downloaded file. All in-text references underlined in blue are added to the original document and are linked to publications on ResearchGate, letting you access and read them immediately.
Clinical practice guidelines are widely used to support physicians, but only on individual pathol... more Clinical practice guidelines are widely used to support physicians, but only on individual pathologies. The treatment of patients affected by multiple diseases (comorbid patients) requires the development of new approaches, supporting physicians in the detection of interactions between guidelines. We propose a new methodology, supporting flexible and physician-driven search and detection. In particular, we provide a flexible and interactive mechanism to navigate guidelines and our ontology of interactions (between drugs, or between actions’ goals) at multiple levels of detail, focusing on specific parts of it (e.g., on a specific pair of actions, or of drugs) to look for interactions. We introduce the notion of “navigation tree”, as the basic data structure to support multiple-level interaction analysis, and describe navigation and focusing algorithms operating on it. We also introduce a visualization tool that is based on the “navigation tree”, and further enhances the user-friendliness of our approach.
ABSTRACT In the real world, many phenomena are time related and in the last three decades the dat... more ABSTRACT In the real world, many phenomena are time related and in the last three decades the database community has devoted much work in dealing with “time of facts” in databases. While many approaches incorporating time in the relational model have been already devised, most of them assume that the exact time of facts is known. However, this assumption does not hold in many practical domains, in which temporal indeterminacy of facts occurs. The treatment of valid-time indeterminacy requires in-depth extensions to the current relational approaches. In this paper, we propose a theoretically grounded approach to cope with this issue, overcoming the limitations of related approaches in the literature. In particular, we present a 1NF temporal relational model and propose a new temporal relational algebra to query it. We also formally study the properties of the new data model and algebra, thus granting that our approach is interoperable with pre-existent temporal and non-temporal relational approaches, and is implementable on top of them. Finally, we consider computational complexity, showing that only a limited overhead is added when moving from determinate to indeterminate time.
ABSTRACT Now-related temporal data play an important role in the medical context. Current relatio... more ABSTRACT Now-related temporal data play an important role in the medical context. Current relational temporal database (TDB) approaches are limited since (i) they (implicitly) assume that the span of time occurring between the time when facts change in the world and the time when the changes are recorded in the database is exactly known, and (ii) do not explicitly provide an extended relational algebra to query now-related data. We propose an approach that, widely adopting AI symbolic manipulation techniques, overcomes the above limitations.
The user has requested enhancement of the downloaded file. All in-text references underlined in b... more The user has requested enhancement of the downloaded file. All in-text references underlined in blue are added to the original document and are linked to publications on ResearchGate, letting you access and read them immediately.
Clinical practice guidelines are widely used to support physicians, but only on individual pathol... more Clinical practice guidelines are widely used to support physicians, but only on individual pathologies. The treatment of patients affected by multiple diseases (comorbid patients) requires the development of new approaches, supporting physicians in the detection of interactions between guidelines. We propose a new methodology, supporting flexible and physician-driven search and detection. In particular, we provide a flexible and interactive mechanism to navigate guidelines and our ontology of interactions (between drugs, or between actions’ goals) at multiple levels of detail, focusing on specific parts of it (e.g., on a specific pair of actions, or of drugs) to look for interactions. We introduce the notion of “navigation tree”, as the basic data structure to support multiple-level interaction analysis, and describe navigation and focusing algorithms operating on it. We also introduce a visualization tool that is based on the “navigation tree”, and further enhances the user-friendliness of our approach.
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Papers by Luca Piovesan