Abstract: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 POI… Show more
“…More recently, the POINT approach has been proposed [18], which outperforms the MAX, MIN and NULL approaches. Even more recently, a relational algebra has been defined to fully support querying NOW-related data (i.e., without resorting to the instantiation to the current time) in all MAX, MIN, NULL and POINT approaches [10]. All such approaches are (implicitly) based on Clifford et al's semantics, assuming latency equal to zero.…”
Section: Resultsmentioning
confidence: 99%
“…At RT=11, the semantics of Example 4 is <John, ICU | { (10,10), (10,11), (11,10), (11,11)}>, and at RT=12 it becomes <John, ICU | { (10,10), (10,11), (10,12), (11,10), (11,11), (11,12), (12,10), (12,11), (12,12)}>.…”
Section: Background: Clifford Et Al's Semantics Of 'Now'mentioning
confidence: 99%
“…For instance, in case NOW-1 is used instead of NOW, the semantics of Example 4 at RT=12 is: <John, ICU | { (10,10), (10,11), (11,10), (11,11), (12,10), (12,11) …”
Section: Background: Clifford Et Al's Semantics Of 'Now'mentioning
confidence: 99%
“…We are not aware of any other algebra explicitly coping with now-related facts (except the POINT approach [10][18], which, however, does not explicitly provide any semantics for NOW). Thus, we have chosen to compare the performance of our approach with an "ideal" (but not realistic) approach in which the exact ending time of nowrelative data is known a priori.…”
Section: Experimental Evaluationmentioning
confidence: 99%
“…Such representational approaches adopt indexing techniques to enhance efficiency, and are experimentally evaluated and compared [8] [9]. An algebra for such approaches have been recently proposed by Anselma et al [10]. However, their data and query semantics has not been explicitly explored yet.…”
Abstract-Now-related temporal data play an important role in many applications. Clifford et al.'s approach is a milestone to model the semantics of 'now' in temporal relational databases. Several relational representation models for now-related data have been presented; however, the semantics of such representations has not been explicitly studied. Additionally, the definition of a relational algebra to query now-related data is an open problem. We propose the first integrated approach that provides both a neat semantics for now-related data and a compact 1NF representation (data model and relational algebra) for them. Additionally, our approach also extends current approaches to consider (i) domains where it is not always possible to know when changes in the world are recorded in the database and (ii) now-related data with a bound on their persistency in the future. To do so, we explicitly model the notion of temporal indeterminacy in the future for now-related data. The properties of our approach are also analyzed both from a theoretical (semantic correctness and reducibility of the algebra) and from the experimental point of view. Experiments show that, despite our approach is a major extension to current temporal relational approaches, no significant overhead is added to deal with 'now'.
“…More recently, the POINT approach has been proposed [18], which outperforms the MAX, MIN and NULL approaches. Even more recently, a relational algebra has been defined to fully support querying NOW-related data (i.e., without resorting to the instantiation to the current time) in all MAX, MIN, NULL and POINT approaches [10]. All such approaches are (implicitly) based on Clifford et al's semantics, assuming latency equal to zero.…”
Section: Resultsmentioning
confidence: 99%
“…At RT=11, the semantics of Example 4 is <John, ICU | { (10,10), (10,11), (11,10), (11,11)}>, and at RT=12 it becomes <John, ICU | { (10,10), (10,11), (10,12), (11,10), (11,11), (11,12), (12,10), (12,11), (12,12)}>.…”
Section: Background: Clifford Et Al's Semantics Of 'Now'mentioning
confidence: 99%
“…For instance, in case NOW-1 is used instead of NOW, the semantics of Example 4 at RT=12 is: <John, ICU | { (10,10), (10,11), (11,10), (11,11), (12,10), (12,11) …”
Section: Background: Clifford Et Al's Semantics Of 'Now'mentioning
confidence: 99%
“…We are not aware of any other algebra explicitly coping with now-related facts (except the POINT approach [10][18], which, however, does not explicitly provide any semantics for NOW). Thus, we have chosen to compare the performance of our approach with an "ideal" (but not realistic) approach in which the exact ending time of nowrelative data is known a priori.…”
Section: Experimental Evaluationmentioning
confidence: 99%
“…Such representational approaches adopt indexing techniques to enhance efficiency, and are experimentally evaluated and compared [8] [9]. An algebra for such approaches have been recently proposed by Anselma et al [10]. However, their data and query semantics has not been explicitly explored yet.…”
Abstract-Now-related temporal data play an important role in many applications. Clifford et al.'s approach is a milestone to model the semantics of 'now' in temporal relational databases. Several relational representation models for now-related data have been presented; however, the semantics of such representations has not been explicitly studied. Additionally, the definition of a relational algebra to query now-related data is an open problem. We propose the first integrated approach that provides both a neat semantics for now-related data and a compact 1NF representation (data model and relational algebra) for them. Additionally, our approach also extends current approaches to consider (i) domains where it is not always possible to know when changes in the world are recorded in the database and (ii) now-related data with a bound on their persistency in the future. To do so, we explicitly model the notion of temporal indeterminacy in the future for now-related data. The properties of our approach are also analyzed both from a theoretical (semantic correctness and reducibility of the algebra) and from the experimental point of view. Experiments show that, despite our approach is a major extension to current temporal relational approaches, no significant overhead is added to deal with 'now'.
Temporal representation and temporal reasoning is a central in Artificial Intelligence. The literature is moving to the treatment of "non-crisp" temporal constraints, in which also preferences or probabilities are considered. However, most approaches only support numeric preferences, while, in many domain applications, users naturally operate on "layered" scales of values (e.g., Low, Medium, High), which are domain-and task-dependent. For many tasks, including decision support, the evaluation of the minimal network of the constraints (i.e., the tightest constraints) is of primary importance. We propose the first approach in the literature coping with layered preferences on quantitative temporal constraints. We extend the widely used simple temporal problem (STP) framework to consider layered user-defined preferences, proposing (i) a formal representation of quantitative constraints with layered preferences, and (ii) a temporal reasoning algorithm, based on the general algorithm Compute-Summaries, for the propagation of such temporal constraints. We also prove that our temporal reasoning algorithm evaluates the minimal network.
The treatment of patients affected by multiple diseases (comorbid patients) is one of the main challenges of the modern healthcare, involving the analysis of the interactions of the guidelines for the specific diseases. However, practically speaking, such interactions occur over time. The GLARE project explicitly provides knowledge representation, temporal representation and temporal reasoning methodologies to cope with such a fundamental issue. In this paper, we propose a further improvement, to take into account that, often, the effects of actions have a probabilistic distribution in time, and being able to reason (through constraint propagation) and to query probabilistic temporal constraints further enhances the support for interaction detection.
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