Artificial Intelligence - Special issue on knowledge representation
On periodicity in temporal databases
Information Systems
Handling infinite temporal data
PODS '90 Proceedings of the ninth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Time Granularities in Databases, Data Mining and Temporal Reasoning
Time Granularities in Databases, Data Mining and Temporal Reasoning
Temporal Reasoning in Workflow Systems
Distributed and Parallel Databases
Symbolic representation of user-defined time granularities
Annals of Mathematics and Artificial Intelligence
An Algebraic Representation of Calendars
Annals of Mathematics and Artificial Intelligence
Solving multi-granularity temporal constraint networks
Artificial Intelligence
Symbolic User-Defined Periodicity in Temporal Relational Databases
IEEE Transactions on Knowledge and Data Engineering
Implementing Calendars and Temporal Rules in Next Generation Databases
Proceedings of the Tenth International Conference on Data Engineering
Calendars, Time Granularities, and Automata
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Discovering Calendar-Based Temporal Association Rules
TIME '01 Proceedings of the Eighth International Symposium on Temporal Representation and Reasoning (TIME'01)
Mapping Calendar Expressions into Periodical Granularities
TIME '04 Proceedings of the 11th International Symposium on Temporal Representation and Reasoning
An Efficient Algorithm for Minimizing Time Granularity Periodical Representations
TIME '05 Proceedings of the 12th International Symposium on Temporal Representation and Reasoning
Representing and Reasoning about Temporal Granularities
Journal of Logic and Computation
Integrating multiple calendars using τ Z AMAN
Software—Practice & Experience
A system prototype for solving multi-granularity temporal CSP
CSCLP'04 Proceedings of the 2004 joint ERCIM/CoLOGNET international conference on Recent Advances in Constraints
A modular approach to user-defined symbolic periodicities
Data & Knowledge Engineering
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In the recent years several research efforts have focused on the concept of time granularity and its applications. A first stream of research investigated the mathematical models behind the notion of granularity and the algorithms to manage temporal data based on those models. A second stream of research investigated symbolic formalisms providing a set of algebraic operators to define granularities in a compact and compositional way. However, only very limited manipulation algorithms have been proposed to operate directly on the algebraic representation making it unsuitable to use the symbolic formalisms in applications that need manipulation of granularities. This paper aims at filling the gap between the results from these two streams of research, by providing an efficient conversion from the algebraic representation to the equivalent low-level representation based on the mathematical models. In addition, the conversion returns a minimal representation in terms of period length. Our results have a major practical impact: users can more easily define arbitrary granularities in terms of algebraic operators, and then access granularity reasoning and other services operating efficiently on the equivalent, minimal low-level representation. As an example, we illustrate the application to temporal constraint reasoning with multiple granularities. From a technical point of view, we propose an hybrid algorithm that interleaves the conversion of calendar subexpressions into periodical sets with the minimization of the period length. The algorithm returns set-based granularity representations having minimal period length, which is the most relevant parameter for the performance of the considered reasoning services. Extensive experimental work supports the techniques used in the algorithm, and shows the efficiency and effectiveness of the algorithm.