Recursive representation of periodicity and temporal reasoning

Anselma, Luca

doi:10.1109/time.2004.1314419

Cited by 8 publications

(2 citation statements)

References 21 publications

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“…Kasperovics et al [1] introduced multislices as a basic object for representing recurrences, defined the difference operation on time slices and multislices, and proposed a scalable representation of multislices in relational databases. There is a number of works improving the expressiveness of time slices (e.g., [10,11,12,2]), or incorporating them into more complex representation formalisms (e.g., [13,7,14]). In this paper we presented an operation that constructs multislice representation for a given recurrence, which was not addressed by the previous works.…”

Section: Related Workmentioning

confidence: 99%

On the Efficient Construction of Multislices from Recurrences

Kasperovičs

Böhlen

Gamper

2010

Lecture Notes in Computer Science

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Recurrences are defined as sets of time instants associated with events and they are present in many application domains, including public transport schedules and personal calendars. Because of their large size, recurrences are rarely stored explicitly, but some form of compact representation is used. Multislices are a compact representation that is well suited for storage in relational databases. A multislice is a set of time slices where each slice employs a hierarchy of time granularities to compactly represent multiple recurrences. In this paper we investigate the construction of multislices from recurrences. We define the compression ratio of a multislice, show that different construction strategies produce multislices with different compression ratios, and prove that the construction of minimal multislices, i.e., multislices with a maximal compression ratio, is an NP-hard problem. We propose a scalable algorithm, termed LMerge, for the construction of multislices from recurrences. Experiments with real-world recurrences from public transport schedules confirm the scalability and usefulness of LMerge: the generated multislices are very close to minimal multislices, achieving an average compression ratio of approx. 99%. A comparison with a baseline algorithm that iteratively merges pairs of mergeable slices shows significant improvements of LMerge over the baseline approach. Abstract. Recurrences are defined as sets of time instants associated with events and they are present in many application domains, including public transport schedules and personal calendars. Because of their large size, recurrences are rarely stored explicitly, but some form of compact representation is used. Multislices are a compact representation that is well suited for storage in relational databases. A multislice is a set of time slices where each slice employs a hierarchy of time granularities to compactly represent multiple recurrences. On the Efficient Construction of Multislices from RecurrencesIn this paper we investigate the construction of multislices from recurrences. We define the compression ratio of a multislice, show that different construction strategies produce multislices with different compression ratios, and prove that the construction of minimal multislices, i.e., multislices with a maximal compression ratio, is an NP-hard problem. We propose a scalable algorithm, termed LMerge, for the construction of multislices from recurrences. Experiments with real-world recurrences from public transport schedules confirm the scalability and usefulness of LMerge: the generated multislices are very close to minimal multislices, achieving an average compression ratio of approx. 99%. A comparison with a baseline algorithm that iteratively merges pairs of mergeable slices shows significant improvements of LMerge over the baseline approach.

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Section: Related Workmentioning

confidence: 99%

On the Efficient Construction of Multislices from Recurrences

Kasperovičs

Böhlen

Gamper

2010

Lecture Notes in Computer Science

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“…In [1] the author introduces a model for the representation of periodic events. By defining temporal relations between events it is possible to represent the order of events and periodic repetitions of them.…”

Section: Introductionmentioning

confidence: 99%

Representation of periodic moving objects in databases

Behr

Almeida

Güting

2006

Proceedings of the 14th Annual ACM International Symposium on Advances in Geographic Information Systems

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In the real world, lots of objects with changing position can be found. Some of them repeat the same movement several times, called periodic movements. Examples include airplanes, trains, planets, and marine turtles. This paper describes a model for representing the periodic movements to be stored in a database system, exploiting the information about the repetitions. The model is generic enough to represent any kind of movement, not being restricted to objects with repetitions in their movement. We present algorithms to detect the repetitions and to convert to the periodic representation as well as the implementation of some operations on such representation. We show, in an experimental evaluation against the so-called flat representation, that the approach presented in this paper significantly improves the performance of query processing in a database system when dealing with objects with some periodic movement. We also show that, for the worst case where the objects do not follow any periodic movement at all, our approach still performs acceptably.

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