Now showing 1 - 10 of 12
- PublicationAttribute refinement in a multigranular temporal object data modelTemporal granularities are the unit of measure for temporal data, thus a multigranular temporal object model allows to store temporal data at different levels of detail, according to the needs of the application domain. In this paper we investigate how the integration of multiple temporal granularities in an object-oriented data model impacts on the inheritance hierarchy. In the paper we specifically address issues related to attribute refinement, and the consequences on object substitutability. This entails the development of suitable instruments for converting temporal values from a granularity to another.
- PublicationSpatio-temporal multi-granularity : modelling and implementation challengesMultiple spatial and temporal granularities are essential to extract significant knowledge from datasets at different levels of detail: they enable to zoom-in and zoom-out a dataset, enhancing the data modelling flexibility and being instrumental to boost the analysis of information. Implementing granularities poses several interesting problems. Specifically, in this paper we analyse the issues involved by enhancing a data model and a query language with spatio-temporal multi-granularity, and we figure out efficacious solutions to address all of them. In our exposition, we investigate proper representations for the spatial and the temporal domains; then we conceive an appropriate design for granules and granularities, and for multi-granular values. In particular, mutual relationships among granularities and how they affect granularities design is discussed according to their influence on data access and considering the application of multi-granular conversions. Afterwards, we dedicated to the design of multi-granular spatio-temporal conversions, discussing the multiple ways in which they affect data usability and envisaging how the design of a multi-granular model and query language may guarantee such an fundamental property, reducing uncertainty on the represented values, combining concepts like topologically consistent transformation, probability distributions, invertibility and quasi-invertibility properties. In our discussion, we are influenced on our previous work on multi-granularity. Especially, we expose some of the considerations that guided the design of a multi-granular spatio-temporal model we already proposed as extension of the ODMG data model. In this paper, we describe also some relevant details of a demonstrative object-oriented prototype realized on top of ObjectStore PSE Proj, and of an object-relational prototype realized on top of ORACLE. Both prototypes, even exploiting different characteristics of the underlying data models, prove the effectiveness of the proposed design solutions.
- PublicationAdaptive management of multigranular spatio-temporal object attributesIn applications involving spatio-temporal modelling, granularities of data may have to adapt according to the evolving semantics and significance of data. To address such a problem, in this paper we define ST2 ODMGe, a multigranular spatio-temporal model supporting evolutions, which encompass the dynamic adaptation of attribute granularities, and the deletion of attribute values. Evolutions are specified as Event - Condition - Action rules and are performed at run-time. The event, the condition and the action may refer to a period of time and a geographical area. Periodic evolutions may be specified, referring to both transaction and valid time dimensions. The evolution may also be constrained by the attribute values. Evolutions greatly enhance flexibility in multigranular spatio-temporal data handling but require revisiting the notion of object consistency with respect to class definitions and access to multigranular object values.
- PublicationContexts as explicit parameterization of ontology driven methodsIn the recent Computer Science literature, contexts have been proposed mainly to formalize context dependent background knowledge. In this paper we discuss the importance of the application of contexts as explicit parametrization of methods exploiting the knowledge encoded in ontologies. We propose a context formalization suitable to improve the flexibility of ontology driven methods like semantic similarity and granularity. Herein, we detail in particular the context parametrization for semantic granularity. The user scenario pertaining to the exploitation of a repository for industrial design product models is discussed to illustrate the proposed formalization.
- PublicationMulti-granular spatio-temporal object models : concepts and research directionsThe capability of representing spatio-temporal objects is fundamental when analysing and monitoring the changes in the spatial configuration of a geographical area over a period of time. An important requirement when managing spatio-temporal objects is the support for multiple granularities. In this paper we discuss how the modelling constructs of object data models can be extended for representing and querying multi-granular spatio-temporal objects. In particular, we describe object-oriented formalizations for granularities, granules, and multi-granular values, exploring the issues of value conversions. Furthermore, we formally define an object-oriented multi-granular query language, and discuss the dynamic adapting of multi-granular data. Finally, we illustrate the current open research issues of multi-granular spatio-temporal data handling.
950Scopus© Citations 10
- PublicationContext enabled semantic granularityIn this paper we propose a powerful ontology driven method that eases the browsing of any repository of information resources described by an ontology: we provide a flexible semantic granularity method for the navigation of a repository according to different levels of abstraction, i.e. granularities. The granularity is explicitly parameterised according to the criteria induced by the context.
331Scopus© Citations 3
- PublicationMultigranular spatio-temporal models : implementation challengesMultiple granularities provide an essential support for extracting significant knowledge from spatio-temporal datasets at different levels of details. They enable to zoom-in and zoom-out spatio-temporal datasets, thus enhancing the data modelling flexibility and improving the analysis of information. In this paper we investigate the implementation issues arising when a data model and a query language are enriched with spatio-temporal multigranularity. We introduce appropriate representations for space and time dimensions, granularities, granules, and multi-granular values. Finally, we discuss how multigranular spatio-temporal conversions affect data usability and how such important property may be guaranteed.
297Scopus© Citations 5
- PublicationAdaptive management of multigranular spatio-temporal object attributesIn applications involving spatio-temporal modelling, granularities of data may have to adapt according to the evolving semantics and significance of data. In this paper we define ST2 ODMGe, a multigranular spatio-temporal model supporting evolutions, which encompass the dynamic adaptation of attribute granularities, and the deletion of attribute values. Evolutions are specified as Event - Condition - Action rules and are executed at run-time. The event, the condition, and the action may refer to a period of time and a geographical area. The evolution may also be constrained by the attribute values. The ability of dynamically evolving the object attributes results in a more flexible management of multigranular spatio-temporal data but it requires revisiting the notion of object consistency with respect to class definitions and access to multigranular object values. Both issues are formally investigated in the paper.
449Scopus© Citations 3
- PublicationMining Spatio-temporal Data at Different Levels of DetailIn this paper we propose a methodology for mining very large spatio-temporal datasets. We propose a two-pass strategy for mining and manipulating spatio-temporal datasets at different levels of detail (i.e., granularities). The approach takes advantage of the multi-granular capability of the underlying spatio-temporal model to reduce the amount of data that can be accessed initially. The approach is implemented and applied to real-world spatio-temporal datasets. We show that the technique can deal easily with very large datasets without losing the accuracy of the extracted patterns, as demonstrated in the experimental results.
1032Scopus© Citations 6
- PublicationQuerying Multigranular Spatio-temporal ObjectsThe integrated management of both spatial and temporal components of information is crucial in order to extract significant knowledge from datasets concerning phenomena of interest to a large variety of applications. Moreover, multigranularity, i.e., the capability of representing information at different levels of detail, enhances the data modelling flexibility and improves the analysis of information, enabling to zoom-in and zoom-out spatio-temporal datasets. Relying on an existing multigranular spatio-temporal extension of the ODMG data model, in this paper we describe the design of a multigranular spatio-temporal query language. We extend OQL value comparison and object navigation in order to access spatio-temporal objects with attribute values defined at different levels of detail.
458Scopus© Citations 7