A Computational Data Model of Intelligent Agents with Time-Varying Resources

Abstract Background The advancement of science and technologies play an immense role in the way scientific experiments are being conducted. Understanding how experiments are performed and how results are derived has become significantly more complex with the recent explosive growth of heterogeneous research data and methods. Therefore, it is important that the provenance of results is tracked, described, and managed throughout the research lifecycle starting from the beginning of an experiment to its end to ensure reproducibility of results described in publications. However, there is a lack of interoperable representation of end-to-end provenance of scientific experiments that interlinks data, processing steps, and results from an experiment’s computational and non-computational processes. Results We present the “REPRODUCE-ME” data model and ontology to describe the end-to-end provenance of scientific experiments by extending existing standards in the semantic web. The ontology brings together different aspects of the provenance of scientific studies by interlinking non-computational data and steps with computational data and steps to achieve understandability and reproducibility. We explain the important classes and properties of the ontology and how they are mapped to existing ontologies like PROV-O and P-Plan. The ontology is evaluated by answering competency questions over the knowledge base of scientific experiments consisting of computational and non-computational data and steps. Conclusion We have designed and developed an interoperable way to represent the complete path of a scientific experiment consisting of computational and non-computational steps. We have applied and evaluated our approach to a set of scientific experiments in different subject domains like computational science, biological imaging, and microscopy.

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Temporal Semistructured Data Models and Data Warehouses

Data Warehouses and OLAP ◽

10.4018/987-1-59904-364-7.ch012 ◽

2011 ◽

pp. 277-297 ◽

Cited By ~ 2

Author(s):

Carlo Combi ◽

Barbara Oliboni

Keyword(s):

Data Warehouse ◽

Data Model ◽

Heterogeneous Data ◽

Data Models ◽

Semistructured Data ◽

Data Sources ◽

Time Varying ◽

Data Warehouses ◽

Time Dimension ◽

Heterogeneous Data Sources

This chapter describes a graph-based approach to represent information stored in a data warehouse, by means of a temporal semistructured data model. We consider issues related to the representation of semistructured data warehouses, and discuss the set of constraints needed to manage in a correct way the warehouse time, i.e. the time dimension considered storing data in the data warehouse itself. We use a temporal semistructured data model because a data warehouse can contain data coming from different and heterogeneous data sources. This means that data stored in a data warehouse are semistructured in nature, i.e. in different documents the same information can be represented in different ways, and moreover, the document schemata can be available or not. Moreover, information stored into a data warehouse is often time varying, thus as for semistructured data, also in the data warehouse context, it could be useful to consider time.

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