Domain Ontology for Time Series Provenance

@inproceedings{Souza2014DomainOF,
  title={Domain Ontology for Time Series Provenance},
  author={Luc{\'e}lia de Souza and Maria Salete Marcon Gomes Vaz and Marcos Sfair Suny{\'e}},
  booktitle={ICEIS},
  year={2014}
}
Time series data are generated all the time with a volume without precedent, constituting themselves of a points sequence spread out over time, usually at time regular intervals. Time series analysis is different from data analysis, given its intrinsic nature, where observations are dependent and the observations order is important for analysis. The knowledge about the data which will be analyzed is relevant in an analysis process, but this knowledge is not always explicit and easy to interpret… 

Figures from this paper

Semantically Enriching the Detrending Step of Time Series Analysis

TLDR
The Detrend Ontology (DO prefix) is presented, designed in a modular way, by reuse of ontological resources, which are extended for modeling of statistical methods applied for detrending in the time domain, and its extensibility for methods in time-frequency domain is described.

References

SHOWING 1-10 OF 24 REFERENCES

Modular Development of Ontologies for Provenance in Detrending Time Series

TLDR
This paper is presenting the modular development of ontologies combined with Open Provenance Model - OPM, which is extended to facilitate the understanding about as detrending processes were executed in time series data, enriching semantically the preprocessing phase of time series analysis.

A showcase of semantic time series processing

TLDR
A new semantic time series processing language is developed and several tools to enrich the time series with meta‐data and for community building have been implemented in Python and Java.

An Ontological Representation of Time Series Observations on the Semantic Sensor Web

TLDR
An ontological representation of time series observations could provide a more expressive model and resolve problems of semantic-level interoperability of sensor networks on the Semantic Sensor Web, as well as a real-world usecase from sensor networks currently measuring rainfall in the South Esk river catchment in the North East of Tasmania, Australia.

A New Perspective on Semantics of Data Provenance

TLDR
This work examines provenance from a semantics perspective and presents the W7 model, an ontological model of data provenance, which is general and extensible enough to capture provenance semantics for data in different domains.

The Open Provenance Model core specification (v1.1)

Provenance in Databases: Past, Current, and Future

  • W. Tan
  • Computer Science
    IEEE Data Eng. Bull.
  • 2007
TLDR
An overview of research in provenance in databases is provided and some future research directions are discussed, based on the tutorial presented at SIGMOD 2007.

The Open Provenance Model (v1.01)

TLDR
The Open Provenance Model is introduced, a model for provenance that is designed to meet the following requirements: to allow provenance information to be exchanged between systems, by means of a compatibility layer based on a shared provenance model.

Ontology Engineering in a Networked World

TLDR
This book by Surez-Figueroa et al. provides the necessary methodological and technological support for the development and use of ontology networks, which ontology developers need in this distributed environment.

Ontologies and languages for representing mathematical knowledge on the Semantic Web

  • C. Lange
  • Computer Science
    Semantic Web
  • 2013
TLDR
It is shown that MathML and OpenMath, the standard XML-based exchange languages for mathematical knowledge, can be fully integrated with RDF representations in order to contribute existing mathematical knowledge to the Web of Data.

Modularity in ontologies

TLDR
The 4 articles in this special issue present thoroughly investigated approaches that contribute to modularity in ontologies on quite distinct, but equally important layers, which the authors will sketch below in more detail.