Abstract
Ontologies are usually applied to drive intelligent applications and also as a resource for integrating or extracting information, as in the case of Natural Language Processing (NLP) tasks. Further, ontologies as the Gene Ontology (GO) are used as an artifact for very specific research aims. However, the value of ontologies for data analysis tasks may also go beyond these uses and span supporting the reuse and composition of data acquisition, integration and fusion code. This requires that both data and code artifacts support meta-descriptions using shared conceptualizations. In this paper, we discuss the different concerns in semantically describing data pipelines as a key reusable artifact that could be retrieved, compared and reused with a degree of automation if semantically consistent descriptions are provided. Concretely, we propose attaching semantic descriptions for data and analytic transformations to current backend-independent distributed processing frameworks as Apache Beam, as these already abstract out the specificity of supporting execution engines.
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Notes
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- 6.
Turnover in industry - consumer durables, available at https://data.europa.eu/euodp/es/data/dataset/Z3YE842s0KutFv7stmNmDw/resource/51c01810-f40a-48d6-b178-c7701a01f821.
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“Index of turnover-Total”, http://dd.eionet.europa.eu/vocabulary/eurostat/indic_bt/TOVT.
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At the time of this writing, Bean is mainly used as a data transformation framework not including distributed machine learning algorithms, for example, TensorFlow Extended (TFX) [4] is built on top of Beam but there are not algorithms available as aggregate transformations.
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The transformations are just examples, they are not intended as analytics with real practical value.
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This would require integrating a parser of that language for checking and manipulation, that could include exploiting semantic mappings.
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Sicilia, MÁ., García-Barriocanal, E., Sánchez-Alonso, S., Mora-Cantallops, M., Cuadrado, JJ. (2019). Ontologies for Data Science: On Its Application to Data Pipelines. In: Garoufallou, E., Sartori, F., Siatri, R., Zervas, M. (eds) Metadata and Semantic Research. MTSR 2018. Communications in Computer and Information Science, vol 846. Springer, Cham. https://doi.org/10.1007/978-3-030-14401-2_16
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