Modernizing data management at the US Bureau of Labor Statistics

Authors

  • Dan Gillman US Bureau of Labor Statistics
  • Clayton Waring

DOI:

https://doi.org/10.29173/iq1038

Keywords:

multi-dimensional data, time-series, metadata, measures

Abstract

The US Bureau of Labor Statistics (BLS) is undertaking initiatives to improve its data and metadata systems. Planning for the replacement of the public facing LABSTAT data query system and efforts within the Office of Productivity and Technology to combine multiple production systems within a single cross-divisional database platform are examples. BLS views time-series data as a combination of three elemental components found in every time-series. A measure element; a person, places, and things element; and a time element are the components. The authors turned this basic approach into a formal conceptual model represented in UML (Unified Modeling Language). The UML model describes a flexible multi-dimensional data structure, of which time-series are a kind, and supports any kind of query into the data. The Office of Productivity and Technology has adopted the model, and it is guiding their approach moving forward. The model was also adopted by the Financial Industry Business Ontology project under the Object Management Group and by the Data Documentation Initiative Cross-Domain Integration (DDI-CDI) development project. There are other similarities between the OPT effort and DDI-CDI as well. In this way, the OPT project demonstrates the feasibility and usefulness of many of the ideas in DDI-CDI. In this paper we describe the time-series formulation and the UML conceptual model. Then, the design of the OPT system and some of its features are described, relating those that are like DDI-CDI where appropriate. In doing so, we provide a thorough understanding of the structure of time-series.

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Published

2023-03-30

How to Cite

Gillman, D., & Waring, C. (2023). Modernizing data management at the US Bureau of Labor Statistics. IASSIST Quarterly, 47(1). https://doi.org/10.29173/iq1038