Fusion Data Mapper

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Italic textBold text=Overview= This document provides guidance and operating procedures for creating and managing mapped datasets using Fusion Registry 9 and the Fusion Data Mapper.

Use Case

The primary use case is transforming single dimensional datasets to SDMX multi-dimensional structures.

Single dimensional datasets are those with a single unique identifier for each series (e.g. Series Code) such as created by FAME or similar time-series production systems.

One-to-one transformations only are supported by this version of the Fusion Data Mapper.

The transformation is performed by Fusion Registry using SDMX Structure Mapping. Fusion Data Mapper provides an easy-to-use user interface for defining and management the mapping rules.

Audience
  • Metadata Managers – those responsible for managing the metadata mappings on the Bank’s catalogue of time series on a day to day basis.
  • Metadata Superusers – those responsible for managing the core structural metadata including Agencies, Concepts, Data Structure Definitions and Codelists.
  • System Administrators – those responsible for administering Fusion Registry 9 as part of the integrated statistical data and metadata system, and managing the Time Series Database as the source of observation data.
Prerequisites

Readers are assumed to have an understanding of basic SDMX principles and the purpose of the main SDMX structural metadata artefacts including Concepts, Codes and Codelists, Categories, Data Structure Definitions (DSDs), Dataflows, Provision Agreements, Structure Sets and Dataflow Maps.

Terminology

Dataset Dataset

refers to a named collection of series that typically all fall under a specific topic, for instance ‘National Accounts’. In Fusion Registry, an SDMX Dataflow represents a dataset. Mapped Dataset A Mapped Dataset is an SDMX Dataflow where data is taken from a ‘source’ Dataflow and transformed to different dimensionality using defined mapping rules. The Fusion Data Mapper manages these mapping rules. In this document, the source Dataflow is assumed to be observation data from the Time Series Database which is described by a Data Structure Definition having only SERIES_CODE, TIME_PERIOD and OBS_VALUE dimensions. Time Series Database The source of time series observation data without metadata that Fusion Registry maps to Mapped Datasets using the defined mapping rules.

The Fusion Data Mapper User Interface

The Fusion Data Mapper is a web user interface providing the following main functions:

Authenticated users with sufficient structural metadata maintenance privileges

  • Add and remove mapped datasets
  • Add and remove series on mapped datasets
  • Interactively set and change the metadata values on a series by series basis
  • Export metadata values for selected series to Excel
  • Import metadata values for defined series from Excel
  • Change code names with impact analysis

Anonymous or authenticated users with sufficient privileges to view but not change the structural metadata

  • Browse the catalogue of mapped datasets
  • Examine the ‘definition’ of a dataset – its dimensionality and list of possible codes for each
  • Dimension or Attribute
  • Browse the series in each dataset

The Fusion Registry Administration Interface

The Administration Interface is Fusion Registry’s main web user interface.

For the purposes of managing the metadata on mapped datasets, it provides the following functions:

Authenticated users with sufficient structural metadata management privileges

  • Create and modify SDMX Data Structure Definitions (DSDs)
  • Create and modify SDMX Concepts
  • Create and modify SDMX Codelists
  • Add and remove codes from SDMX Codelists
  • Register a series (series must be ‘registered’ before they can be mapped in dataset by adding the Series Code and Series Name to the relevant SERIES_CODE Codelist)

Refer to the Fusion Registry Structural Metadata Management Guide for general information on using the Fusion Registry Administration Interface for creating and maintaining core SDMX structure metadata artefacts including DSDs, Dataflows, Concepts, Categories and Codelists.