Fusion Data Mapper

Overview – Fusion Data Mapper

This document provides guidance and operating procedures for creating and managing mapped datasets using Fusion Registry 10 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.

Related Pages

For further guidance on Fusion Data Mapper:

Add a Mapped Dataset – Fusion Data Mapper

Add Series to a Dataset – Fusion Data Mapper

Browse Privileges – Fusion Data Mapper

Bulk Maintenance of Metadata Values using Excel Import / Export – Fusion Data Mapper

Changing the Dimensionality of a Dataset – Fusion Data Mapper

Clone a Dataset – Fusion Data Mapper

Codelists - Adding and Removing Codes – Fusion Data Mapper

Codelists – Adding and Changing Multilingual Code Names with Impact Analysis - Fusion Data Mapper

Content Security Caveats – Fusion Data Mapper

Content Security Metadata Management Use Cases – Fusion Data Mapper

Default Code Values – Fusion Data Mapper

Maintaining Metadata Values on Series Interactively using the Web Interface – Fusion Data Mapper

Maintenance Privileges – Fusion Data Mapper

Registering a Series – Fusion Data Mapper

Remove a Mapped Dataset – Fusion Data Mapper

Removing Series from a Dataset – Fusion Data Mapper

The Fusion Data Mapper User Interface