The RDF Data Cube vocabulary

Last update:
Richard Cyganiak (DERI, NUI Galway)
Dave Reynolds (Epimorphics Ltd)
Jeni Tennison (TSO)


There are many situations where it would be useful to be able to publish multi-dimensional data, such as statistics, on the web in such a way that it can be linked to related data sets and concepts. The Data Cube vocabulary provides a means to do this using the W3C RDF (Resource Description Framework) standard. The model underpinning the Data Cube vocabulary is compatible with the cube model that underlies SDMX (Statistical Data and Metadata eXchange), an ISO standard for exchanging and sharing statistical data and metadata among organizations. The Data Cube vocabulary is a core foundation which supports extension vocabularies to enable publication of other aspects of statistical data flows.

Status of this document

This is an editor's draft without any formal standing. It is not endorsed by any organisation. Anything in this document is still subject to change at this point. The editors seek feedback on the document. Please send any comments to the project's Google Group.

Table of Contents

1. Introduction

1.1 A Data Cube vocabulary

Statistical data is a foundation for policy prediction, planning and adjustments and underpins many of the mash-ups and visualisations we see on the web. There is strong interest in being able to publish statistical data in a web-friendly format to enable it to be linked and combined with related information.

At the heart of a statistical dataset is a set of observed values organized along a group of dimensions, together with associated metadata. The Data Cube vocabulary enables such information to be represented using the the W3C RDF (Resource Description Framework) standard and published following the principles of linked data. The vocabulary is based upon the approach used by the SDMX ISO standard for statistical data exchange. This cube model is very general and so the Data Cube vocabulary can be used for other data sets such as survey data, spreadsheets and OLAP data cubes [OLAP].

The Data Cube vocabulary is focused purely on the publication of multi-dimensional data on the web. We envisage a series of modular vocabularies being developed which extend this core foundation. In particular, we see the need for an SDMX extension vocabulary to support the publication of additional context to statistical data (such as the encompassing Data Flows and associated Provision Agreements). Other extensions are possible to support metadata for surveys (so called "micro-data", as encompassed by DDI) or publication of statistical reference metadata.

The Data Cube in turn builds upon the following existing RDF vocabularies:

1.2 RDF and Linked Data

Linked data is an approach to publishing data on the web, enabling datasets to be linked together through references to common concepts. The approach [LOD] recommends use of HTTP URIs to name the entities and concepts so that consumers of the data can look-up those URIs to get more information, including links to other related URIs. RDF [RDF] provides a standard for the representation of the information that describes those entities and concepts, and is returned by dereferencing the URIs.

There are a number of benefits to being able to publish multi-dimensional data, such as statistics, using RDF and the linked data approach:

1.3 SDMX and related standards

The Statistical Data and Metadata Exchange (SDMX) Initiative was organised in 2001 by seven international organisations (BIS, ECB, Eurostat, IMF, OECD, World Bank and the UN) to realise greater efficiencies in statistical practice. These organisations all collect significant amounts of data, mostly from the national level, to support policy. They also disseminate data at the supra-national and international levels.

There have been a number of important results from this work: two versions of a set of technical specifications - ISO:TS 17369 (SDMX) - and the release of several recommendations for structuring and harmonising cross-domain statistics, the SDMX Content-Oriented Guidelines. All of the products are available at The standards are now being widely adopted around the world for the collection, exchange, processing, and dissemination of aggregate statistics by official statistical organisations. The UN Statistical Commission recommended SDMX as the preferred standard for statistics in 2007.

The SDMX specification defines a core information model which is reflected in concrete form in two syntaxes - SDMX-ML (an XML syntax) and SDMX-EDI. The Data Cube vocabulary builds upon the core of the SDMX information model.

A key component of the SDMX standards package are the Content-Oriented Guidelines (COGs), a set of cross-domain concepts, code lists, and categories that support interoperability and comparability between datasets by providing a shared terminology between SDMX implementers. RDF versions of these terms are available separately for use along with the Data Cube vocabulary.

1.4 Relationship to SCOVO

The Statistical Core Vocabulary (SCOVO) [SCOVO] is a lightweight RDF vocabulary for expressing statistical data. Its relative simplicity allows easy adoption by data producers and consumers, and it can be combined with other RDF vocabularies for greater effect. The model is extensible both on the schema and the instance level for more specialized use cases.

While SCOVO addresses the basic use case of expressing statistical data in RDF, its minimalist design is limiting, and it does not support important scenarios that occur in statistical publishing, such as:

The design of the Data Cube vocabulary is informed by SCOVO, and every SCOVO dataset can be re-expressed within the vocabulary.

1.5 Audience and scope

This document describes the Data Cube vocabulary It is aimed at people wishing to publish statistical or other multi-dimension data in RDF. Mechanics of cross-format translation from other formats such as SDMX-ML will be covered elsewhere.

1.6 Document conventions

The names of RDF entities -- classes, predicates, individuals -- are URIs. These are usually expressed using a compact notation where the name is written prefix:localname, and where the prefix identifies a namespace URI. The namesapce identified by the prefix is prepended to the localname to obtain the full URI.

In this document we shall use the conventional prefix names for the well-known namespaces:

We also introduce the prefix qb for the Data Cube namespace

2. Data cubes

2.1 The cube model - dimensions, attributes, measures

A statistical data set comprises a collection of observations made at some points across some logical space. The collection can be characterized by a set of dimensions that define what the observation applies to (e.g. time, area, population) along with metadata describing what has been measured (e.g. economic activity), how it was measured and how the observations are expressed (e.g. units, multipliers, status). We can think of the statistical data set as multi-dimensional space, or hyper-cube, indexed by those dimensions. This space is commonly referred to as a cube for short; though the name shouldn't be taken literally, it is not meant to imply that there are exactly three dimensions (there can be more or fewer) nor that all the dimensions are somehow similar in size.

A cube is organized according to a set of dimensions, attributes and measures. We collectively call these components.

The dimension components serve to identify the observations. A set of values for all the dimension components is sufficient to identify a single observation. Examples of dimensions include the time to which the observation applies, or a geographic region which the observation covers.

The measure components represent the phenomenon being observed.

The attribute components allow us to qualify and interpret the observed value(s). They enable specification of the units of measures, any scaling factors and metadata such as the status of the observation (e.g. estimated, provisional).

2.2 Slices

It is frequently useful to group subsets of observations within a dataset. In particular to fix all but one (or a small subset) of the dimensions and be able to refer to all observations with those dimension values as a single entity. We call such a selection a slice through the cube. For example, given a data set on regional performance indicators then we might group all the observations about a given indicator and a given region into a slice, each slice would then represent a time series of observed values.

A data publisher may identify slices through the data for various purposes. They can be a useful grouping to which metadata might be attached, for example to note a change in measurement process which affects a particular time or region. Slices also enable the publisher to identify and label particular subsets of the data which should be presented to the user - they can enable the consuming application to more easily construct the appropriate graph or chart for presentation.

In statistical applications it is common to work with slices in which a single dimension is left unspecified. In particular, to refer to such slices in which the single free dimension is time as Time Series and to refer slices along non-time dimensions as Sections. Within the Data Cube vocabulary we allow arbitrary dimensionality slices and do not give different names to particular types of slice but extension vocabularies, such as SDMX-RDF, can easily add such concept labels.

3. An example

In order to illustrate the use of the data cube vocabulary we will use a small demonstration data set extracted from StatsWales report number 003311 which describes life expectancy broken down by region (unitary authority), age and time. The extract we will use is:


Merthyr Tydfil

We can see that there are three dimensions - time period (averages over three year timespans?), region, sex. Each observation represents the life expectancy for that population (the measure) and we will need an attribute to define the units (years) of the measured values.

An example of slicing the data would be to define slices in which the time and sex are fixed for each slice. Such slices then show the variation in life expectancy across the different regions, i.e. corresponding to the columns in the above tabular layout.

4. Outline of the vocabulary

Vocabulary index

Classes: qb:Attachable qb:AttributeProperty qb:CodedProperty qb:ComponentProperty qb:ComponentSet qb:ComponentSpecification qb:DataSet qb:DataStructureDefinition qb:DimensionProperty qb:MeasureProperty qb:Observation qb:Slice qb:SliceKey

Properties: qb:attribute qb:codeList qb:component qb:componentAttachment qb:componentProperty qb:componentRequired qb:concept qb:dataSet qb:dimension qb:measure qb:measureDimension qb:measureType qb:observation qb:order qb:slice qb:sliceKey qb:sliceStructure qb:structure qb:subSlice

5. Creating data structure definitions

A qb:DataStructureDefinition defines the structure of one or more datasets. In particular, it defines the dimensions, attributes and measures used in the dataset along with qualifying information such as ordering of dimensions and whether attributes are required or optional. For well-formed data sets much of this information is implicit within the RDF component properties found on the observations. However, the explicit declaration of the structure has several benefits:

It is common, when publishing statistical data, to have a regular series of publications which all follow the same structure. The notion of a Data Structure Definition (DSD) allows us to define that structure once and then reuse it for each publication in the series. Consumers can then be confident that the structure of the data has not changed.

5.1 Dimensions, attributes and measures

The Data Cube vocabulary represents the dimensions, attributes and measures as RDF properties. Each is an instance of the abstract qb:ComponentProperty class, which in turn has sub-classes qb:DimensionProperty, qb:AttributeProperty and qb:MeasureProperty.

A component property encapsulates several pieces of information:

The same concept can be manifested in different components. For example, the concept of currency may be used as a dimension (in a data set dealing with exchange rates) or as an attribute (when describing the currency in which an observed trade took place). The concept of time is typically used only as a dimension but may be encoded as a data value (e.g. an xsd:dateTime) or as a symbolic value (e.g. a URI drawn from the reference time URI set developed by In statistical agencies it is common to have a standard thesaurus of statistical concepts which underpin the components used in multiple different data sets.

To support this reuse of general statistical concepts the data cube vocabulary provides the qb:concept property which links a qb:ComponentProperty to the concept it represents. We use the SKOS vocabulary [SKOS] to represent such concepts. This is very natural for those cases where the concepts are already maintained as a controlled term list or thesaurus. When developing a data structure definition for an informal data set there may not be an appropriate concept already. In those cases, if the concept is likely to be reused in other guises it is recommended to publish a skos:Concept along with the specific qb:ComponentProperty. However, if such reuse is not expected then it is not required to do so - the qb:concept link is optional and a simple instance of the appropriate subclass of qb:ComponentProperty is sufficient.

The representation of the possible values of the component is described using the rdfs:range property of the component in the usual RDF manner. Thus, for example, values of a time dimension might be represented using literals of type xsd:dateTime or as URIs drawn from a time reference service.

In statistical data sets it is common for values to be encoded using some (possibly hierarchical) code list and it can be useful to be able to easily identify the overall code list in some more structured form. To cater for this a component can also be optionally annotated with a qb:codeList denoting a skos:ConceptScheme. In such a case the rdfs:range of the component might be left as simply skos:Concept but a useful design pattern is to also define an rdfs:Class whose members are all the skos:Concepts within a particular scheme. In that way the rdfs:range can be made more specific which enables generic RDF tools to perform appropriate range checking.

Note that in the SDMX extension vocabulary there is one further item of information to encode about components - the role that they play within the structure definition. In particular, is sometimes convenient for consumers to be able to easily identify which is the time dimension, which component is the primary measure and so forth. It turns out that such roles are intrinsic to the concepts and so this information is encoded by providing subclasses of skos:Concept for each role. The particular choice of roles here is specific to the SDMX standard and so is not included within the core data cube vocabulary. In cases where such roles are appropriate then we encourage applications of the data cube vocabulary to also supply the relevant SDMX-derived role information.

Before illustrating the components needed for our running example, there is one more piece of machinery to introduce, a reusable set of concepts and components based on SDMX.

5.2 Content oriented guidelines

The SDMX standard includes a set of content oriented guidelines (COG) [COG] which define a set of common statistical concepts and associated code lists that are intended to be reusable across data sets. As part of the data cube work we have created RDF analogues to the COG. These include:

The data cube vocabulary is standalone and it is not mandatory to use the SDMX COG-derived terms. However, when the concepts being expressed do match a COG concept it is recommended that publishers should reuse the corresponding components and/or concept URIs to simplify comparisons across data sets. Given this background we will reuse the relevant COG components in our worked example.

5.3 Example

Turning to our example data set then we can see there are three dimensions to represent - time period, region (unitary authority) and sex of the population. There is a single (primary) measure which corresponds to the topic of the data set (life expectancy) and encodes a value in years. Hence, we need the following components.

Time. There is a suitable predefined concept in the SMDX-COG for this, REF_PERIOD, so we could reuse the corresponding component property sdmx-dimension:refPeriod. However, to represent the time period itself it would be convenient to use the reference time service and to declare this within the data structure definition.

  eg:refPeriod  a rdf:Property, qb:DimensionProperty;
      rdfs:label "reference period"@en;
      rdfs:subPropertyOf sdmx-dimension:refPeriod;
      rdfs:range interval:Interval;
      qb:concept sdmx-concept:refPeriod . 

Region. Again there is a suitable COG concept and associated component that we can use for this, and again we can customize the range of the component. In this case we can use the Ordanance Survey administrative geography ontology [OS-GEO].

  eg:refArea  a rdf:Property, qb:DimensionProperty;
      rdfs:label "reference area"@en;
      rdfs:subPropertyOf sdmx-dimension:refArea;
      rdfs:range admingeo:UnitaryAuthority;
      qb:concept sdmx-concept:refArea . 

Sex. In this case we can use the corresponding COG component sdmx-dimension:sex directly, since the default code list for it includes the terms we need.

Measure. This property will give the value of each observation. We could use the default smdx-measure:obsValue for this (defining the topic being observed using metadata). However, it can aid readability and processing of the RDF data sets to use a specific measure corresponding to the phenomenon being observed.

  eg:lifeExpectancy  a rdf:Property, qb:MeasureProperty;
      rdfs:label "life expectancy"@en;
      rdfs:subPropertyOf sdmx-measure:obsValue;
      rdfs:range xsd:decimal . 

Unit measure attribute. The primary measure on its own is a plain decimal value. To correctly interpret this value we need to define what units it is measured in (years in this case). This is defined using attributes which qualify the interpretation of the observed value. Specifically in this example we can use the predefined sdmx-attribute:unitMeasure which in turn corresponds to the COG concept of UNIT_MEASURE. To express the value of this attribute we would typically us a common thesaurus of units of measure. For the sake of this simple example we will use the DBpedia resource which corresponds to the topic of the Wikipedia page on "Years".

This covers the minimal components needed to define the structure of this data set.

5.4 ComponentSpecifications and DataStructureDefinitions

To combine the components into a specification for the structure of this datasets we need to declare a qb:DataStuctureDefinition resource which in turn will reference a set of qb:ComponentSpecification resources. The qb:DataStuctureDefinition will be reusable across other data sets with the same structure.

In the simplest case the qb:ComponentSpecification simply references the corresponding qb:ComponentProperty (ususally using one of the sub properties qb:dimension, qb:measure or qb:attribute. However, it is also possible to qualify the component specification in several ways.

In the case of our running example the dimensions can be usefully ordered. There is only one attribute, the unit measure, and this is required. In the interests of illustrating the vocabulary use we will declare that this attribute will be attached at the level of the data set, however flattened representations are in general easier to query and combine.

So the structure of our example data set (and other similar datasets) can be declared by:

  eg:dsd-le a qb:DataStructureDefinition;
      # The dimensions
      qb:component [qb:dimension eg:refArea;         qb:order 1];
      qb:component [qb:dimension eg:refPeriod;       qb:order 2];
      qb:component [qb:dimension sdmx-dimension:sex; qb:order 3];
      # The measure(s)
      qb:component [qb:measure eg:lifeExpectancy];
      # The attributes
      qb:component [qb:attribute sdmx-attribute:unitMeasure; qb:componentAttachment qb:DataSet;] .

Note that we have given the data structure definition (DSD) a URI since it will be reused across different datasets with the same structure. Similarly the component properties themselves can be reused across different DSDs. However, the component specifications are only useful within the scope of a particular DSD and so we have chosen the represent them using blank nodes.

5.5 Handling multiple measures

Our example data set is relatively simple in having a single observable (in this case "life expectancy") that is being measured. In other data sets there can be multiple measures. These measures may be of similar nature (e.g. a data set on local government performance might provide multiple different performance indicators for each region) or quite different (e.g. a data set on trades might provide quantity, value, weight for each trade).

There are two approaches to representing multiple measures. In the SDMX information model then each observation can record a single observed value. In a data set with multiple observations then we add an additional dimension whose value indicates the measure. This is appropriate for applications where the measures are separate aggregate statistics. In other domains such as a clinical statistics or sensor networks then the term observation usually denotes an observation event which can include multiple observed values. Similarly in Business Intelligence applications and OLAP then a single "cell" in the data cube will typically represent multiple facts about a single transaction.

The data cube vocabulary permits either representation approach to be used though they cannot be mixed within the same data set.

Multi-measure observations

This approach allows multiple observed values to be attached to an individual observation. Is suited to representation of things like sensor data and OLAP cubes. To use this representation you simply declare multiple qb:MeasureProperty components in the data structure definition and attach an instance of each property to the observations within the data set.

For example, if we have a set of shipment data containing unit count and total weight for each shipment then we might have a data structure definition such as:

eg:dsd1 a qb:DataStructureDefinition;
    rdfs:comment "shipments by time (multiple measures approach)"@en;
        [ qb:dimension  sdmx-dimension:refTime; ],
        [ qb:measure    eg-measure:quantity; ],
        [ qb:measure    eg-measure:weight; ] . 

This would correspond to individual observations such as:

eg:dataset1 a qb:DataSet;
    qb:structure eg:dsd1 .
eg:obs1a  a qb:Observation;
    qb:dataSet eg:dataset1;
    sdmx-dimension:refTime "30-07-2010"^^xsd:date;
    eg-measure:weight 1.3 ;
    eg-measure:quantity 42 ;

Note that one limitation of the multi-measure approach is that it is not possible to attach an attribute to a single observed value. An attribute attached to the observation instance will apply to the whole observation (e.g. to indicate who made the observation). Attributes can also be attached directly to the qb:MeasureProperty itself (e.g. to indicate the unit of measure for that measure) but that attachment applies to the whole data set (indeed any data set using that measure property) and cannot vary for different observations. For applications where this limitation is a problem then use the measure dimension approach.

Measure dimension

This approach restricts observations to having a single measured value but allows a data set to carry multiple measures by adding an extra dimension, a measure dimension. The value of the measure dimension denotes which particular measure is being conveyed by the observation. This is the representation approach used within SDMX and the SMDX-in-RDF extension vocabulary introduces a subclass of qb:DataStructureDefinition which is restricted to using the measure dimension representation.

To use this representation you declare an additional dimension within the data structure definition to play the role of the measure dimension. For use within the Data Cube vocabulary we provide a single distinguished component for this purpose -- qb:measureType. Within the SDMX-in-RDF extension then there is a role used to identify concepts which act as measure types, enabling other measure dimensions to be declared. In the special case of using qb:measureType as the measure dimension, the set of allowed measures is assumed to be those measures declared within the DSD. There is no need to define a separate code list or enumerated class to duplicate this information. Thus, qb:measureType is a “magic” dimension property with an implicit code list.

The data structure definition for our above example, using this representation approach, would then be:

eg:dsd2 a qb:DataStructureDefinition;
    rdfs:comment "shipments by time (measure dimension approach)"@en;
        [ qb:dimension  sdmx-dimension:refTime; ],
        [ qb:measure    eg-measure:quantity; ],
        [ qb:measure    eg-measure:weight; ],
        [ qb:dimension  qb:measureType; ] . 

This would correspond to individual observations such as:

eg:dataset2 a qb:DataSet;
    qb:structure eg:dsd2 .
eg:obs2a  a qb:Observation;
    qb:dataSet eg:dataset2;
    sdmx-dimension:refTime "30-07-2010"^^xsd:date;
    qb:measureType eg-measure:weight ;
    eg-measure:weight 1.3 .
eg:obs2b  a qb:Observation;
    qb:dataSet eg:dataset2;
    sdmx-dimension:refTime "30-07-2010"^^xsd:date;
    qb:measureType eg-measure:quantity ;
    eg-measure:quantity 42 . 

Note the duplication of having the measure property show up both as the property that carries the measured value, and as the value of the measure dimension. We accept this duplication as necessary to ensure the uniform cube/dimension mechanism and a uniform way of declaring and using measure properties on all kinds of datasets.

Those familiar with SDMX should also note that in the RDF representation there is no need for a separate "primary measure" which subsumes each of the individual measures, those individual measures are used directly. The SDMX-in-RDF extension vocabulary addresses the round-tripping of the SDMX primary measure by use of a separate annotation on sdmx:DataStructureDefinition.

6. Expressing data sets

A DataSet is a collection of statistical data that corresponds to a given data structure definition. The data in a data set can be roughly described as belonging to one of the following kinds:

This is the actual data, the measured numbers. In a statistical table, the observations would be the numbers in the table cells.
Organizational structure
To locate an observation within the hypercube, one has at least to know the value of each dimension at which the observation is located, so these values must be specified for each observation. Datasets can have additional organizational structure in the form of slices as described earlier in section 2.2.
Internal metadata
Having located an observation, we need certain metadata in order to be able to interpret it. What is the unit of measurement? Is it a normal value or a series break? Is the value measured or estimated? These metadata are provided as attributes and can be attached to individual observations, or to higher levels as defined by the ComponentSpecification described earlier.
External metadata
This is metadata that describes the dataset as a whole, such as categorization of the dataset, its publisher, and a SPARQL endpoint where it can be accessed. External metadata is described in section 9.

6.1 Data sets and observations

A resource representing the entire data set is created and typed as qb:DataSet and linked to the corresponding data structure definition via the qb:structure property.

Pitfall: Note the capitalization of qb:DataSet, which differs from the capitalization in other vocabularies, such as void:Dataset and dcat:Dataset. This unusual capitalization is chosen for compatibility with the SDMX standard. The same applies to the related property qb:dataSet.

Each observation is represented as an instance of type qb:Observation. In the basic case then values for each of the attributes, dimensions and measurements are attached directly to the observation (remember that these components are all RDF properties). The observation is linked to the containing data set using the qb:dataSet property. For example:

Thus for our running example we might expect to have:

  eg:dataset-le1 a qb:DataSet;
      rdfs:label "Life expectancy"@en;
      rdfs:comment "Life expectancy within Welsh Unitary authorities - extracted from Stats Wales"@en;
      qb:structure eg:dsd-le ;

  eg:o1 a qb:Observation;
      qb:dataSet  eg:dataset-le1 ;
      eg:refArea                 admingeo:newport_00pr ;                  
      eg:refPeriod               <> ;
      sdmx-dimension:sex         sdmx-code:sex-M ;
      sdmx-attribute:unitMeasure <> ;
      eg:lifeExpectancy          76.7 ;

  eg:o2 a qb:Observation;
      qb:dataSet  eg:dataset-le1 ;
      eg:refArea                 admingeo:cardiff_00pt ;                  
      eg:refPeriod               <> ;
      sdmx-dimension:sex         sdmx-code:sex-M ;
      sdmx-attribute:unitMeasure <> ;
      eg:lifeExpectancy          78.7 ;

  eg:o3 a qb:Observation;
      qb:dataSet  eg:dataset-le1 ;
      eg:refArea                 admingeo:monmouthshire_00pp ;                  
      eg:refPeriod               <> ;
      sdmx-dimension:sex         sdmx-code:sex-M ;
      sdmx-attribute:unitMeasure <> ;
      eg:lifeExpectancy          76.6 ;


This flattened structure makes it easy to query and combine data sets but there is some redundancy here. For example, the unit of measure for the life expectancy is uniform across the whole data set and does not change between observations. To cater for situations like this the Data Cube vocabulary allows components to be attached at a high level in the nested structure. Indeed if we re-examine our original Data Structure Declaration we see that we declared the unit of measure to be attached at the data set level. So the corrected example is:

  eg:dataset-le1 a qb:DataSet;
      rdfs:label "Life expectancy"@en;
      rdfs:comment "Life expectancy within Welsh Unitary authorities - extracted from Stats Wales"@en;
      qb:structure eg:dsd-le ;  
      sdmx-attribute:unitMeasure <> ;
  eg:o1 a qb:Observation;
      qb:dataSet  eg:dataset-le1 ;
      eg:refArea                 admingeo:newport_00pr ;                  
      eg:refPeriod               <> ;
      sdmx-dimension:sex         sdmx-code:sex-M ;
      eg:lifeExpectancy          76.7 ;
  eg:o2 a qb:Observation;
      qb:dataSet  eg:dataset-le1 ;
      eg:refArea                 admingeo:cardiff_00pt ;                  
      eg:refPeriod               <> ;
      sdmx-dimension:sex         sdmx-code:sex-M ;
      eg:lifeExpectancy          78.7 ;

  eg:o3 a qb:Observation;
      qb:dataSet  eg:dataset-le1 ;
      eg:refArea                 admingeo:monmouthshire_00pp ;                  
      eg:refPeriod               <> ;
      sdmx-dimension:sex         sdmx-code:sex-M ;
      eg:lifeExpectancy          76.6 ;


In a data set containing just observations with no intervening structure then each observation must have a complete set of dimension values, along with all the measure values. If the set is structured by using slices then further abbreviation is possible, as discussed in the next section.

7. Slices

Slices allow us to group subsets of observations together. This not intended to represent arbitrary selections from the observations but uniform slices through the cube in which one or more of the dimension values are fixed.

Slices may be used for a number of reasons:

To illustrate the use of slices let us group the sample data set into geographic series. That will enable us to refer to e.g. "male life expectancy observations for 2004-6" and guide applications to present a comparative chart across regions.

We first define the structure of the slices we want by associating a "slice key" which the data structure definition. This is done by creating a qb:SliceKey which lists the component properties (which must be dimensions) which will be fixed in the slice. The key is attached to the DSD using qb:sliceKey. For example:

  eg:sliceByRegion a qb:SliceKey;
      rdfs:label "slice by region"@en;
      rdfs:comment "Slice by grouping regions together, fixing sex and time values"@en;
      qb:componentProperty eg:refPeriod, sdmx-dimension:sex .
  eg:dsd-le-slice1 a qb:DataStructureDefinition;
          [qb:dimension eg:refArea;         qb:order 1];
          [qb:dimension eg:refPeriod;       qb:order 2];
          [qb:dimension sdmx-dimension:sex; qb:order 3];
          [qb:measure eg:lifeExpectancy];
          [qb:attribute sdmx-attribute:unitMeasure; qb:componentAttachment qb:DataSet;] ;
      qb:sliceKey eg:sliceByRegion .

In the instance data then slices are represented by instances of qb:Slice which link to the observations in the slice via qb:observation and to the key by means of qb:sliceStructure. Data sets indicate the slices they contain by means of qb:slice. Thus in our example we would have:

  eg:dataset-le2 a qb:DataSet;
      rdfs:label "Life expectancy"@en;
      rdfs:comment "Life expectancy within Welsh Unitary authorities - extracted from Stats Wales"@en;
      qb:structure eg:dsd-le-slice2 ;  
      sdmx-attribute:unitMeasure <> ;
      qb:slice eg:slice2;
  eg:slice2 a qb:Slice;
      qb:sliceStructure  eg:sliceByRegion ;
      eg:refPeriod               <> ;
      sdmx-dimension:sex         sdmx-code:sex-M ;
      qb:observation eg:o1b, eg:o2b; eg:o3b, ... .

  eg:o1b a qb:Observation;
      qb:dataSet  eg:dataset-le2 ;
      eg:refArea                 admingeo:newport_00pr ;                  
      eg:refPeriod               <> ;
      sdmx-dimension:sex         sdmx-code:sex-M ;
      eg:lifeExpectancy          76.7 ;
  eg:o2b a qb:Observation;
      qb:dataSet  eg:dataset-le2 ;
      eg:refArea                 admingeo:cardiff_00pt ;                  
      eg:refPeriod               <> ;
      sdmx-dimension:sex         sdmx-code:sex-M ;
      eg:lifeExpectancy          78.7 ;

  eg:o3b a qb:Observation;
      qb:dataSet  eg:dataset-le2 ;
      eg:refArea                 admingeo:monmouthshire_00pp ;                  
      eg:refPeriod               <> ;
      sdmx-dimension:sex         sdmx-code:sex-M ;
      eg:lifeExpectancy          76.6 ;


Note that here we are still repeating the dimension values on the individual observations. This flattened representation means that a consuming application can still query for observed values uniformly without having to first parse the data structure definition and search for slice definitions. If it is desired, these redundancy can be reduced by declaring different attachment levels for the dimensions. For example:

  eg:dsd-le-slice3 a qb:DataStructureDefinition;
          [qb:dimension eg:refArea;         qb:order 1];
          [qb:dimension eg:refPeriod;       qb:order 2; qb:componentAttachment qb:Slice];
          [qb:dimension sdmx-dimension:sex; qb:order 3; qb:componentAttachment qb:Slice];
          [qb:measure eg:lifeExpectancy];
          [qb:attribute sdmx-attribute:unitMeasure; qb:componentAttachment qb:DataSet;] ;
      qb:sliceKey eg:sliceByRegion .

  eg:dataset-le3 a qb:DataSet;
      rdfs:label "Life expectancy"@en;
      rdfs:comment "Life expectancy within Welsh Unitary authorities - extracted from Stats Wales"@en;
      qb:structure eg:dsd-le-slice3 ;  
      sdmx-attribute:unitMeasure <> ;
      qb:slice eg:slice3 ;
  eg:slice3 a qb:Slice;
      qb:sliceStructure  eg:sliceByRegion ;
      eg:refPeriod               <> ;
      sdmx-dimension:sex         sdmx-code:sex-M ;
      qb:observation eg:o1c, eg:o2c; eg:o3c, ... .

  eg:o1c a qb:Observation;
      qb:dataSet  eg:dataset-le3 ;
      eg:refArea                 admingeo:newport_00pr ;                  
      eg:lifeExpectancy          76.7 ;
  eg:o2c a qb:Observation;
      qb:dataSet  eg:dataset-le3 ;
      eg:refArea                 admingeo:cardiff_00pt ;                  
      eg:lifeExpectancy          78.7 ;

  eg:o3c a qb:Observation;
      qb:dataSet  eg:dataset-le3 ;
      eg:refArea                 admingeo:monmouthshire_00pp ;                  
      eg:lifeExpectancy          76.6 ;


The Data Cube vocabulary allows slices to be nested. We can declare multiple slice keys in a DSD and it is possible for one slice key to be a narrower version of another, represented using qb:subSlice. In that case, when providing non-flattened data with dimensions attached to the slice level, then it is permissible to nest the qb:Slice instances and so further reduce the duplication stating of dimension values. However, in general flat representations are recommended to simplify data consumption. Some tool chains may support (dynamic or static) generation flattened representations from abbreviated data sets.

8. Concept schemes and code lists

The values for dimensions within a data set must be unambiguously defined. They may be typed values (e.g. xsd:dateTime for time instances) or codes drawn from some for of code list. Similarly, many attributes used in data sets represent coded values from some controlled term list rather than free text descriptions. In the Data Cube vocabulary such coded are represented by URI references in the usual RDF fashion.

Sometimes appropriate URI sets already exist for the relevant dimensions (e.g. the representations of area and time periods in our running example). In other cases the data set being converted may use controlled terms from some scheme which does not yet have associated URIs. In those cases we recommend use of SKOS, representing the individual code values using skos:Concept and the overall set of admissible values using skos:ConceptScheme.

We illustrate this with an example drawn from the translation of the SDMX COG code list for gender, as used already in our worked example. The relevant subset of this code list is:

sdmx-code:sex a skos:ConceptScheme;
    skos:prefLabel "Code list for Sex (SEX) - codelist scheme"@en;
    rdfs:label "Code list for Sex (SEX) - codelist scheme"@en;
    skos:notation "CL_SEX";
    skos:note "This  code list provides the gender."@en;
    skos:definition <> ;
    rdfs:seeAlso sdmx-code:Sex ;
    sdmx-code:sex skos:hasTopConcept sdmx-code:sex-F ;
    sdmx-code:sex skos:hasTopConcept sdmx-code:sex-M .

sdmx-code:Sex a rdfs:Class, owl:Class;
    rdfs:subClassOf skos:Concept ;
    rdfs:label "Code list for Sex (SEX) - codelist class"@en;
    rdfs:comment "This  code list provides the gender."@en;
    rdfs:seeAlso sdmx-code:sex .

sdmx-code:sex-F a skos:Concept, sdmx-code:Sex;
    skos:topConceptOf sdmx-code:sex;
    skos:prefLabel "Female"@en ;
    skos:notation "F" ;
    skos:inScheme sdmx-code:sex .

sdmx-code:sex-M a skos:Concept, sdmx-code:Sex;
    skos:topConceptOf sdmx-code:sex;
    skos:prefLabel "Male"@en ;
    skos:notation "M" ; 
    skos:inScheme sdmx-code:sex .

skos:prefLabel is used to give a name to the code, skos:note gives a description and skos:notation can be used to record a short form code which might appear in other serializations. The SKOS specification [SKOS] recommends the generation of a custom datatype for each use of skos:notation but here the notation is not intended for use within RDF encodings, it merely documents the notation used in other representations (which do not use such a datatype).

It is convenient and good practice when developing a code list to also create an Class to denote all the codes within the code list, irrespective of hierarchical structure. This allows the range of an qb:ComponentProperty to be defined by using rdfs:range which then permits standard RDF closed-world checkers to validate use of the code list without requiring custom SDMX-RDF-aware tooling. We do that in the above example by using the common convention that the class name is the same as that of the concept scheme but with leading upper case.

This code list can then be associated with a coded property, such as a dimension:

  eg:sex a sdmx:DimensionProperty, sdmx:CodedProperty;
      qb:codeList sdmx-code:sex ;
      rdfs:range sdmx-code:Sex .

Explicitly declaring the code list using qb:codeList is not mandatory but can be helpful in those cases where a concept scheme has been defined.

In some cases code lists have a hierarchical structure. In particular, this is used in SDMX when the data cube includes aggregations of data values (e.g. aggregating a measure across geographic regions). Hierarchical code lists lists should be represented using the skos:narrower relationship to link from the skos:hasTopConcept codes down through the tree or lattice of child codes. In some publishing tool chains the corresponding transitive closure skos:narrowerTransitive will be automatically inferred. The use of skos:narrower makes it possible to declare new concept schemes which extend an existing scheme by adding additional aggregation layers on top. All items are linked to the scheme via skos:inScheme.

9. DataSet metadata

DataSets should be marked up with metadata to support discovery, presentation and processing. Metadata such as a display label (rdfs:label), descriptive comment (rdfs:comment) and creation date (dcterms:date) are common to most resources. We recommend use of Dublin Core Terms for representing the key metadata annotations commonly needed for DataSets.

9.1 Categorizing a data set

Publishers of statistics often categorize their data sets into different statistical domains, such as Education, Labour, or Transportation. We encourage use of dcterms:subject to record such a classification of an whole data set. The classification terms can include coarse grained classifications, such as the List of Subject-matter Domains from the SDMX Content-oriented Guidelines, and fine grained classifications to support discovery of data sets.

The classification schemes are typically represented using the SKOS vocabulary. For convenience the SMDX Subject-matter Domains have been encoded as a SKOS concept scheme at

Thus our sample dataset might be marked up by:

  eg:dataset1 a qb:DataSet;
      rdfs:label "Life expectancy"@en;
      rdfs:comment "Life expectancy within Welsh Unitary authorities - extracted from Stats Wales"@en;
      dcterms:date "2010-08-11"^^xsd:date;
          sdmx-subject:3.2 ,      # regional and small area statistics
          sdmx-subject:1.4 ,      # Health
          admingeo:wales_gor_l ;  # Wales

where eg:Wales is a skos:Concept drawn from an appropriate controlled vocabulary for places.

9.2 Describing publishers

The organization that publishes a dataset should be recorded as part of the dataset metadata. Again we recommend use of the Dublin Core term dcterms:publisher for this. The organization should be represented as an instance of foaf:Agent, or some more specific subclass such as org:Organization [ORG].

eg:dataset1 a qb:DataSet;
    dc:publisher  .
 a org:Organization, foaf:Agent;
    rdfs:label "Epimorphics Ltd" .    

Note that the SDMX extension vocabulary supports further description of publication pipelines (data flows, reporting taxonomies, maintainers, provision agreements.


This work is based on a collaboration that was initiated in a workshop on Publishing statistical datasets in SDMX and the semantic web, hosted by ONS in Sunningdale, United Kingdom in February 2010 and continued at the ODaF 2010 workshop in Tilburg. The authors would like to thank all the participants at those workshops for their input into this work but especially Arofan Gregory for his patient explanations of SDMX and insight in the need and requirements for a core Data Cube representation.

The authors would also like to thank John Sheridan for his comments, suggestions and support for this work.


Online Analytical Processing Data Cubes,
Linked Data,
Resource Description Framework,
The Statistical Core Vocabulary,
SCOVO: Using Statistics on the Web of data,
Simple Knowledge Organization System,
SDMX Contnent Oriented Guidelines,
Ordnance Survey Administrative Geography Ontology v1,
An Organization Ontology,

Appendix 1: namespaces used in this document

prefix namespace URI vocabulary
rdf RDF core
rdfs RDF Schema
skos Simple Knowledge Organization System
foaf Friend Of A Friend
void Vocabulary of Interlinked Datasets
scovo Statistical Core Vocabulary
dc Dublin Core
qb The Data Cube vocabulary

Appendix 2: vocabulary reference


Class: qb:DataSet Sub class of: qb:Attachable Equivalent to: scovo:Dataset
Represents a collection of observations, possibly organized into various slices, conforming to some common dimensional structure.


Class: qb:Observation Sub class of: qb:Attachable Equivalent to: scovo:Item
A single observation in the cube, may have one or more associated measured values
Property: qb:dataSet ( qb:Observation -> qb:DataSet )
indicates the data set of which this observation is a part
Property: qb:observation ( qb:Slice -> qb:Observation )
indicates a observation contained within this slice of the data set


Class: qb:Slice Sub class of: qb:Attachable
Denotes a subset of a DataSet defined by fixing a subset of the dimensional values, component properties on the Slice
Property: qb:slice ( qb:DataSet -> qb:Observation )
Indicates a subset of a DataSet defined by fixing a subset of the dimensional values
Property: qb:subSlice ( qb:Slice -> qb:Slice )
Indicates a narrower slice which has additional fixed dimensional values, for example a time-series slice might a subSlice of a slice which spans both time and geographic area

Dimensions, Attributes, Measures

Class: qb:Attachable
Abstract superclass for everything that can have attributes and dimensions
Class: qb:ComponentProperty Sub class of: rdf:Property
Abstract super-property of all properties representing dimensions, attributes or measures
Class: qb:DimensionProperty Sub class of: qb:ComponentProperty qb:CodedProperty
The class of components which represent the dimensions of the cube
Class: qb:AttributeProperty Sub class of: qb:ComponentProperty
The class of components which represent attributes of observations in the cube, e.g. unit of measurement
Class: qb:MeasureProperty Sub class of: qb:ComponentProperty
The class of components which represent the measured value of the phenomenon being observed
Class: qb:CodedProperty Sub class of: qb:ComponentProperty
Superclass of all coded ComponentProperties

Reusable general purpose component properties

Property: qb:measureType ( -> qb:MeasureProperty )
Generic measure dimension, the value of this dimension indicates which measure (from the set of measures in the DSD) is being given by the obsValue (or other primary measure)

Data Structure Definitions

Class: qb:DataStructureDefinition Sub class of: qb:ComponentSet
Defines the structure of a DataSet or slice
Property: qb:structure ( qb:DataSet -> qb:DataStructureDefinition )
indicates the structure to which this data set conforms
Property: qb:component ( qb:DataStructureDefinition -> qb:ComponentSpecification )
indicates a component specification which is included in the structure of the dataset

Component specifications - for qualifying component use in a DSD

Class: qb:ComponentSpecification Sub class of: qb:ComponentSet
Used to define properties of a component (attribute, dimension etc) which are specific to its usage in a DSD.
Class: qb:ComponentSet
Abstract class of things which reference one or more ComponentProperties
Property: qb:componentProperty ( qb:ComponentSet -> qb:ComponentProperty )
indicates a ComponentProperty (i.e. attribute/dimension) expected on a DataSet, or a dimension fixed in a SliceKey
Property: qb:order ( qb:ComponentSpecification -> xsd:int )
indicates a priority order for the components of sets with this structure, used to guide presentations - lower order numbers come before higher numbers, un-numbered components come last
Property: qb:componentRequired ( qb:ComponentSpecification -> xsd:boolean )
Indicates whether a component property is required (true) or optional (false) in the context of a DSD or MSD
Property: qb:componentAttachment ( qb:ComponentSpecification -> rdfs:Class )
Indicates the level at which the component property should be attached, this might an qb:DataSet, qb:Slice or qb:Observation, or a qb:MeasureProperty.
Property: qb:dimension ( -> qb:DimensionProperty ; sub property of: qb:componentProperty )
An alternative to qb:componentProperty which makes explicit that the component is a dimension
Property: qb:measure ( -> qb:MeasureProperty ; sub property of: qb:componentProperty )
An alternative to qb:componentProperty which makes explicit that the component is a measure
Property: qb:attribute ( -> qb:AttributeProperty ; sub property of: qb:componentProperty )
An alternative to qb:componentProperty which makes explicit that the component is a attribute
Property: qb:measureDimension ( -> qb:DimensionProperty ; sub property of: qb:componentProperty )
An alternative to qb:componentProperty which makes explicit that the component is a measure dimension

Slice definitions

Class: qb:SliceKey Sub class of: qb:ComponentSet
Denotes a subset of the component properties of a DataSet which are fixed in the corresponding slices
Property: qb:sliceStructure ( qb:Slice -> qb:SliceKey )
indicates the sub-key corresponding to this slice
Property: qb:sliceKey ( qb:DataSet -> qb:SliceKey )
indicates a slice key which is used for slices in this dataset


Property: qb:concept ( qb:ComponentProperty -> skos:Concept )
gives the concept which is being measured or indicated by a ComponentProperty
Property: qb:codeList ( qb:CodedProperty -> skos:ConceptScheme )
gives the code list associated with a CodedProperty