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.
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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:
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-PRIMER] 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:
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 www.sdmx.org. 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.
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.
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.
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
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:
rdf, rdfs-- the core RDF namespaces
dc-- Dublin Core
skos-- Simple Knowledge Organization System
foaf-- Friend Of A Friend
void-- Vocabulary of Interlinked Datasets
scovo-- Statistical Core Vocabulary
We also introduce the prefix
qb for the Data Cube
All RDF examples are written in Turtle syntax [TURTLE-TR].
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, gender) along with metadata describing what has been measured (e.g. economic activity, population), how it was measured and how the observations are expressed (e.g. units, multipliers, status). We can think of the statistical data set as a 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).
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.
In order to illustrate the use of the data cube vocabulary we will
use a small demonstration
data set extracted from
number 003311 which describes life expectancy broken down by region
(unitary authority), age and time. The extract we will use is:
We can see that there are three dimensions - time period (rolling averages over three year timespans), region and 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.
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
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
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.
The Data Cube vocabulary represents the dimensions, attributes and measures
as RDF properties. Each is an instance of the abstract
class, which in turn has sub-classes
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
or as a symbolic value (e.g. a URI drawn from the reference time URI set developed by data.gov.uk).
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
qb:ComponentProperty to the concept it represents. We use the SKOS
vocabulary [SKOS-PRIMER] 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
skos:Concept along with the specific
qb:ComponentProperty. However, if
such reuse is not expected then it is not required to do so - the
link is optional and a simple instance of the appropriate subclass of
The representation of the possible values of the component is described using the
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
In such a case the
rdfs:range of the component might be left as simply
a useful design pattern is to also define an
whose members are all the
skos:Concepts within a particular scheme. In that way
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
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
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.
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:
sdmx-concept: SKOS Concepts for each COG defined concept;
sdmx-code: SKOS Concepts and ConceptSchemes for each COG defined code list;
sdmx-dimension: component properties corresponding to each COG concept that can be used as a dimension;
sdmx-attribute: component properties corresponding to each COG concept that can be used as a attribute;
sdmx-measure: component properties corresponding to each COG concept that can be used as a measure.
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.
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
to represent the time period itself it would be convenient to use the data.gov.uk 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
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
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.
To combine the components into a specification for the structure of this
dataset we need to declare a
resource which in turn will reference a set of
qb:DataStuctureDefinition will be reusable across other data sets with the same structure.
In the simplest case the
qb:ComponentSpecification simply references the
qb:ComponentProperty (ususally using one of the sub properties
However, it is also possible to qualify the
component specification in several ways.
qb:order. This order carries no semantics but can be useful to aid consuming agents in generating appropriate user interfaces. It can also be useful in the publication chain to enable synthesis of appropriate URIs for observations.
qb:componentAttachmentproperty of the specification should reference the class corresponding to the attachment level (e.g.
qb:DataSetfor attributes that will be attached to the overall data set).
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.
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, 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, a single "cell" in the data cube will typically contain values for multiple measures.
The data cube vocabulary permits either representation approach to be used though they cannot be mixed within the same data set.
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
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:component [ 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.
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 --
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:component [ 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
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:
A resource representing the entire data set is created and typed as
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
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 <http://reference.data.gov.uk/id/gregorian-interval/2004-01-01T00:00:00/P3Y> ; sdmx-dimension:sex sdmx-code:sex-M ; sdmx-attribute:unitMeasure <http://dbpedia.org/resource/Year> ; eg:lifeExpectancy 76.7 ; . eg:o2 a qb:Observation; qb:dataSet eg:dataset-le1 ; eg:refArea admingeo:cardiff_00pt ; eg:refPeriod <http://reference.data.gov.uk/id/gregorian-interval/2004-01-01T00:00:00/P3Y> ; sdmx-dimension:sex sdmx-code:sex-M ; sdmx-attribute:unitMeasure <http://dbpedia.org/resource/Year> ; eg:lifeExpectancy 78.7 ; . eg:o3 a qb:Observation; qb:dataSet eg:dataset-le1 ; eg:refArea admingeo:monmouthshire_00pp ; eg:refPeriod <http://reference.data.gov.uk/id/gregorian-interval/2004-01-01T00:00:00/P3Y> ; sdmx-dimension:sex sdmx-code:sex-M ; sdmx-attribute:unitMeasure <http://dbpedia.org/resource/Year> ; 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 <http://dbpedia.org/resource/Year> ; . eg:o1 a qb:Observation; qb:dataSet eg:dataset-le1 ; eg:refArea admingeo:newport_00pr ; eg:refPeriod <http://reference.data.gov.uk/id/gregorian-interval/2004-01-01T00:00:00/P3Y> ; 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 <http://reference.data.gov.uk/id/gregorian-interval/2004-01-01T00:00:00/P3Y> ; 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 <http://reference.data.gov.uk/id/gregorian-interval/2004-01-01T00:00:00/P3Y> ; 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.
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
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:component [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
link to the observations in the slice via
qb:observation and to the key by means
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 <http://dbpedia.org/resource/Year> ; qb:slice eg:slice2; . eg:slice2 a qb:Slice; qb:sliceStructure eg:sliceByRegion ; eg:refPeriod <http://reference.data.gov.uk/id/gregorian-interval/2004-01-01T00:00:00/P3Y> ; 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 <http://reference.data.gov.uk/id/gregorian-interval/2004-01-01T00:00:00/P3Y> ; 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 <http://reference.data.gov.uk/id/gregorian-interval/2004-01-01T00:00:00/P3Y> ; 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 <http://reference.data.gov.uk/id/gregorian-interval/2004-01-01T00:00:00/P3Y> ; 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, this redundancy can be reduced by declaring different attachment levels for the dimensions. For example:
eg:dsd-le-slice3 a qb:DataStructureDefinition; qb:component [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 <http://dbpedia.org/resource/Year> ; qb:slice eg:slice3 ; . eg:slice3 a qb:Slice; qb:sliceStructure eg:sliceByRegion ; eg:refPeriod <http://reference.data.gov.uk/id/gregorian-interval/2004-01-01T00:00:00/P3Y> ; 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.
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 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 codes are
represented by URI references in the usual RDF fashion.
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
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 <http://sdmx.org/wp-content/uploads/2009/01/02_sdmx_cog_annex_2_cl_2009.pdf> ; 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
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
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
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
DataSets should be marked up with metadata to support discovery, presentation and
processing. Metadata such as a display label (
descriptive comment (
rdfs:comment) and creation date (
are common to most resources. We recommend use of Dublin Core Terms
for representing the key metadata annotations commonly needed for DataSets.
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
a 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 http://purl.org/linked-data/sdmx/2009/subject#.
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; dcterms:subject sdmx-subject:3.2 , # regional and small area statistics sdmx-subject:1.4 , # Health admingeo:wales_gor_l ; # Wales ...
eg:Wales is a
skos:Concept drawn from an appropriate controlled
vocabulary for places.
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
some more specific subclass such as
eg:dataset1 a qb:DataSet; dc:publisher <http://example.com/meta#organization> . <http://example.com/meta#organization> a org:Organization, foaf:Agent; rdfs:label "Example org" .
Note that the SDMX extension vocabulary supports further description of publication pipelines (data flows, reporting taxonomies, maintainers, provision agreements).
|skos||http://www.w3.org/2004/02/skos/core#||Simple Knowledge Organization System|
|foaf||http://xmlns.com/foaf/0.1/||Friend Of A Friend|
|void||http://rdfs.org/ns/void#||Vocabulary of Interlinked Datasets|
|scovo||http://purl.org/NET/scovo#||Statistical Core Vocabulary|
|qb||http://purl.org/linked-data/cube#||The Data Cube vocabulary|
qb:DataSetSub class of:
qb:ObservationSub class of:
qb:SliceSub class of:
qb:ComponentPropertySub class of:
qb:DimensionPropertySub class of:
qb:AttributePropertySub class of:
qb:MeasurePropertySub class of:
qb:CodedPropertySub class of:
qb:DataStructureDefinitionSub class of:
qb:ComponentSpecificationSub class of:
qb:DimensionProperty; sub property of:
qb:MeasureProperty; sub property of:
qb:AttributeProperty; sub property of:
qb:DimensionProperty; sub property of:
qb:SliceKeySub class of:
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.
Based on early use experiences with the vocabulary the working group is considering some clarifications and modifications to the specification. Each of the candidate areas under consideration are listed as issues below. Note that there is, as yet, no commitment that all (or indeed any) of these areas will be addressed by the working group.
Specify additional well-formedness criteria to which cube-publishers should adhere to facilitate tool interoperability.
Consider extending the vocabulary to support declaring relations between data cubes (or between measures within a cube).
The Data Cube vocabulary allows hierarchical code lists to be used as dimensions values by means of SKOS. Consider whether to extend this to support use of other hierarchical relations (e.g. geo-spatial containment) without requiring mapping to SKOS.
One use case for the Data Cube vocabulary is for the publication of observational, sensor network and forecast data sets. Existing standards for such publication include OGC Observations & Measurements (ISO 19156). There are multiple ways that Data Cube can be mapped to the logical model of O&M. Consider making an explicit statement of the ways in which Data Cube can be related to O&M as guidance for users seeking to work with both specifications.
Experience with Data Cube has shown that publishers often wish
to publish slices comprising arbitrary collections of
Consider supporting this usage, either through a clarification
qb:Slice or through an additional collection mechanism.
qb:subslice in abbreviated datasets can result
in ambiguity. Consider
clarifying or deprecating
Bring all references into W3C style
No normative references.