A Browser for Linked Data Cubes

Data Cube Vocabulary Workshop26th May 2015, Luxembourg

Developing a Browser for Linked Data CubesEvangelos Kalampokis

2Eurostat Workshop

The OpenCube Browser enables exploring an RDF Data Cube by presenting the values in a table.

It is currently based on the InformationWorkbench open source platform.

It requires as an input the URI of a cube.

The OpenCube Browser

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Eurostat Workshop 326th May 2015


It presents a 2-dimensitonal slice of an

RDF cube in a table



Change the two axes of the table (in the case of cubes with more

than 2 dimensions)



Change the fixed values of the dimensions that are not

included in the table



Change the language of the values

Eurostat Workshop 7

The feedback received by the employees of the Flemish Government It should be understood in the context of a department considering

alternatives to an existing proprietary solution. Overall, users were satisfied. Main criticism by some users

Usefulness: “We don’t see added value compared to other tools” Easy of use

User interface is not clear to an average user Performance (response time) should be better

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Evaluation of the OpenCube Browser

Eurostat Workshop 8

The OpenCube OLAP Browser enhances previous version. The OpenCube OLAP Browser

New clear and user-friendly interface (similar to an OLAP browser) Enables browsing integrated multiple RDF cubes

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OpenCube OLAP Browser

http://83.212.122.81:8888/resource/OpenCubeOLAPBrowser



Eurostat Workshop 9

We start with an empty canvas User can add dimensions and measures

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Address easy of use related comments

Eurostat Workshop 10

Linked Data as a competitive advantage over existing tools. Enabling the integration of RDF data cubes across the Web. One can start with an initial RDF cube, identify compatible cubes

from other sources and browse the new expanded cube.

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Address usefulness related comments


The OpenCube OLAP browser is a proof of concept of this vision

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The vision of exploiting Expanded Linked Data Cubes

LATC’s Eurostat ExampleMain economic variables (2006)Dimensions: timePeriod (2006) Geopolitical entity Economical indicator for structural

business statistics Classification of economic activities

Measures: ObsValue

Main economic variables (2007)Dimensions:timePeriod (2007)Geopolitical entityEconomical indicator for structural business statisticsClassification of economic activities

Measures: ObsValue


Emphasizing on Cubes Integration

Discover compatible to join linked data cubes.

Establish typed links between compatible to join cubes

Create expanded cubes by increasing the size of one of the sets that define a cube


Some functionalities of the OpenCube OLAP Browser require the output of functionalities performed by other tools of the OpenCube Toolkit.

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Tools and Functionalities

OpenCube OLAP Browser

B1: Present 2D slice of a cube

B2: Add/remove Measures

B3: Support multiple languages

B4: Add/remove Dimensions

B5: Roll-up/drill-down

B6: Integrated view of cubes

Aggregator

A1: Compute aggregations across dimension

A2: Compute aggregations across hierarchy

requires Compatibility Explorer

CE1: Identify compatible cubes


B1: Present a two-dimensional slice of a cube


B2: Add/remove measures

Adding one more measure to be presented


Change the language of the available data

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B3: Support multiple languages


B4: Add/remove dimensions

Adding one more dimension to be presented


Functionality B4 requires the use of the OpenCube Aggregator tool in order to pre-compute aggregations across all dimensions.

The Aggregator creates 2n-1 sub-cubes from a cube of n dimensions. We define this set of cubes as an Aggregation Set.

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A1: Compute aggregations across dimension

Time

GeoSex

Time Time

Geo Sex

Geo

Sex

Time GeoSex

Total

Three dimensions

Two dimensions

One dimension

No dimensions


If a user adds/removes one dimension the OpenCube OLAP browser picks up and presents a new cube from the aggregation set based on the selected dimensions.

In order to improve performance we establish typed links between the pre-computed cubes and the Aggregation Set

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B4: Add/remove dimension


Select the aggregation function This can be done based on the unit of measure

Meaningless aggregations Users intervention is probably required

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Challenges related to the Aggregator


This functionality is under development It requires pre-computing aggregations across a hierarchy using the

OpenCube Aggregator tool

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B5: Roll-up/Drill-down

Under Development


It enriches an existing cube with new observations by using a hierarchy.

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A2: Compute aggregations across hierarchy

Time

Geo

Sex

city1

city2

city3

+city4

country1

region1

region2

city1

city2

city3

city4Time

Geo

city1city2city3city4=

region1

region2country

1 Sex

24Eurostat Workshop

The user selects a cube and an operation

Add new measure Add new value to dimension

The tool presents all the available compatible cubes.

The user selects one of the cubes.

B6: Integrated view of multiple cubes (1/2)

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The OpenCube OLAP Browser presents an integrated view of the two RDF data cubes.

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B6: Integrated view of multiple cubes (2/2)

Added values


The OpenCube Compatibility Explorer pre-identifies and establishes typed links between compatible to merge cubes.

Two types of compatibility Add new measure compatible Add new value to dimension compatible

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E1: Identify compatible cubes


Identification of same dimension If the dimensions use codelists, the code list URI is used to determine

equality. If no code lists exist, the dimension URI is used to determine equality

Identification of equal measures Two measures are considered equal if they have the same URI

The measure obsValue does not explain what is actually measured at the cube so equality cannot be determined

Identify and make available the key reference datasets that connect different statistical datasets – e.g. concerning geography, physical assets and areas of government policy.

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Challenges related to compatibility (1/2)


The approach of expanding data cubes requires small cubes i.e. cubes that describe few measures.

…but how a cube can be modelled? For example

LATC’s Eurostat: more than 5000 cubes with few measures per cube Irish Census 2011: 682 cubes with one measure per cube Digital Agenda: Only 2 cubes with more than 100 measures per cube.

We need common understanding on how to conceptually model a Cube.

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Challenges related to compatibility (2/2)

Data level Operations per Functionality

29

Browser functionality Data level Operations

B1: Present 2D slice of a cube • Identify single cube measure (D1), multiple cube measures (D2)• Identify cube dimensions (D3)• Identify cube attributes (D4) • Identify dimension values (D5)

B2: Add/remove measures D3

B3: Multilinguality Data available at multiple languages (D6)

B4: Add/remove Dimensions D1, D2

B5: Roll-up/drill-down Definition of a hierarchy for hierarchical data (D7)

B6: Integrated view of cubes D1, D2, D3, D4, D5

Data level Operations per Functionality

30

Aggregator functionality Data level Operations

A1: Compute aggregations across dimension Identify the unit of cube’s single measure (D8), multiple measures (D9)

A2: Compute aggregations across hierarchy D7, D8, D9

Compatibility Explorer Functionality Data level Operations

CE1: Identify compatible cubes D1, D2, D3, D4, D5, D7


According to the QB vocabulary the qb:DataStructureDefinition must have a qb:MeasureProperty that defines the measure.

The Browser follows this approach However, other approach are used:

LATC’s Eurostat dataset defines sdmx-measure:obsValue as a qb:DimensionProperty

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D1: Identify single cube measure


The QB vocabulary offers two options: Multimeasure Observations

Define multiple qb:MeasureProperty one for each measure attached to qb:DataStructureDefinition

Use all qb:MeasureProperty at each observation

Measure Dimension Define multiple qb:MeasureProperty components one for each measure attached to

qb:DataStructureDefinition Define a special qb:DimensionProperty named qb:measureType At each observation use one qb:MeasureProperty. The dimension qb:measureType

defines the qb:MeasureProperty used at the specific observation

The browser follows the Multimeasure Observations approach

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D2: Identify multiple measures


The Open Data Communities use the Measure Dimension approach The Irish 2011 Census dataset defines only one measure per cube However other approaches are followed:

LATC’s Eurostat uses a qb:DimensionProperty to encode multiple measures The same holds for Digital Agenda

Other challenges: In multiple-measure observation, missing value in one measure will invalidate

the entire observation.

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D2: Identify multiple measures (Challenges)


The QB vocabulary defines that the qb:DataStructureDefinition must have a qb:DimensionProperty for every dimension.

The Browsers assumes that qb:DimensionProperty defines ONLY dimensions of a cube.

Other approaches: In LATC’s Eurostat dataset (a) attributes e.g. sdmx-dimension:freq ,

property:unit and (b) sdmx-measure:obsValue are declared as qb:DimensionProperty

Digital Agenda defines the breakdown dimension, which is actually a “super-dimension” in which one can add all the values of dimensions other than time and geography.

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D3: Identify cube dimensions


According to the QB vocabulary a qb:DimensionProperty is connected to a skos:ConceptScheme with the values of the dimension.

The Browser get the URIs of the dimension values from the observations and the labels from the Concept Scheme.

Some other approaches: Although the Irish CSO does not connect a qb:DimensionProperty with a

qb:codeList property, it gets the dimension values from a Concept Scheme. Other challenges:

How to encode the order of dimension values

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D5: Identify dimension values


The names of a qb:ComponentProperty can be Directly defined either as rdfs:label or skos:prefLabel Defined through a skos:Concept connected with the qb:concept to the

qb:ComponentProperty The Browser use both ways to get the names of the values. Other approaches

LATC’s Eurostat uses rdfs:label attached to the qb:ComponentPropoerty Open Data Communities in some cubes uses the first approach and in some

others use the second

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D6: Data available in multiple languages


According to the RDF Data Cube Voc: Use qb:HierarchicalCodeLists Use both SKOS and XKOS vocabularies:

skos defines the hierarchy (skos:broader, skos:inScheme, skos:member etc.) xkos defines the classification levels (xkos:ClassificationLevel, xkos:numberOfLevels,

xkos:depth etc)

The Browser assumes that The levels of the hierarchies used by the cube should be defined using the

xkos:ClassificationLevel concept. Each member of the level is defined as a skos:Concept object which is related

to the xkos:ClassificationLevel by the skos:member property

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D7: Define a hierarchy


Other practices LATC’s Eurostat does not define hierarchies although hierarchical data exist in

the same cube e.g. city(Riga), region (South West Wales), country (Greece), set of countries (EU28) (aei_ps_alt.ttl)

Irish Census does not define hierarchies. It has data about 12 geographical levels, it defines different Code Lists per geo level, and different cube per geo level.

Open Data Communities re-uses URIs from the Spatial Relations Ontology defined by Ordnance Survey and thus reuse the hierarchy.

The Flemish Government’s dataset defines hierarchies based on SKOS and XKOS

Other challenges xkos semantics of xkos:isPartOf and xkos:generalises seem wrong

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Challenges regarding hierarchies


Unit attach to the dataset Declare that the sdmx-attribute:unitMeasure can be attached

(qb:componentAttachment) to the qb:DataSet Use the sdmx-attribute:unitMeasure to the qb:Dataset to define the

measure’s unit In case multiple units are used for the same measure e.g. Kilos and

grams for the measure weight, then: Declare that the sdmx-attribute:unitMeasure can be attached

(qb:componentAttachment) to the qb:Observation Use the sdmx-attribute:unitMeasure at each observation to define the

measure’s unit The Browser does not support multiple units for the same measure

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D8: Identify the unit of cube’s single measure


Which measurement units to use; is there an easy to use universal standard?

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Challenges regarding unit of measure


Challenges: “Note that one limitation of the multi-measure approach is that it is not

possible to attach an attribute to a single observed value.” If one splits the observations and has one measure per observation then

again this seems contradicting to the vocabulary: “It is also possible to attach attributes to a qb:MeasureProperty in which case the attribute is intended to apply only to that property and not to the observations in which that property occurs.”

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D9: Identify the unit of cube’s multiple measures


The Browser assumes that the geospatioal dimension is the sdmx:refArea or a subproperty of the sdmx:refArea

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Identify the geospatial dimension of the cube


The time can be represented either as URI or as a literal. The Browser takes into account both, Approaches in existing datasets:

LATC’s Eurostat uses dcterms:date in the DSD and sdmx-dimension: timePeriod in the data. No URIs but literal.

The Irish Census data does not have a time dimension Challenges:

Which is better: atomic values or identifiers for e.g. year

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Identify the time dimension of the cube


Data & Analytics

A Browser for Linked Data Cubes