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Querying the Global Cube: Integration of Multidimensional Datasets from the Web

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Querying the Global Cube: Integration of Multidimensional Datasets from the Web Querying the Global Cube: Integration of Multidimensional Datasets from the Web Benedikt K¨ampgen1, Steffen Stadtm¨uller1, and Andreas Harth1 Institute AIFB, Karlsruhe Institute of Technology, Karlsruhe, Germany, fbenedikt.kaempgen,steffen.stadtmueller,[email protected] Abstract. National statistical indicators such as the Gross Domestic Product per Capita are published on the Web by various organisations such as Eurostat, the World Bank and the International Monetary Fund. Uniform access to such statistics will allow for elaborate analysis and vi- sualisations. Though many datasets are also available as Linked Data, heterogeneities remain since publishers use several identifiers for com- mon dimensions and differing levels of detail, units, and formulas. For queries over the Global Cube, i.e., the integration of available datasets modelled in the RDF Data Cube Vocabulary, we extend the well-known Drill-Across operation over data cubes to consider implicit overlaps be- tween datasets in Linked Data. To evaluate more complex mappings we define the Convert-Cube operation over values from a single dataset. We generalise the two operations for arbitrary combinations of multiple datasets with the Merge-Cubes operation and show the feasibility of the analytical operations for integrating government statistics. 1 Introduction Given the Open Data policy of governments and intergovernmental organisa- tions, citizens can access many statistical datasets online. For example, one can find the Gross Domestic Product of countries per year from Eurostat, the World Bank and the International Monetary Fund. Integrating such multidimensional datasets will allow for more complete answers and detailed comparisons of indi- cators. For example, the GDP of a country from one and the population from another dataset allow analysts to compute the GDP per Capita and to cross- check these derived values with values from other publishers. Towards providing uniform access, many datasets are also made available { directly or by third-parties { as Linked Data reusing the RDF Data Cube Vocabulary (QB), the quasi-standard for publishing multidimensional datasets. Although analytical operations over QB datasets have been defined [5] and in- dicators from two datasets can be compared in visualisations [3] integration is still difficult [13] if datasets: { contextualise their indicators with different dimensions, e.g., \geo-location", \time" or \gender", { use different names for the same dimensions, e.g., \geo" and \location" or dimension values, e.g., \DE" and \Germany", 2 { provide different levels of detail, e.g., regional or national level, { use different units of measurement, e.g., \Million Euro" and \Euro", { and publish datasets derived from other datasets, e.g., \GDP per Capita" computed via \Nominal GDP" divided by \Population". Defining a global schema over heterogeneous datasets published as Linked Data is challenging since related work has so far concentrated on relational set- tings [2] with few sources that are centrally integrated. Relationships between different datasets are often buried in informal descriptions, and the routines to resolve semantic conflicts are provided in code or external background informa- tion [10]. After introducing preliminaries and a motivating scenario in Section 2 and before describing related work in Section 6, and concluding with Section 7, we provide the following contributions: { We define the Global Cube using the Drill-Across operation [6] over datasets published as Linked Data. We describe how to derive previously unknown values in the Global Cube using OWL axioms as well as conversion [10] and merging correspondences [2] (Section 3). { We analyse the complexity of generating the Global Cube (Section 4) and show the feasibility of our approach for government statistics (Section 5). 2 Preliminaries and Government Statistics Scenario We use the common Multidimensional Data Model (MDM) of Data Cubes as a conceptualisation of QB datasets since we then can apply analytical operations such as Slice and Dice [5, 7]: An MDM consists of data cubes (instances of qb:DataSet) with facts (instances of qb:Observation). Every data cube defines measures (instances of qb:MeasureProperty) and dimensions (qb:Dimension- Property). Every fact in the data cube has a value for each of the measures and dimensions. The values of the measures are functionally dependent on the values of the dimensions, i.e., for every possible combination of dimension values, only one fact can be contained in the data cube. Members are the possible dimension values and may be grouped into levels along hierarchies (e.g., instances of qb:CodeList). For instance, see Table 1 for an overview of data cubes from existing datasets to which an integration system should provide access. The table shows in the rows all data cubes and in the columns all their dimensions. The cells give example members for a dimension, \-" if the dimension is not and \... " if the dimension may be used. For readability reasons, we describe URIs with namespaces1, slightly abusing the W3C CURIE syntax for expressing compact URIs. Relative URIs such as :DE are defined by the data source in the respective context. Since most publishers { also of our scenario data cubes { follow the practice of using an unspecific measure sdmx-measure:obsValue and a dimension indi- cating the measured variable, e.g., estatwrap:indic na and gesis:variable, 1 Use http://prefix.cc/ to look up prefix definition. 3 Table 1. Overview of data cubes in rows with their dimensions in columns and dimen- sion members available in the scenario in cells. Cube nDimension estat- estatwrap-dcterms: gesis: gesis: estatwrap- estat- estat- wrap:geo :unit date geo variable :indic na wrap:sex wrap:age eurostat:id/ :DE... :PCH PRE...2001... - - - - - tec00115#ds (GDP Growth) allbus: - - 2004... :00... :v590 1...- - - ZA4570v590.rdf#ds (Unemploy. Fear) eurostat:id/ :DE... ... 2001... - - ... ... ... tsdcc310#ds... (EU 2020 Indicator) eurostat:id/ :DE... :EUR HAB...2001... - - :NGDPH... - - nama aux gph#ds (GDP Per Capita) eurostat:id/ :DE... :MIO EUR...2001... - - :B1G, - - nama gdp c#ds :D21 M- (GDP Components) D31 eurostat:id/ :DE... - 2001... - - - :F... :Y18... demo pjan#ds (Popu- lation) and since cubes with multiple measures can be transformed to this form by in- troducing a new measure dimension, for the remainder of this paper we assume data cubes to have only one general measure, sdmx-measure:obsValue. Every multidimensional element is published by a data source identified by the namespace. The eurostat namespace2 makes available thousands of data cubes with indicators about European countries from Eurostat. For instance, the GDP Growth with the growth rate of the gross domestic product of all Euro- pean countries per year, with the unit \percentage change on previous period" (:PCH PRE) and for the geo dimension denoting Germany as :DE. Also, Eurostat provides citizens with EU 2020 Indicators, e.g., the energy dependence, produc- tivity, and intensity as well as the greenhouse gas emission. Table 1 gives an example of one of those datasets; every EU 2020 Indicator cube exhibits the geo and time dimension and can contain other dimensions from the same data source. GDP Components provides granular values from which other indicators can be computed. For instance, the Nominal GDP (GDP at market prices) can be computed from adding the \Total gross value added" (estatwrap:indic na of :B1G) and \Taxes less subsidies on products" (:D21 M D31); similarly, the Nominal GDP divided by the Population should result in the GDP Per Capita. The allbus namespace3 provides information about \attitudes, behaviour and social structure in Germany" from the Cumulated German General So- cial Survey (ALLBUS). Among others, we can retrieve a survey where Ger- man employees were asked about their fear of becoming unemployed (Unem- ploy. Fear). The measure describes the number of answers for a given question. The allbus:variable dimension denotes the type of answers given \No fear", 2 http://estatwrap.ontologycentral.com/ 3 http://lod.gesis.org/lodpilot/ALLBUS/ 4 \Yes, of becoming unemployed", \Yes, of having to change employer"4. The :geo dimension describes the participants' country, e.g., Germany is denoted via :00. An analytical query over a data cube can be described as a nested set of OLAP operations and executed using SPARQL over QB datasets [6, 5, 7]: Projection selects measures from a data cube; Dice filters for facts with cer- tain members as dimension values; Slice sets dimensions to an implicit ALL member so that they can be aggregated over and removed; and Roll-Up aggre- gates dimension members to a higher level of the dimension hierarchy. Measures are aggregated using an aggregation function, e.g., described by the measure or by the user in the query [5]. We now describe three example queries that citizens may want to pose over the available datasets. Unemployment Fear and GDP Growth (UNEMPLOY): Citizens want to compare the indicator about unemployment fear with the \GDP Growth" over time for Germany to get insights about the relation between GDP and em- ployees' perceived situation. Comparing EU 2020 - Indicators (EU2020): Here, citizens want to aggregate and compare important metrics about European countries by average for all countries and to show the aggregated numbers per year, so that trends of important indicators for European countries become visible. GDP per Capita from Different Sources (GDP CAP): Here, citizens may want to confirm that the
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