* upload version from 2012-02-22
authorbkaempge
Thu, 28 Feb 2013 00:59:24 +0100
changeset 305 818be3f5aa5d
parent 304 9e87cf6a32b2
child 306 83d525c933ea
* upload version from 2012-02-22
data-cube-ucr/data-cube-ucr-20120222/figures/Eurostat_GPDE_Example.png
data-cube-ucr/data-cube-ucr-20120222/figures/FIOS_example.PNG
data-cube-ucr/data-cube-ucr-20120222/figures/Level_above_msl_3_locations.png
data-cube-ucr/data-cube-ucr-20120222/figures/Relationships_Statistical_Data_Cogs_Example.png
data-cube-ucr/data-cube-ucr-20120222/figures/SDMX_Web_Dissemination_Use_Case.png
data-cube-ucr/data-cube-ucr-20120222/figures/modeling_quantity_measurement_observation.png
data-cube-ucr/data-cube-ucr-20120222/figures/pivot_analysis_measurements.PNG
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data-cube-ucr/data-cube-ucr-20120222/respec-config.js
data-cube-ucr/data-cube-ucr-20120222/respec-ref.js
data-cube-ucr/data-cube-ucr-2013-02-28/index.html
Binary file data-cube-ucr/data-cube-ucr-20120222/figures/Eurostat_GPDE_Example.png has changed
Binary file data-cube-ucr/data-cube-ucr-20120222/figures/FIOS_example.PNG has changed
Binary file data-cube-ucr/data-cube-ucr-20120222/figures/Level_above_msl_3_locations.png has changed
Binary file data-cube-ucr/data-cube-ucr-20120222/figures/Relationships_Statistical_Data_Cogs_Example.png has changed
Binary file data-cube-ucr/data-cube-ucr-20120222/figures/SDMX_Web_Dissemination_Use_Case.png has changed
Binary file data-cube-ucr/data-cube-ucr-20120222/figures/modeling_quantity_measurement_observation.png has changed
Binary file data-cube-ucr/data-cube-ucr-20120222/figures/pivot_analysis_measurements.PNG has changed
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/data-cube-ucr/data-cube-ucr-20120222/local-style.css	Thu Feb 28 00:59:24 2013 +0100
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+    //copyrightStart:       "2010",
+
+    // the specification's short name, as in http://www.w3.org/TR/short-name/
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+++ b/data-cube-ucr/data-cube-ucr-20120222/respec-ref.js	Thu Feb 28 00:59:24 2013 +0100
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--- a/data-cube-ucr/data-cube-ucr-2013-02-28/index.html	Thu Feb 28 00:55:59 2013 +0100
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-<body><div class="head">
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-      <a href="http://www.w3.org/"><img width="72" height="48" src="https://www.w3.org/Icons/w3c_home" alt="W3C"></a>
-    
-  </p>
-  <h1 class="title" id="title">Use Cases and Requirements for the Data Cube Vocabulary</h1>
-  
-  <h2 id="w3c-editor-s-draft-25-february-2013"><abbr title="World Wide Web Consortium">W3C</abbr> Editor's Draft 25 February 2013</h2>
-  <dl>
-    
-      <dt>This version:</dt>
-      <dd><a href="http://dvcs.w3.org/hg/gld/raw-file/default/data-cube-ucr/index.html">http://dvcs.w3.org/hg/gld/raw-file/default/data-cube-ucr/index.html</a></dd>
-      <dt>Latest published version:</dt>
-      <dd><a href="http://www.w3.org/TR/data-cube-ucr/">http://www.w3.org/TR/data-cube-ucr/</a></dd>
-    
-    
-      <dt>Latest editor's draft:</dt>
-      <dd><a href="http://dvcs.w3.org/hg/gld/raw-file/default/data-cube-ucr/index.html">http://dvcs.w3.org/hg/gld/raw-file/default/data-cube-ucr/index.html</a></dd>
-    
-    
-    
-    
-      
-    
-    
-    
-    <dt>Editors:</dt>
-    <dd><a href="http://www.aifb.kit.edu/web/Benedikt_K%C3%A4mpgen/en">Benedikt Kämpgen</a>, <a href="http://www.fzi.de/index.php/en">FZI Karlsruhe</a></dd>
-<dd><a href="http://richard.cyganiak.de/">Richard Cyganiak</a>, <a href="http://www.deri.ie/">DERI, NUI Galway</a></dd>
-
-    
-  </dl>
-  
-  
-  
-  
-    
-      <p class="copyright">
-        <a href="http://www.w3.org/Consortium/Legal/ipr-notice#Copyright">Copyright</a> © 
-        2013
-        
-        <a href="http://www.w3.org/"><abbr title="World Wide Web Consortium">W3C</abbr></a><sup>®</sup> 
-        (<a href="http://www.csail.mit.edu/"><abbr title="Massachusetts Institute of Technology">MIT</abbr></a>,
-        <a href="http://www.ercim.eu/"><abbr title="European Research Consortium for Informatics and Mathematics">ERCIM</abbr></a>,
-        <a href="http://www.keio.ac.jp/">Keio</a>), All Rights Reserved.
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-        <a href="http://www.w3.org/Consortium/Legal/copyright-documents">document use</a> rules apply.
-      </p>
-    
-  
-  <hr>
-</div>
-
-	<section id="abstract" class="introductory"><h2>Abstract</h2>
-	<p>Many national, regional and local governments, as well as other
-		organizations inside and outside of the public sector, create
-		statistics. There is a need to publish those statistics in a
-		standardized, machine-readable way on the web, so that statistics can
-		be freely integrated and reused in consuming applications. This
-		document is a collection of use cases for a standard vocabulary to
-		publish statistics as Linked Data.</p>
-	</section><section id="sotd" class="introductory"><h2>Status of This Document</h2>
-  
-    
-      
-        <p>
-          <em>This section describes the status of this document at the time of its publication. Other
-          documents may supersede this document. A list of current <abbr title="World Wide Web Consortium">W3C</abbr> publications and the latest revision
-          of this technical report can be found in the <a href="http://www.w3.org/TR/"><abbr title="World Wide Web Consortium">W3C</abbr> technical reports
-          index</a> at http://www.w3.org/TR/.</em>
-        </p>
-        
-	<p>
-		This is a working document of the <a href="http://www.w3.org/2011/gld/wiki/Data_Cube_Vocabulary">Data
-			Cube Vocabulary project</a> within the <a href="http://www.w3.org/2011/gld/"><abbr title="World Wide Web Consortium">W3C</abbr> Government Linked Data
-			Working Group</a>. Feedback is welcome and should be sent to the <a href="mailto:[email protected]">[email protected]
-			mailing list</a>.
-	</p>
-	
-        <p>
-          This document was published by the <a href="http://www.w3.org/2011/gld/">Government Linked Data Working Group</a> as an Editor's Draft.
-          
-          
-          If you wish to make comments regarding this document, please send them to 
-          <a href="mailto:[email protected]">[email protected]</a> 
-          (<a href="mailto:[email protected]?subject=subscribe">subscribe</a>,
-          <a href="http://lists.w3.org/Archives/Public/public-gld-comments/">archives</a>).
-          
-          
-          
-          
-        All comments are welcome.
-        
-        
-          </p><p>
-            Publication as an Editor's Draft does not imply endorsement by the <abbr title="World Wide Web Consortium">W3C</abbr> Membership.
-            This is a draft document and may be updated, replaced or obsoleted by other documents at 
-            any time. It is inappropriate to cite this document as other than work in progress.
-          </p>
-        
-        
-        <p>
-          
-            This document was produced by a group operating under the 
-            <a href="http://www.w3.org/Consortium/Patent-Policy-20040205/">5 February 2004 <abbr title="World Wide Web Consortium">W3C</abbr> Patent Policy</a>.
-          
-          
-          
-            
-              <abbr title="World Wide Web Consortium">W3C</abbr> maintains a <a href="" rel="disclosure">public list of any patent disclosures</a> 
-            
-            made in connection with the deliverables of the group; that page also includes instructions for 
-            disclosing a patent. An individual who has actual knowledge of a patent which the individual believes contains
-            <a href="http://www.w3.org/Consortium/Patent-Policy-20040205/#def-essential">Essential Claim(s)</a> must disclose the
-            information in accordance with <a href="http://www.w3.org/Consortium/Patent-Policy-20040205/#sec-Disclosure">section
-            6 of the <abbr title="World Wide Web Consortium">W3C</abbr> Patent Policy</a>.
-          
-          
-        </p>
-        
-      
-    
-  
-</section><section id="toc"><h2 class="introductory">Table of Contents</h2><ul class="toc"><li class="tocline"><a href="#introduction" class="tocxref"><span class="secno">1. </span>Introduction</a></li><li class="tocline"><a href="#terminology" class="tocxref"><span class="secno">2. </span>Terminology</a></li><li class="tocline"><a href="#use-cases" class="tocxref"><span class="secno">3. </span>Use cases</a><ul class="toc"><li class="tocline"><a href="#publishing-statistical-data" class="tocxref"><span class="secno">3.1 </span>Publishing statistical data</a><ul class="toc"><li class="tocline"><a href="#publishing-general-statistics-in-a-machine-readable-and-application-independent-way-uc-1" class="tocxref"><span class="secno">3.1.1 </span>Publishing general statistics in a machine-readable and
-		application-independent way (UC 1)</a></li><li class="tocline"><a href="#publishing-one-or-many-ms-excel-spreadsheet-files-with-statistical-data-on-the-web-uc-2" class="tocxref"><span class="secno">3.1.2 </span>Publishing one or many MS excel spreadsheet files with
-		statistical data on the web (UC 2)</a></li><li class="tocline"><a href="#publishing-sdmx-as-linked-data-uc-3" class="tocxref"><span class="secno">3.1.3 </span>Publishing SDMX as Linked Data (UC 3)</a></li><li class="tocline"><a href="#publishing-sensor-data-as-statistics-uc-4" class="tocxref"><span class="secno">3.1.4 </span>Publishing sensor data as statistics (UC 4)</a></li><li class="tocline"><a href="#registering-statistical-data-in-dataset-catalogs-uc-5" class="tocxref"><span class="secno">3.1.5 </span>Registering statistical data in dataset catalogs (UC 5)</a></li><li class="tocline"><a href="#making-transparent-transformations-on-or-different-versions-of-statistical-data-uc-6" class="tocxref"><span class="secno">3.1.6 </span>Making transparent transformations on or different versions of
-		statistical data (UC 6)</a></li></ul></li><li class="tocline"><a href="#consuming-published-statistical-data" class="tocxref"><span class="secno">3.2 </span>Consuming published statistical data</a><ul class="toc"><li class="tocline"><a href="#simple-chart-visualizations-of-integrated-published-statistical-datasets-uc-7" class="tocxref"><span class="secno">3.2.1 </span>Simple chart visualizations of (integrated) published
-		statistical datasets (UC 7)</a></li><li class="tocline"><a href="#uploading-published-statistical-data-in-google-public-data-explorer-uc-8" class="tocxref"><span class="secno">3.2.2 </span>Uploading published statistical data in Google Public Data
-		Explorer (UC 8)</a></li><li class="tocline"><a href="#allow-online-analytical-processing-on-published-datasets-of-statistical-data-uc-9" class="tocxref"><span class="secno">3.2.3 </span>Allow Online Analytical Processing on published datasets of
-		statistical data (UC 9)</a></li><li class="tocline"><a href="#transforming-published-statistics-into-xbrl-uc-10" class="tocxref"><span class="secno">3.2.4 </span>Transforming published statistics into XBRL (UC 10)</a></li></ul></li></ul></li><li class="tocline"><a href="#requirements" class="tocxref"><span class="secno">4. </span>Requirements</a><ul class="toc"><li class="tocline"><a href="#publishing-requirements" class="tocxref"><span class="secno">4.1 </span>Publishing requirements</a><ul class="toc"><li class="tocline"><a href="#machine-readable-and-application-independent-representation-of-statistics" class="tocxref"><span class="secno">4.1.1 </span>Machine-readable and application-independent representation of
-		statistics</a></li><li class="tocline"><a href="#representing-statistics-from-various-resource" class="tocxref"><span class="secno">4.1.2 </span>Representing statistics from various resource</a></li><li class="tocline"><a href="#communicating-exposing-statistics-on-the-web" class="tocxref"><span class="secno">4.1.3 </span>Communicating, exposing statistics on the web</a></li><li class="tocline"><a href="#coverage-of-typical-statistics-metadata" class="tocxref"><span class="secno">4.1.4 </span>Coverage of typical statistics metadata</a></li><li class="tocline"><a href="#expressing-hierarchies" class="tocxref"><span class="secno">4.1.5 </span>Expressing hierarchies</a></li><li class="tocline"><a href="#machine-readable-and-application-independent-representation-of-statistics-1" class="tocxref"><span class="secno">4.1.6 </span>Machine-readable and application-independent representation of
-		statistics</a></li><li class="tocline"><a href="#expressing-aggregation-relationships-in-data-cube" class="tocxref"><span class="secno">4.1.7 </span>Expressing aggregation relationships in Data Cube</a></li><li class="tocline"><a href="#scale---how-to-publish-large-amounts-of-statistical-data" class="tocxref"><span class="secno">4.1.8 </span>Scale - how to publish large amounts of statistical data</a></li><li class="tocline"><a href="#compliance-levels-or-criteria-for-well-formedness" class="tocxref"><span class="secno">4.1.9 </span>Compliance-levels or criteria for well-formedness</a></li><li class="tocline"><a href="#declaring-relations-between-cubes" class="tocxref"><span class="secno">4.1.10 </span>Declaring relations between Cubes</a></li></ul></li><li class="tocline"><a href="#consumption-requirements" class="tocxref"><span class="secno">4.2 </span>Consumption requirements</a><ul class="toc"><li class="tocline"><a href="#finding-statistical-data" class="tocxref"><span class="secno">4.2.1 </span>Finding statistical data</a></li><li class="tocline"><a href="#retrival-of-fine-grained-statistics" class="tocxref"><span class="secno">4.2.2 </span>Retrival of fine grained statistics</a></li><li class="tocline"><a href="#understanding---end-user-consumption-of-statistical-data" class="tocxref"><span class="secno">4.2.3 </span>Understanding - End user consumption of statistical data</a></li><li class="tocline"><a href="#comparing-and-trusting-statistics" class="tocxref"><span class="secno">4.2.4 </span>Comparing and trusting statistics</a></li><li class="tocline"><a href="#integration-of-statistics" class="tocxref"><span class="secno">4.2.5 </span>Integration of statistics</a></li><li class="tocline"><a href="#scale---how-to-consume-large-amounts-of-statistical-data" class="tocxref"><span class="secno">4.2.6 </span>Scale - how to consume large amounts of statistical data</a></li><li class="tocline"><a href="#common-internal-representation-of-statistics-to-be-exported-in-other-formats" class="tocxref"><span class="secno">4.2.7 </span>Common internal representation of statistics, to be exported
-		in other formats</a></li><li class="tocline"><a href="#dealing-with-imperfect-statistics" class="tocxref"><span class="secno">4.2.8 </span>Dealing with imperfect statistics</a></li></ul></li></ul></li><li class="tocline"><a href="#acknowledgments" class="tocxref"><span class="secno">A. </span>Acknowledgments</a></li></ul></section>
-
-	
-
-	<section id="introduction">
-	<!--OddPage--><h2><span class="secno">1. </span>Introduction</h2>
-
-	<p>Many national, regional and local governments, as well as other
-		organizations inside and outside of the public sector, create
-		statistics. There is a need to publish those statistics in a
-		standardized, machine-readable way on the web, so that statistics can
-		be freely linked, integrated and reused in consuming applications.
-		This document is a collection of use cases for a standard vocabulary
-		to publish statistics as Linked Data.</p>
-	</section>
-
-
-	<section id="terminology">
-	<!--OddPage--><h2><span class="secno">2. </span>Terminology</h2>
-	<p>
-		<dfn id="dfn-statistics">Statistics</dfn>
-		is the <a href="http://en.wikipedia.org/wiki/Statistics">study</a> of
-		the collection, organization, analysis, and interpretation of data. A
-		statistic is a statistical dataset.
-	</p>
-
-	<p>
-		A
-		<dfn id="dfn-statistical-dataset">statistical dataset</dfn>
-		comprises multidimensional data - a set of observed values organized
-		along a group of dimensions, together with associated metadata. Basic
-		structure of (aggregated) statistical data is a multidimensional table
-		(also called a cube) <a href="#ref-SDMX">[SDMX]</a>.
-	</p>
-
-	<p>
-		<dfn id="dfn-source-data">Source data</dfn>
-		is data from datastores such as RDBs or spreadsheets that acts as a
-		source for the Linked Data publishing process.
-	</p>
-
-	<p>
-		<dfn id="dfn-metadata">Metadata</dfn>
-		about statistics defines the data structure and give contextual
-		information about the statistics.
-	</p>
-
-	<p>
-		A format is
-		<dfn id="dfn-machine-readable">machine-readable</dfn>
-		if it is amenable to automated processing by a machine, as opposed to
-		presentation to a human user.
-	</p>
-
-	<p>
-		A
-		<dfn id="dfn-publisher">publisher</dfn>
-		is a person or organization that exposes source data as Linked Data on
-		the Web.
-	</p>
-
-	<p>
-		A
-		<dfn id="dfn-consumer">consumer</dfn>
-		is a person or agent that uses Linked Data from the Web.
-	</p>
-
-	</section>
-
-
-	<section id="use-cases">
-	<!--OddPage--><h2><span class="secno">3. </span>Use cases</h2>
-	<p>
-		This section presents scenarios that would be enabled by the existence
-		of a standard vocabulary for the representation of statistics as
-		Linked Data. Since a draft of the specification of the cube vocabulary
-		has been published, and the vocabulary already is in use, we will call
-		this standard vocabulary after its current name RDF Data Cube
-		vocabulary (short <a href="#ref-QB">[QB]</a>) throughout the document.
-	</p>
-	<p>We distinguish between use cases of publishing statistical data,
-		and use cases of consuming statistical data since requirements for
-		publishers and consumers of statistical data differ.</p>
-	<section id="publishing-statistical-data">
-	<h3><span class="secno">3.1 </span>Publishing statistical data</h3>
-
-	<section id="publishing-general-statistics-in-a-machine-readable-and-application-independent-way-uc-1">
-	<h4><span class="secno">3.1.1 </span>Publishing general statistics in a machine-readable and
-		application-independent way (UC 1)</h4>
-	<p>More and more organizations want to publish statistics on the
-		web, for reasons such as increasing transparency and trust. Although
-		in the ideal case, published data can be understood by both humans and
-		machines, data often is simply published as CSV, PDF, XSL etc.,
-		lacking elaborate metadata, which makes free usage and analysis
-		difficult.</p>
-
-	<p>The goal in this use case is to use a machine-readable and
-		application-independent description of common statistics with use of
-		open standards. The use case is fulfilled if QB will be a Linked Data
-		vocabulary for encoding statistical data that has a hypercube
-		structure and as such can describe common statistics in a
-		machine-readable and application-independent way.</p>
-
-	<p>
-		An example scenario of this use case has been to publish the Combined
-		Online Information System (<a href="http://data.gov.uk/resources/coins">COINS</a>). There, HM
-		Treasury, the principal custodian of financial data for the UK
-		government, released previously restricted information from its
-		Combined Online Information System (COINS). Five data files were
-		released containing between 3.3 and 4.9 million rows of data. The
-		COINS dataset was translated into RDF for two reasons:
-	</p>
-
-	<ol>
-		<li>To publish statistics (e.g., as data files) are too large to
-			load into widely available analysis tools such as Microsoft Excel, a
-			common tool-of-choice for many data investigators.</li>
-		<li>COINS is a highly technical information source, requiring
-			both domain and technical skills to make useful applications around
-			the data.</li>
-	</ol>
-	<p>Publishing statistics is challenging for the several reasons:</p>
-	<p>
-		Representing observations and measurements requires more complex
-		modeling as discussed by Martin Fowler <a href="#Fowler1997">[Fowler,
-			1997]</a>: Recording a statistic simply as an attribute to an object
-		(e.g., a the fact that a person weighs 185 pounds) fails with
-		representing important concepts such as quantity, measurement, and
-		observation.
-	</p>
-
-	<p>Quantity comprises necessary information to interpret the value,
-		e.g., the unit and arithmetical and comparative operations; humans and
-		machines can appropriately visualize such quantities or have
-		conversions between different quantities.</p>
-
-	<p>A Measurement separates a quantity from the actual event at
-		which it was collected; a measurement assigns a quantity to a specific
-		phenomenon type (e.g., strength). Also, a measurement can record
-		metadata such as who did the measurement (person), and when was it
-		done (time).</p>
-
-	<p>Observations, eventually, abstract from measurements only
-		recording numeric quantities. An Observation can also assign a
-		category observation (e.g., blood group A) to an observation. Figure
-		demonstrates this relationship.</p>
-	<p>
-	</p><div class="fig">
-		<a href="figures/modeling_quantity_measurement_observation.png"><img src="figures/modeling_quantity_measurement_observation.png" alt="Modeling quantity, measurement, observation"> </a>
-		<div>Modeling quantity, measurement, observation</div>
-	</div>
-	
-	<p></p>
-
-	<p>QB deploys the multidimensional model (made of observations with
-		Measures depending on Dimensions and Dimension Members, and further
-		contextualized by Attributes) and should cater for these complexity in
-		modelling.</p>
-	<p>Another challenge is that for brevity reasons and to avoid
-		repetition, it is useful to have abbreviation mechanisms such as
-		assigning overall valid properties of observations at the dataset or
-		slice level, and become implicitly part of each observation. For
-		instance, in the case of COINS, all of the values are in thousands of
-		pounds sterling. However, one of the use cases for the linked data
-		version of COINS is to allow others to link to individual
-		observations, which suggests that these observations should be
-		standalone and self-contained – and should therefore have explicit
-		multipliers and units on each observation. One suggestion is to author
-		data without the duplication, but have the data publication tools
-		"flatten" the compact representation into standalone observations
-		during the publication process.</p>
-	<p>A further challenge is related to slices of data. Slices of data
-		group observations that are of special interest, e.g., slices
-		unemployment rates per year of a specific gender are suitable for
-		direct visualization in a line diagram. However, depending on the
-		number of Dimensions, the number of possible slices can become large
-		which makes it difficult to select all interesting slices. Therefore,
-		and because of their additional complexity, not many publishers create
-		slices. In fact, it is somewhat unclear at this point which slices
-		through the data will be useful to (COINS-RDF) users.</p>
-	<p>Unanticipated Uses (optional): -</p>
-	<p>Existing Work (optional): -</p>
-
-	</section> <section id="publishing-one-or-many-ms-excel-spreadsheet-files-with-statistical-data-on-the-web-uc-2">
-	<h4><span class="secno">3.1.2 </span>Publishing one or many MS excel spreadsheet files with
-		statistical data on the web (UC 2)</h4>
-	<p>Not only in government, there is a need to publish considerable
-		amounts of statistical data to be consumed in various (also
-		unexpected) application scenarios. Typically, Microsoft Excel sheets
-		are made available for download. Those excel sheets contain single
-		spreadsheets with several multidimensional data tables, having a name
-		and notes, as well as column values, row values, and cell values.</p>
-	<p>The goal in this use case is to to publish spreadsheet
-		information in a machine-readable format on the web, e.g., so that
-		crawlers can find spreadsheets that use a certain column value. The
-		published data should represent and make available for queries the
-		most important information in the spreadsheets, e.g., rows, columns,
-		and cell values. QB should provide the level of detail that is needed
-		for such a transformation in order to fulfil this use case.</p>
-	<p>In a possible use case scenario an institution wants to develop
-		or use a software that transforms their excel sheets into the
-		appropriate format.</p>
-
-	<p class="editorsnote">@@TODO: Concrete example needed.</p>
-	<p>Challenges of this use case are:</p>
-	<ul>
-		<li>Excel sheets provide much flexibility in arranging
-			information. It may be necessary to limit this flexibility to allow
-			automatic transformation.</li>
-		<li>There may be many spreadsheets.</li>
-		<li>Semi-structured information, e.g., notes about lineage of
-			data cells, may not be possible to be formalized.</li>
-	</ul>
-	<p>Unanticipated Uses (optional): -</p>
-	<p>
-		Existing Work (optional): Stats2RDF uses OntoWiki to translate CSV
-		into QB <a href="http://aksw.org/Projects/Stats2RDF">[Stats2RDF]</a>.
-	</p>
-
-	</section> <section id="publishing-sdmx-as-linked-data-uc-3">
-	<h4><span class="secno">3.1.3 </span>Publishing SDMX as Linked Data (UC 3)</h4>
-	<p>The ISO standard for exchanging and sharing statistical data and
-		metadata among organizations is Statistical Data and Metadata eXchange
-		(SDMX). Since this standard has proven applicable in many contexts, QB
-		is designed to be compatible with the multidimensional model that
-		underlies SDMX.</p>
-	<p class="editorsnote">@@TODO: The QB spec should maybe also use
-		the term "multidimensional model" instead of the less clear "cube
-		model" term.</p>
-	<p>Therefore, it should be possible to re-publish SDMX data using
-		QB.</p>
-	<p>
-		The scenario for this use case is Eurostat <a href="http://epp.eurostat.ec.europa.eu/">[EUROSTAT]</a>, which
-		publishes large amounts of European statistics coming from a data
-		warehouse as SDMX and other formats on the web. Eurostat also provides
-		an interface to browse and explore the datasets. However, linking such
-		multidimensional data to related data sets and concepts would require
-		download of interesting datasets and manual integration.
-	</p>
-	<p>The goal of this use case is to improve integration with other
-		datasets; Eurostat data should be published on the web in a
-		machine-readable format, possible to be linked with other datasets,
-		and possible to be freeley consumed by applications. This use case is
-		fulfilled if QB can be used for publishing the data from Eurostat as
-		Linked Data for integration.</p>
-	<p>A publisher wants to make available Eurostat data as Linked
-		Data. The statistical data shall be published as is. It is not
-		necessary to represent information for validation. Data is read from
-		tsv only. There are two concrete examples of this use case: Eurostat
-		Linked Data Wrapper (http://estatwrap.ontologycentral.com/), and
-		Linked Statistics Eurostat Data
-		(http://eurostat.linked-statistics.org/). They have slightly different
-		focus (e.g., with respect to completeness, performance, and agility).
-	</p>
-	<p>Challenges of this use case are:</p>
-	<ul>
-		<li>There are large amounts of SDMX data; the Eurostat dataset
-			comprises 350 GB of data. This may influence decisions about toolsets
-			and architectures to use. One important task is to decide whether to
-			structure the data in separate datasets.</li>
-		<li>Again, the question comes up whether slices are useful.</li>
-	</ul>
-	<p>Unanticipated Uses (optional): -</p>
-	<p>Existing Work (optional): -</p>
-	</section> <section id="publishing-sensor-data-as-statistics-uc-4">
-	<h4><span class="secno">3.1.4 </span>Publishing sensor data as statistics (UC 4)</h4>
-	<p>Typically, multidimensional data is aggregated. However, there
-		are cases where non-aggregated data needs to be published, e.g.,
-		observational, sensor network and forecast data sets. Such raw data
-		may be available in RDF, already, but using a different vocabulary.</p>
-	<p>The goal of this use case is to demonstrate that publishing of
-		aggregate values or of raw data should not make much of a difference
-		in QB.</p>
-	<p>
-		For example the Environment Agency uses it to publish (at least
-		weekly) information on the quality of bathing waters around England
-		and Wales <a href="http://www.epimorphics.com/web/wiki/bathing-water-quality-structure-published-linked-data">[EnvAge]</a>.
-		In another scenario DERI tracks from measurements about printing for a
-		sustainability report. In the DERI scenario, raw data (number of
-		printouts per person) is collected, then aggregated on a unit level,
-		and then modelled using QB.
-	</p>
-	<p>Problems and Limitations:</p>
-	<ul>
-		<li>This use case also shall demonstrate how to link statistics
-			with other statistics or non-statistical data (metadata).</li>
-	</ul>
-	<p>Unanticipated Uses (optional): -</p>
-	<p>
-		Existing Work (optional): Semantic Sensor Network ontology <a href="http://purl.oclc.org/NET/ssnx/ssn">[SSN]</a> already provides a
-		way to publish sensor information. SSN data provides statistical
-		Linked Data and grounds its data to the domain, e.g., sensors that
-		collect observations (e.g., sensors measuring average of temperature
-		over location and time). A number of organizations, particularly in
-		the Climate and Meteorological area already have some commitment to
-		the OGC "Observations and Measurements" (O&amp;M) logical data model, also
-		published as ISO 19156. The QB spec should maybe also prefer the term
-		"multidimensional model" instead of the less clear "cube model" term.
-
-
-	
-	</p><p class="editorsnote">@@TODO: Are there any statements about
-		compatibility and interoperability between O&amp;M and Data Cube that can
-		be made to give guidance to such organizations?</p>
-	<p></p>
-	</section> <section id="registering-statistical-data-in-dataset-catalogs-uc-5">
-	<h4><span class="secno">3.1.5 </span>Registering statistical data in dataset catalogs (UC 5)</h4>
-	<p>
-		After statistics have been published as Linked Data, the question
-		remains how to communicate the publication and let users find the
-		statistics. There are catalogs to register datasets, e.g., CKAN, <a href="http://www.datacite.org/datacite.org">datacite.org</a>, <a href="http://www.gesis.org/dara/en/home/?lang=en">da|ra</a>, and <a href="http://pangaea.de/">Pangea</a>. Those catalogs require specific
-		configurations to register statistical data.
-	</p>
-	<p>The goal of this use case is to demonstrate how to expose and
-		distribute statistics after modeling using QB. For instance, to allow
-		automatic registration of statistical data in such catalogs, for
-		finding and evaluating datasets. To solve this issue, it should be
-		possible to transform QB data into formats that can be used by data
-		catalogs.</p>
-
-	<p class="editorsnote">@@TODO: Find specific use case scenario or
-		ask how other publishers of QB data have dealt with this issue Maybe
-		relation to DCAT?</p>
-	<p>Problems and Limitations: -</p>
-	<p>Unanticipated Uses (optional): If data catalogs contain
-		statistics, they do not expose those using Linked Data but for
-		instance using CSV or HTML (Pangea [11]). It could also be a use case
-		to publish such data using QB.</p>
-	<p>Existing Work (optional): -</p>
-	</section> <section id="making-transparent-transformations-on-or-different-versions-of-statistical-data-uc-6">
-	<h4><span class="secno">3.1.6 </span>Making transparent transformations on or different versions of
-		statistical data (UC 6)</h4>
-	<p>Statistical data often is used and further transformed for
-		analysis and reporting. There is the risk that data has been
-		incorrectly transformed so that the result is not interpretable any
-		more. Therefore, if statistical data has been derived from other
-		statistical data, this should be made transparent.</p>
-	<p>The goal of this use case is to describe provenance and
-		versioning around statistical data, so that the history of statistics
-		published on the web becomes clear. This may also relate to the issue
-		of having relationships between datasets published using QB. To fulfil
-		this use case QB should recommend specific approaches to transforming
-		and deriving of datasets which can be tracked and stored with the
-		statistical data.</p>
-
-	<p>A simple specific use case is that the Welsh Assembly government
-		publishes a variety of population datasets broken down in different
-		ways. For many uses then population broken down by some category (e.g.
-		ethnicity) is expressed as a percentage. Separate datasets give the
-		actual counts per category and aggregate counts. In such cases it is
-		common to talk about the denominator (often DENOM) which is the
-		aggregate count against which the percentages can be interpreted.</p>
-	<p>Challenges of this use case are:</p>
-	<ul>
-		<li>Operations on statistical data result in new statistical
-			data, depending on the operation. For intance, in terms of Data Cube,
-			operations such as slice, dice, roll-up, drill-down will result in
-			new Data Cubes. This may require representing general relationships
-			between cubes (as discussed here: [12]).</li>
-		<li>Should Data Cube support explicit declaration of such
-			relationships either between separated qb:DataSets or between
-			measures with a single qb:DataSet (e.g. ex:populationCount and
-			ex:populationPercent)?</li>
-		<li>If so should that be scoped to simple, common relationships
-			like DENOM or allow expression of arbitrary mathematical relations?</li>
-	</ul>
-	<p>Unanticipated Uses (optional): -</p>
-	<p>Existing Work (optional): Possible relation to Best Practices
-		part on Versioning [13], where it is specified how to publish data
-		which has multiple versions.</p>
-
-
-	</section></section> <section id="consuming-published-statistical-data">
-	<h3><span class="secno">3.2 </span>Consuming published statistical data</h3>
-
-	<section id="simple-chart-visualizations-of-integrated-published-statistical-datasets-uc-7">
-	<h4><span class="secno">3.2.1 </span>Simple chart visualizations of (integrated) published
-		statistical datasets (UC 7)</h4>
-	<p>Data that is published on the Web is typically visualized by
-		transforming it manually into CSV or Excel and then creating a
-		visualization on top of these formats using Excel, Tableau,
-		RapidMiner, Rattle, Weka etc.</p>
-	<p>This use case shall demonstrate how statistical data published
-		on the web can be directly visualized, without using commercial or
-		highly-complex tools. This use case is fulfilled if data that is
-		published in QB can be directly visualized inside a webpage.</p>
-	<p>An example scenario is environmental research done within the
-		SMART research project (http://www.iwrm-smart.org/). Here, statistics
-		about environmental aspects (e.g., measurements about the climate in
-		the Lower Jordan Valley) shall be visualized for scientists and
-		decision makers. Statistics should also be possible to be integrated
-		and displayed together. The data is available as XML files on the web.
-		On a separate website, specific parts of the data shall be queried and
-		visualized in simple charts, e.g., line diagrams. The following figure
-		shows the wanted display of an environmental measure over time for
-		three regions in the lower Jordan valley; displayed inside a web page:</p>
-
-	<p>
-	</p><div class="fig">
-		<a href="figures/Level_above_msl_3_locations.png"><img width="800px" src="figures/Level_above_msl_3_locations.png" alt="Line chart visualization of QB data"> </a>
-		<div>Line chart visualization of QB data</div>
-	</div>
-	
-	<p></p>
-
-	<p>The following figure shows the same measures in a pivot table.
-		Here, the aggregate COUNT of measures per cell is given.</p>
-
-	<p>
-	</p><div class="fig">
-		<a href="figures/pivot_analysis_measurements.PNG"><img src="figures/pivot_analysis_measurements.PNG" alt="Pivot analysis measurements"> </a>
-		<div>Pivot analysis measurements</div>
-	</div>
-	
-	<p></p>
-
-	<p>The use case uses Google App Engine, Qcrumb.com, and Spark. An
-		example of a line diagram is given at [14] (some loading time needed).
-		Current work tries to integrate current datasets with additional data
-		sources, and then having queries that take data from both datasets and
-		display them together.</p>
-	<p>Challenges of this use case are:</p>
-	<ul>
-		<li>The difficulties lay in structuring the data appropriately so
-			that the specific information can be queried.</li>
-		<li>Also, data shall be published with having potential
-			integration in mind. Therefore, e.g., units of measurements need to
-			be represented.</li>
-		<li>Integration becomes much more difficult if publishers use
-			different measures, dimensions.</li>
-
-	</ul>
-	<p>Unanticipated Uses (optional): -</p>
-	<p>Existing Work (optional): -</p>
-	</section> <section id="uploading-published-statistical-data-in-google-public-data-explorer-uc-8">
-	<h4><span class="secno">3.2.2 </span>Uploading published statistical data in Google Public Data
-		Explorer (UC 8)</h4>
-	<p>Google Public Data Explorer (GPDE -
-		http://code.google.com/apis/publicdata/) provides an easy possibility
-		to visualize and explore statistical data. Data needs to be in the
-		Dataset Publishing Language (DSPL -
-		https://developers.google.com/public-data/overview) to be uploaded to
-		the data explorer. A DSPL dataset is a bundle that contains an XML
-		file, the schema, and a set of CSV files, the actual data. Google
-		provides a tutorial to create a DSPL dataset from your data, e.g., in
-		CSV. This requires a good understanding of XML, as well as a good
-		understanding of the data that shall be visualized and explored.</p>
-	<p>In this use case, it shall be demonstrate how to take any
-		published QB dataset and to transform it automatically into DSPL for
-		visualization and exploration. A dataset that is published conforming
-		to QB will provide the level of detail that is needed for such a
-		transformation.</p>
-	<p>In an example scenario, a publisher P has published data using
-		QB. There are two different ways to fulfil this use case: 1) A
-		customer C is downloading this data into a triple store; SPARQL
-		queries on this data can be used to transform the data into DSPL and
-		uploaded and visualized using GPDE. 2) or, one or more XLST
-		transformation on the RDF/XML transforms the data into DSPL.</p>
-	<p>Challenges of this use case are:</p>
-	<ul>
-		<li>The technical challenges for the consumer here lay in knowing
-			where to download what data and how to get it transformed into DSPL
-			without knowing the data.</li>
-		<p>Unanticipated Uses (optional): DSPL is representative for using
-			statistical data published on the web in available tools for
-			analysis. Similar tools that may be automatically covered are: Weka
-			(arff data format), Tableau, etc.</p>
-		<p>Existing Work (optional): -</p>
-	</ul>
-	<p>Unanticipated Uses (optional): -</p>
-	<p>Existing Work (optional): -</p>
-	</section> <section id="allow-online-analytical-processing-on-published-datasets-of-statistical-data-uc-9">
-	<h4><span class="secno">3.2.3 </span>Allow Online Analytical Processing on published datasets of
-		statistical data (UC 9)</h4>
-	<p>Online Analytical Processing [15] is an analysis method on
-		multidimensional data. It is an explorative analysis methode that
-		allows users to interactively view the data on different angles
-		(rotate, select) or granularities (drill-down, roll-up), and filter it
-		for specific information (slice, dice).</p>
-	<p>The multidimensional model used in QB to model statistics should
-		be usable by OLAP systems. More specifically, data that conforms to QB
-		can be used to define a Data Cube within an OLAP engine and can then
-		be queries by OLAP clients.</p>
-	<p>An example scenario of this use case is the Financial
-		Information Observation System (FIOS) [16], where XBRL data has been
-		re-published using QB and made analysable for stakeholders in a
-		web-based OLAP client. The following figure shows an example of using
-		FIOS. Here, for three different companies, cost of goods sold as
-		disclosed in XBRL documents are analysed. As cell values either the
-		number of disclosures or - if only one available - the actual number
-		in USD is given:</p>
-
-	<p>
-	</p><div class="fig">
-		<a href="figures/FIOS_example.PNG"><img src="figures/FIOS_example.PNG" alt="OLAP of QB data"> </a>
-		<div>OLAP of QB data</div>
-	</div>
-	
-	<p></p>
-	<p>Challenges of this use case are:</p>
-	<ul>
-		<li>A problem lies in the strict separation between queries for
-			the structure of data, and queries for actual aggregated values.</li>
-		<li>Another problem lies in defining Data Cubes without greater
-			insight in the data beforehand.</li>
-		<li>Depending on the expressivity of the OLAP queries (e.g.,
-			aggregation functions, hierarchies, ordering), performance plays an
-			important role.</li>
-		<li>QB allows flexibility in describing statistics, e.g., in
-			order to reduce redundancy of information in single observations.
-			These alternatives make general consumption of QB data more complex.
-			Also, it is not clear, what "conforms" to QB means, e.g., is a
-			qb:DataStructureDefinition required?</li>
-		<p>Unanticipated Uses (optional): -</p>
-		<p>Existing Work (optional): -</p>
-	</ul>
-	<p>Unanticipated Uses (optional): -</p>
-	<p>Existing Work (optional): -</p>
-	</section> <section id="transforming-published-statistics-into-xbrl-uc-10">
-	<h4><span class="secno">3.2.4 </span>Transforming published statistics into XBRL (UC 10)</h4>
-	<p>XBRL is a standard data format for disclosing financial
-		information. Typically, financial data is not managed within the
-		organization using XBRL but instead, internal formats such as excel or
-		relational databases are used. If different data sources are to be
-		summarized in XBRL data formats to be published, an internally-used
-		standard format such as QB could help integrate and transform the data
-		into the appropriate format.</p>
-	<p>In this use case data that is available as data conforming to QB
-		should also be possible to be automatically transformed into such XBRL
-		data format. This use case is fulfilled if QB contains necessary
-		information to derive XBRL data.</p>
-	<p>In an example scenario, DERI has had a use case to publish
-		sustainable IT information as XBRL to the Global Reporting Initiative
-		(GRI - https://www.globalreporting.org/). Here, raw data (number of
-		printouts per person) is collected, then aggregated on a unit level
-		and modelled using QB. QB data shall then be used directly to fill-in
-		XBRL documents that can be published to the GRI.</p>
-	<p>Challenges of this use case are:</p>
-	<ul>
-		<li>So far, QB data has been transformed into semantic XBRL, a
-			vocabulary closer to XBRL. There is the chance that certain
-			information required in a GRI XBRL document cannot be encoded using a
-			vocabulary as general as QB. In this case, QB could be used in
-			concordance with semantic XBRL.</li>
-	</ul>
-	<p class="editorsnote">@@TODO: Add link to semantic XBRL.</p>
-	<p>Unanticipated Uses (optional): -</p>
-	<p>Existing Work (optional): -</p>
-
-	</section> </section></section>
-	<section id="requirements">
-	<!--OddPage--><h2><span class="secno">4. </span>Requirements</h2>
-
-	<p>The use cases presented in the previous section give rise to the
-		following requirements for a standard representation of statistics.
-		Requirements are cross-linked with the use cases that motivate them.
-		Requirements are similarly categorized as deriving from publishing or
-		consuming use cases.</p>
-
-	<section id="publishing-requirements">
-	<h3><span class="secno">4.1 </span>Publishing requirements</h3>
-
-	<section id="machine-readable-and-application-independent-representation-of-statistics">
-	<h4><span class="secno">4.1.1 </span>Machine-readable and application-independent representation of
-		statistics</h4>
-	<p>It should be possible to add abstraction, multiple levels of
-		description, summaries of statistics.</p>
-
-	<p>Required by: UC1, UC2, UC3, UC4</p>
-	</section> <section id="representing-statistics-from-various-resource">
-	<h4><span class="secno">4.1.2 </span>Representing statistics from various resource</h4>
-	<p>Statistics from various resource data should be possible to be
-		translated into QB. QB should be very general and should be usable for
-		other data sets such as survey data, spreadsheets and OLAP data cubes.
-		What kind of statistics are described: simple CSV tables (UC 1), excel
-		(UC 2) and more complex SDMX (UC 3) data about government statistics
-		or other public-domain relevant data.</p>
-
-	<p>Required by: UC1, UC2, UC3</p>
-	</section> <section id="communicating-exposing-statistics-on-the-web">
-	<h4><span class="secno">4.1.3 </span>Communicating, exposing statistics on the web</h4>
-	<p>It should become clear how to make statistical data available on
-		the web, including how to expose it, and how to distribute it.</p>
-
-	<p>Required by: UC5</p>
-	</section> <section id="coverage-of-typical-statistics-metadata">
-	<h4><span class="secno">4.1.4 </span>Coverage of typical statistics metadata</h4>
-	<p>It should be possible to add metainformation to statistics as
-		found in typical statistics or statistics catalogs.</p>
-
-	<p>Required by: UC1, UC2, UC3, UC4, UC5</p>
-	</section> <section id="expressing-hierarchies">
-	<h4><span class="secno">4.1.5 </span>Expressing hierarchies</h4>
-	<p>It should be possible to express hierarchies on Dimensions of
-		statistics. Some of this requirement is met by the work on ISO
-		Extension to SKOS [17].</p>
-
-	<p>Required by: UC3, UC9</p>
-	</section> <section id="machine-readable-and-application-independent-representation-of-statistics-1">
-	<h4><span class="secno">4.1.6 </span>Machine-readable and application-independent representation of
-		statistics</h4>
-	<p>It should be possible to add abstraction, multiple levels of
-		description, summaries of statistics.</p>
-
-	<p>Required by: UC1, UC2, UC3, UC4</p>
-	</section> <section id="expressing-aggregation-relationships-in-data-cube">
-	<h4><span class="secno">4.1.7 </span>Expressing aggregation relationships in Data Cube</h4>
-	<p>Based on [18]: It often comes up in statistical data that you
-		have some kind of 'overall' figure, which is then broken down into
-		parts. To Supposing I have a set of population observations, expressed
-		with the Data Cube vocabulary - something like (in pseudo-turtle):</p>
-	<pre>ex:obs1
-  sdmx:refArea <uk>;
-  sdmx:refPeriod "2011";
-  ex:population "60" .
-
-ex:obs2
-  sdmx:refArea <england>;
-  sdmx:refPeriod "2011";
-  ex:population "50" .
-
-ex:obs3
-  sdmx:refArea <scotland>;
-  sdmx:refPeriod "2011";
-  ex:population "5" .
-
-ex:obs4
-  sdmx:refArea <wales>;
-  sdmx:refPeriod "2011";
-  ex:population "3" .
-
-ex:obs5
-  sdmx:refArea <northernireland>;
-  sdmx:refPeriod "2011";
-  ex:population "2" .
-  	
-	
-	
-	
-	</northernireland></wales></scotland></england></uk></pre>
-	<p>What is the best way (in the context of the RDF/Data Cube/SDMX
-		approach) to express that the values for the England/Scotland/Wales/
-		Northern Ireland ought to add up to the value for the UK and
-		constitute a more detailed breakdown of the overall UK figure? I might
-		also have population figures for France, Germany, EU27, etc...so it's
-		not as simple as just taking a qb:Slice where you fix the time period
-		and the measure.</p>
-	<p>Some of this requirement is met by the work on ISO Extension to
-		SKOS [19].</p>
-
-
-	<p>Required by: UC1, UC2, UC3, UC9</p>
-	</section> <section id="scale---how-to-publish-large-amounts-of-statistical-data">
-	<h4><span class="secno">4.1.8 </span>Scale - how to publish large amounts of statistical data</h4>
-	<p>Publishers that are restricted by the size of the statistics
-		they publish, shall have possibilities to reduce the size or remove
-		redundant information. Scalability issues can both arise with
-		peoples's effort and performance of applications.</p>
-
-	<p>Required by: UC1, UC2, UC3, UC4</p>
-	</section> <section id="compliance-levels-or-criteria-for-well-formedness">
-	<h4><span class="secno">4.1.9 </span>Compliance-levels or criteria for well-formedness</h4>
-	<p>The formal RDF Data Cube vocabulary expresses few formal
-		semantic constraints. Furthermore, in RDF then omission of
-		otherwise-expected properties on resources does not lead to any formal
-		inconsistencies. However, to build reliable software to process Data
-		Cubes then data consumers need to know what assumptions they can make
-		about a dataset purporting to be a Data Cube.</p>
-	<p>What *well-formedness* criteria should Data Cube publishers
-		conform to? Specific areas which may need explicit clarification in
-		the well-formedness criteria include (but may not be limited to):</p>
-	<ul>
-		<li>use of abbreviated data layout based on attachment levels</li>
-		<li>use of qb:Slice when (completeness, requirements for an
-			explicit qb:SliceKey?)</li>
-		<li>avoiding mixing two approaches to handling multiple-measures
-		</li>
-		<li>optional triples (e.g. type triples)</li>
-	</ul>
-
-	<p>Required by all use cases.</p>
-	</section> <section id="declaring-relations-between-cubes">
-	<h4><span class="secno">4.1.10 </span>Declaring relations between Cubes</h4>
-	<p>In some situations statistical data sets are used to derive
-		further datasets. Should Data Cube be able to explicitly convey these
-		relationships?</p>
-	<p>Note that there has been some work towards this within the SDMX
-		community as indicated here:
-		http://groups.google.com/group/publishing-statistical-data/msg/b3fd023d8c33561d</p>
-
-	<p>Required by: UC6</p>
-	</section> </section> <section id="consumption-requirements">
-	<h3><span class="secno">4.2 </span>Consumption requirements</h3>
-
-	<section id="finding-statistical-data">
-	<h4><span class="secno">4.2.1 </span>Finding statistical data</h4>
-	<p>Finding statistical data should be possible, perhaps through an
-		authoritative service</p>
-
-	<p>Required by: UC5</p>
-	</section> <section id="retrival-of-fine-grained-statistics">
-	<h4><span class="secno">4.2.2 </span>Retrival of fine grained statistics</h4>
-	<p>Query formulation and execution mechanisms. It should be
-		possible to use SPARQL to query for fine grained statistics.</p>
-
-	<p>Required by: UC1, UC2, UC3, UC4, UC5, UC6, UC7</p>
-	</section> <section id="understanding---end-user-consumption-of-statistical-data">
-	<h4><span class="secno">4.2.3 </span>Understanding - End user consumption of statistical data</h4>
-	<p>Must allow presentation, visualization .</p>
-
-	<p>Required by: UC7, UC8, UC9, UC10</p>
-	</section> <section id="comparing-and-trusting-statistics">
-	<h4><span class="secno">4.2.4 </span>Comparing and trusting statistics</h4>
-	<p>Must allow finding what's in common in the statistics of two or
-		more datasets. This requirement also deals with information quality -
-		assessing statistical datasets - and trust - making trust judgements
-		on statistical data.</p>
-
-	<p>Required by: UC5, UC6, UC9</p>
-	</section> <section id="integration-of-statistics">
-	<h4><span class="secno">4.2.5 </span>Integration of statistics</h4>
-	<p>Interoperability - combining statistics produced by multiple
-		different systems. It should be possible to combine two statistics
-		that contain related data, and possibly were published independently.
-		It should be possible to implement value conversions.</p>
-
-	<p>Required by: UC1, UC3, UC4, UC7, UC9, UC10</p>
-	</section> <section id="scale---how-to-consume-large-amounts-of-statistical-data">
-	<h4><span class="secno">4.2.6 </span>Scale - how to consume large amounts of statistical data</h4>
-	<p>Consumers that want to access large amounts of statistical data
-		need guidance.</p>
-
-	<p>Required by: UC7, UC9</p>
-	</section> <section id="common-internal-representation-of-statistics-to-be-exported-in-other-formats">
-	<h4><span class="secno">4.2.7 </span>Common internal representation of statistics, to be exported
-		in other formats</h4>
-	<p>QB data should be possible to be transformed into data formats
-		such as XBRL which are required by certain institutions.</p>
-
-	<p>Required by: UC10</p>
-	</section> <section id="dealing-with-imperfect-statistics">
-	<h4><span class="secno">4.2.8 </span>Dealing with imperfect statistics</h4>
-	<p>Imperfections - reasoning about statistical data that is not
-		complete or correct.</p>
-
-	<p>Required by: UC7, UC8, UC9, UC10</p>
-	</section> </section> </section>
-	<section class="appendix" id="acknowledgments">
-	<!--OddPage--><h2><span class="secno">A. </span>Acknowledgments</h2>
-	<p>The editors are very thankful for comments and suggestions ...</p>
-	</section>
-
-	<h2 id="references">References</h2>
-
-	<dl>
-		<dt id="ref-SDMX">[SMDX]</dt>
-		<dd>
-			SMDX - User Guide 2009, <a href="http://sdmx.org/wp-content/uploads/2009/02/sdmx-userguide-version2009-1-71.pdf">http://sdmx.org/wp-content/uploads/2009/02/sdmx-userguide-version2009-1-71.pdf</a>
-		</dd>
-
-		<dt id="ref-SDMX">[Fowler1997]</dt>
-		<dd>Fowler, Martin (1997). Analysis Patterns: Reusable Object
-			Models. Addison-Wesley. ISBN 0201895420.</dd>
-
-		<dt id="ref-QB">[QB]</dt>
-		<dd>
-			RDF Data Cube vocabulary, <a href="http://dvcs.w3.org/hg/gld/raw-file/default/data-cube/index.html">http://dvcs.w3.org/hg/gld/raw-file/default/data-cube/index.html</a>
-		</dd>
-
-		<dt id="ref-OLAP">[OLAP]</dt>
-		<dd>
-			Online Analytical Processing Data Cubes, <a href="http://en.wikipedia.org/wiki/OLAP_cube">http://en.wikipedia.org/wiki/OLAP_cube</a>
-		</dd>
-
-		<dt id="ref-linked-data">[LOD]</dt>
-		<dd>
-			Linked Data, <a href="http://linkeddata.org/">http://linkeddata.org/</a>
-		</dd>
-
-		<dt id="ref-rdf">[RDF]</dt>
-		<dd>
-			Resource Description Framework, <a href="http://www.w3.org/RDF/">http://www.w3.org/RDF/</a>
-		</dd>
-
-		<dt id="ref-scovo">[SCOVO]</dt>
-		<dd>
-			The Statistical Core Vocabulary, <a href="http://sw.joanneum.at/scovo/schema.html">http://sw.joanneum.at/scovo/schema.html</a>
-			<br> SCOVO: Using Statistics on the Web of data, <a href="http://sw-app.org/pub/eswc09-inuse-scovo.pdf">http://sw-app.org/pub/eswc09-inuse-scovo.pdf</a>
-		</dd>
-
-		<dt id="ref-skos">[SKOS]</dt>
-		<dd>
-			Simple Knowledge Organization System, <a href="http://www.w3.org/2004/02/skos/">http://www.w3.org/2004/02/skos/</a>
-		</dd>
-
-		<dt id="ref-cog">[COG]</dt>
-		<dd>
-			SDMX Content Oriented Guidelines, <a href="http://sdmx.org/?page_id=11">http://sdmx.org/?page_id=11</a>
-		</dd>
-
-	</dl>
-
-
-</body></html>
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