Briefly introducing UnifiedViews and outlines intended future directions of UnifiedViews. Presented at the Czech workshop Dateso 2015.

1. UnifiedViews: Towards ETL Tool
for Simple yet Powerful RDF Data Management
T. Knap, P. Škoda, J. Klímek, M. Nečaský
http://xrg.cz | knap@ksi.mff.cuni.cz
XML and Web Engineering Research Group
Faculty of Mathematics and Physics
Charles University in Prague, Czech Republic
Dateso 2015

2. Agenda
 UnifiedViews
 Basic concepts, Impact
 Areas of ongoing and future work
Dateso 2015

3. UnifiedViews
Basic Concepts, Impact
Dateso 2015

4. UnifiedViews
 an Extract-Transform-Load (ETL)
framework with UI that allows users to
define, execute, monitor, debug, schedule,
and share RDF data processing tasks
 UnifiedViews differs from other ETL
frameworks by natively supporting processing
of RDF data.
Dateso 2015

5. A Pipeline
 Every data processing task is modelled as a pipeline in
UnifiedViews
 Every pipeline consists of one or more DPUs (data
processing units) and arrows depicting data flow
 a
Dateso 2015

6. A Data Processing Unit (DPU)
 Plugin, which encapsulates certain functionality, typically on top of
RDF data
 Users may prepare custom plugins
 Every DPU has its inputs, outputs, business logic and configuration
 E.g., DPU may apply SPARQL Update query to the input RDF data and
produce output RDF data
 a
Dateso 2015

7. Key Features
 Web administration interface:
 Define and manage pipelines
 Validate, execute, monitor and debug pipelines
 Possibility to schedule tasks, set up notifications about the pipeline executions
 Define and manage DPUs
 Possibility to debug inputs to/outputs from DPUs
 Possibility to share pipelines and DPUs
 Possibility to get notifications about the result of the pipeline execution
 Multi-user environment
 Engine running the tasks
 Ensures that DPUs on the pipeline are executed in the proper order
 It may send notifications about the result of the pipeline execution
 Core DPUs to work with RDF data
 Easy way how to extend Unified Views with your own DPUs
 Every DPU is an OSGi bundle, as a result, two DPUs using two different
versions of the same library may coexist in the framework
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8. Impact of UnifiedViews
 Projects
• OpenData.cz initiative
• INTLIB (2012-2014) – TaCR project
• LOD2 (2011-2014) – EU FP7 project
• UnifiedViews integrated into the LOD2 stack
• COMSODE (2013-2015) – EU FP7 project
• Open Data Node contains UnifiedViews
• YourDataStories (2015+), H2020
• TenForce, Belgium
 also commercial projects
• Semantic Web Company (Austria),
• EEA s.r.o. (SK)
Dateso 2015

9. UnifiedViews
Ongoing and Future Work
Dateso 2015

10. Automatic Schema Alignment and
Object Linkage
 Object Linkage:
 Motivation: If various datasets use the same identifiers for the same
real world objects (cities, countries), level of data integration is
increased and costs of ad-hoc application integration is reduced
 Goal: To automatically discover that certain columns in the processed
tabular data represent certain types of data (e.g. cities, countries) and
automatically mapping values in this column to Linked Data URIs taken
from the preferred dataset for the given type of data
 Schema Alignment:
 Motivation: increase understandability of the data and simplify reuse
of the data by various applications by using common vocabularies.
 Goal: To automatically suggest mappings of used RDF vocabulary
terms (e.g., predicated) to well-known RDF terms (e.g., predicates)
Dateso 2015

11. Simplicity of Use
 Hiding SPARQL Queries
 Goal: To provide set of DPUs for executing typical SPARQL query
operations on top of RDF data
 Autocompleting Terms from Well-known Vocabularies
 Goal: To Suggest and autocomplete vocabulary terms from well-
known Linked Data vocabularies
• Vocabulary autocomplete-aware controls (text boxes)
• Description of the term, formal def., recommended usage
 Wizards for Simple Definition of Data Processing Tasks
 Motivation: Defining data processing tasks typically requires
detailed knowledge of the DPUs that are available in the
deployed UnifiedViews instance;
 Goal: Step by step guides for defining new typical types of data
processing tasks, e.g, extracting and publishing tabular
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12. Sustainability and Quality
 Sustainable RDF Data Processing
 Goal: To allow task designer to define for each DPU a set of
SPARQL queries, which tests that the output data
produced by the given DPU satisfies certain conditions. If
possible, automate creation of such queries.
 Assessing Quality of Produced Data, Recommendation
of Cleansing DPUs
 Motivation: task designer should be informed about any
problems in the data, e.g., w.r.t. syntactic/semantic
accuracy of the produced Linked Data or completeness of
the published datasets
 Goal: Set of DPUs assessing the quality of the data,
cleansing the data
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13. Conclusions
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14. Summary
 UnifiedViews – ETL tool for RDF data
processing
 Basic concepts, Impact
 Areas of ongoing and future work
Dateso 2015

15. Would you like to try UnifiedViews?
 UnifiedViews is available under open
source license
 GPLv3 + LGPLv3
 Hosted on GitHub
 Repository: https://github.com/UnifiedView
 Current latest version: Unified Views 2.0.1
 More info:
 unifiedviews.eu
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16. Thank You!
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17. How to contribute?
 Guideline for contributors:
 https://grips.semantic-
web.at/display/UDDOC/Guidelines+for+Contributors
Dateso 2015
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