=Paper=
{{Paper
|id=Vol-1224/paper6
|storemode=property
|title=Processing RDF Data in UnifiedViews
|pdfUrl=https://ceur-ws.org/Vol-1224/paper6.pdf
|volume=Vol-1224
|dblpUrl=https://dblp.org/rec/conf/i-semantics/Knap14
}}
==Processing RDF Data in UnifiedViews==
https://ceur-ws.org/Vol-1224/paper6.pdf
Processing RDF Data in UnifiedViews
Tomáš Knap1,2 ?
1
University of Economics, Prague, Czech Republic
2
Charles University in Prague,
Faculty of Mathematics and Physics, Dept. of Software Engineering
Malostranské nám. 25, 118 00 Prague, Czech Republic
tomas.knap@mff.cuni.cz
Abstract. UnifiedViews is an Extract-Transform-Load (ETL) frame-
work that allows users – publishers, consumers, or analysts – to define,
execute, monitor, debug, schedule, and share RDF data processing tasks.
The data processing tasks may use custom plugins created by users.
UnifiedViews differs from other ETL frameworks by natively supporting
RDF data and ontologies. The practical demonstration of UnifiedViews
at the conference will (1) clearly demonstrate how UnifiedViews helps
RDF/Linked Data users with RDF data processing (2) and show the
real instance of UnifiedViews with tens of data processing tasks and
DPUs motivated by real data processing use cases.
1 Introduction
There are lots of tools used by the RDF/Linked Data community3 , which may
support various phases of RDF data processing; e.g., a user may use any23 4 for
extraction of non-RDF data and its conversion to RDF data, Virtuoso 5 database
for storing RDF data and executing SPARQL (Update) queries [1, 2], Silk [4] for
RDF data linkage, or Cr-batch 6 for RDF data fusion.
Nevertheless, the user who is preparing a data processing task producing a
data mart typically has to (1) configure every such tool using a different con-
figurator, (2) implement a script for retrieving source data, (3) write his own
script holding the set of SPARQL Update queries refining the data, (4) imple-
ment custom transformers which, e.g., enrich processed data with the data in
his knowledge base, (5) write his own script executing the tools in the required
order, so that every tool has all desired inputs when being launched, (6) pre-
pare a scheduling script, which ensures that the task is executed regularly, and
(7) extend his script with notification capabilities, such as sending an email in
case of an error during task execution. Furthermore, in case of errors in data
processing, the user has no support for debugging the tasks.
?
This work was supported by EU ICT FP7 under No.257943 (LOD2 project) and by
projects SVV-2014-260100 and PRVOUK.
3
http://semanticweb.org/wiki/Tools
4
https://any23.apache.org/
5
http://virtuoso.openlinksw.com/
6
https://github.com/mifeet/cr-batch
Posters & Demos Track @ SEMANTiCS2014 25
Fig. 1. UnifiedViews Framework – Definition of a Data Processing Task
2 UnifiedViews
To address these problems, we developed UnifiedViews, an Extract-Transform-
Load (ETL) framework, where the concept of data processing task is a central
concept. Another central concept is the native support for RDF data format and
ontologies.
A data processing task (or simply task) consists of one or more data pro-
cessing units. A data processing unit (DPU) encapsulates certain business logic
needed when processing data (e.g., one DPU may extract data from a SPARQL
endpoint or apply a SPARQL query). Every DPU must define its required/optional
inputs and produced outputs. UnifiedViews supports exchange of RDF data be-
tween DPUs. Every tool produced by RDF/Linked Data community can be used
in UnifiedViews as a DPU, if a simple wrapper is provided7 .
UnifiedViews allows users to define and adjust data processing tasks, using
graphical user interface (an excerpt is depicted in Figure 1). Every user may
also define their custom DPUs, or share DPUs provided by others together with
their configurations. DPUs may be drag&dropped on the canvas where the data
processing task is constructed. Data flow between two DPUs is denoted as an
edge on the canvas (see Figure 1); a label on the edge clarifies which outputs
of a DPU are mapped to which inputs of another DPU. UnifiedViews natively
supports exchange of RDF data between DPUs; apart from that, files and folders
may be exchanged between DPUs.
UnifiedViews takes care of task schedulling, a user may configure Unified-
Views to get notifications about errors in the tasks’ executions; user may also
get daily summaries about the tasks executed. UnifiedViews ensures that DPUs
are executed in the proper order, so that all DPUs have proper required inputs
when being launched. UnifiedViews provides users with the debugging capabili-
ties – a user may browse and query (using SPARQL query language) the RDF
inputs to and RDF outputs from any DPU. UnifiedViews allows users to share
DPUs and tasks as needed.
Paper [3] contains discussion about related work and lists projects where
UnifiedViews is used. The code of UnifiedViews is available at GitHub8 under a
combination of GPLv3 and LGPLv3 license9 .
7
https://grips.semantic-web.at/display/UDDOC/Creation+of+Plugins
8
https://github.com/UnifiedViews
9
http://www.gnu.org/licenses/gpl.txt, http://www.gnu.org/licenses/lgpl.txt
26 Tomáš Knapl
3 UnifiedViews - The Demo
The demo of the tool is available at http://odcs.xrg.cz:8080/unifiedviews. You
can use the account guest/guest to test the framework. When you log in, you
can see tasks (menu item Pipelines) and DPUs (menu item DPU Templates)
available in the framework. You can, for example, run DBpedia pipeline, which
is extracting data about Prague from DBpedia, and supplement the pipeline
with SPARQL Transformer DPU executing certain SPARQL (Update) queries
on top of the extracted data.
The practical demonstration of UnifiedViews at the conference will (1) clearly
demonstrate how UnifiedViews helps RDF/Linked Data users with RDF data
processing (2) and show the real instance of UnifiedViews with tens of data
processing tasks and DPUs motivated by real use cases.
References
1. S. H. Garlik, A. Seaborne, and E. Prud’hommeaux. SPARQL 1.1 Query Language.
W3C Recommendation, 2013. http://www.w3.org/TR/2013/REC-sparql11-query-
20130321/, Retrieved 20/03/2014.
2. P. Gearon, A. Passant, and A. Polleres. SPARQL 1.1 Update. Tech-
nical report, W3C, 2013. Published online on March 21st, 2013 at
http://www.w3.org/TR/2013/REC-sparql11-update-20130321/, Retrieved
20/03/2014.
3. T. Knap, M. Kukhar, B. Macháč, P. Škoda, J. Tomeš, and J. Vojt. UnifiedViews:
An ETL Framework for Sustainable RDF Data Processing. In Extended Semantic
Web Conference (ESWC 2014), Anissaras, Crete, Greece, 2014. Springer.
4. J. Volz, C. Bizer, M. Gaedke, and G. Kobilarov. Silk - A Link Discovery Framework
for the Web of Data. In Proceedings of the WWW2009 Workshop on Linked Data
on the Web (LDOW), Madrid, Spain, 2009. CEUR-WS.org.