=Paper=
{{Paper
|id=None
|storemode=property
|title=Importing Knowledge Fragments to CMS-Enabled Data Mining Analytical Reports
|pdfUrl=https://ceur-ws.org/Vol-611/paper9.pdf
|volume=Vol-611
}}
==Importing Knowledge Fragments to CMS-Enabled Data Mining Analytical Reports==
<pdf width="1500px">https://ceur-ws.org/Vol-611/paper9.pdf</pdf>
<pre>
        Importing Knowledge Fragments to
    CMS-Enabled Data Mining Analytical Reports

              Andrej Hazucha, Tomáš Kliegr, and Vojtěch Svátek

                        University of Economics, Prague,
                    {xhaza00,tomas.kliegr,svatek}@vse.cz
    Descriptive data mining only brings its fruits when the results are provided
to the end user in a palatable form. The vehicle for end-user delivery of mining
results (and associated information such as data schema, task settings, and do-
main background knowledge) are so-called analytical reports. In order to manage
a huge number of reports referring to different mining sessions, we designed a
data mining web portal based on a content management system, together called
SEWEBAR-CMS.1 One of the requirements on the CMS was the ability to in-
teract with semantic knowledge sources and other structured data, see [1].
    The data analyst who authors an analytical report in the CMS has different
possibilities of (semi-)automatically entering structured data into the text.
    First, for locally stored data such as mining task/result/data descriptions
exported from mining tools in PMML (Predictive Model Mark-Up Language), a
CMS plugin can pick marked segments of HTML code, produced from PMML
using XSLT, and insert them into the report as indicated by the analyst.
    Second, sophisticated support for remote data/knowledge has been newly
added. The infrastructure for this functionality allows to persistently specify
 – Links to queriable resources
 – Template queries for these resources (which can be paramatrized by the
   end-user at runtime)
 – XSLT transformations allowing to insert the results of queries as HTML
   fragments, either static or dynamically updated from the resources.
Currently we experiment with queriable resources in the form of native XML
database (Berkeley, queried via XQuery), which stores PMML data, and seman-
tic knowledge bases both in the form of SPARQL endpoint and Ontopia Knowl-
edge Suite (a Topic Maps tool, queried via a Prolog-like language called tolog).
Inclusion of further types of resources such as Lucene indices is in progress.
This work has been partially supported by the CSF project no.201/08/0802, and
by Grant F4/15/2010 of the University of Economics, Prague.

References
 1. Kliegr M., Ralbovský M., Svátek, V, Šimůnek M., Jirkovský V., Nemrava J.,
    Zemánek J.: Semantic Analytical Reports: A Framework for Post-Processing Data
    Mining Results. In: Proc. ISMIS’09, Springer Verlag, LNCS, 2009, 8898.

1
    SEWEBAR stands for SEmantic WEB and Analytical Reports. More details in
    http://sewebar.vse.cz.

</pre>