Lithuania Turkey Collaboration Day (LiTurCo 2017) 4 & 5 December 2017 Eskişehir, Turkey http://liturco.org/

1. Elaboration and enhanced usage of data
analysis tool DAMIS+
Dr. Saulius Maskeliūnas
Vilnius University
Lithuania Turkey Collaboration Day
4 & 5 December 2017 – Eskişehir / TURKEY

2. 2
PROJECT IDEA NUMBER: ..
(change in 1st slide of master)
The Institute of Mathematics and Informatics is a subdivision of Vilnius
University. It was a state scientific institute, a budgetary scientific
research institution until 30th September 2010;
IMI will become
Institute of Data Science and Digital Technologies since 2018.
IMI was set up to pursue important long-term research for the economy
of Lithuania and international cooperation.
There are 89 employees at the Institute.
67 researchers are working in the scientific departments, including: 11
doctors habilitatus and 48 doctors (11 doctors have completed
habilitation procedure); 19 professors, and 9 docents.
There are 52 doctoral students: 9 in Mathematics, 19 in Informatics
and 24 in Informatics Engineering; all of them are full-time students.
https://www.mii.lt/files/doc/en/imi-reports/research_at_vu_imi_2016.pdf
Vilnius University Institute of
Mathematics and Informatics (VU IMI)

3. 3
PROJECT IDEA NUMBER: ..
(change in 1st slide of master)
VU IMI structure / research directions

4. 4
PROJECT IDEA NUMBER: ..
(change in 1st slide of master)
Erasmus+ partners of VU Institute of
Mathematics and Informatics from Turkey

5. 5
PROJECT IDEA NUMBER: ..
(change in 1st slide of master)
• Production Effectiveness Navigator (PEN)
Joint programme „EuroStars“ E!6232 Nr. 31V-20/LSS-580000-360
Turkish partner: HISBIM (coordinator)
May 2011 – October 2014.
https://web.archive.org/web/20141018082514/
http://www.eurekanetwork.org/project/-/id/6232
• ADHER: ADopt your HERitage.
Grundtvig project of Longlife Learning Program for adult
education, Nr. LLP-GRU-MP-2009-LT-00008.
Turkish partner: Atakum Adult Education Center
August 2009 – July 2011
http://www.adher.mii.lt/
VU IMI project with partners from Turkey

6. Project idea description & Key selling points
 We have developed and are using [in the environment of
National Open Access Research Data Archive (http://midas.lt/), etc.]
the data analysis tool DAMIS (http://damis.lt/ ; demo / demo ).
 Now DAMIS allows to carry out: (a) data preprocessing (clea-
ning, filtering, splitting, trasposing, normalising, feature selection);
(b) statistical handling; (c) dimensionality reduction
[PCA, SMACOF, DMA, relative DMS, SAMANN, SOM-MDS];
(d) classification, clustering [SOM, MLP, RDF, K-MEANS];
(e) viewing of results [chart and/or matrix view, technical details].
 The proposed collaborative project DAMIS+ should develop:
(1) methodology, (2) platform, (3) toolbox – possibility to
dinamically extend functionality, capabilities of this data analysis
tool [with additional methods, etc.] by users themselves,
(4) expanded use cases for enhanced usage of
data analysis tool.

7. Partners & expertise
 Partners already involved
- Vilnius University;
- Vilnius University Hospital Santaros Klinikos; Lithuanian Hospitals;
- Lithuanian Researchers (mainly from Universities).
 Partners looking for
- Software and/or research companies/institutions;
- Any prospective users of data mining and knowledge discovery,
multidimensional data analysis, visualisation, etc. ...

8. Name Surname Saulius Maskeliūnas
Institution VU Institute of Mathematics and Informatics
(after 2017: Institute of Data Science and Digital Technologies)
Email address saulius.maskeliunas@mii.vu.lt
Web address www.mii.lt
Telephone +370 5 2109342
Contact details

9. Functionalities and demonstration
• DAMIS is a web-based system http://damis.lt
(user name/password: demo/demo );
• The web interface does not require any software
installation; a web browser is enough for its usage;
• There is a possibility to choose
high performance computing resources
(VU MII cluster – VU MIF supercomputer);
• The usage is based on creation of scientific workflows;
• The results obtained can be saved in MIDAS and
in a user computer.

10. A sample of multidimensional data
(breast cancer data)
C
5 1 1 1 2 1 3 1 1 b
5 4 4 5 7 10 3 2 1 b
3 1 1 1 2 2 3 1 1 b
6 8 8 1 3 4 3 7 1 b
4 1 1 3 2 1 3 1 1 b
1 1 1 1 2 10 3 1 1 b
2 1 2 1 2 1 3 1 1 b
2 1 1 1 2 1 1 1 5 b
4 2 1 1 2 1 2 1 1 b
... ... ... ... ... ... ... ... ... ... ...
8 10 10 8 7 10 9 7 1 m
5 3 3 3 2 3 4 4 1 m
8 7 5 10 7 9 5 5 4 m
7 4 6 4 6 1 4 3 1 m
10 7 7 6 4 10 4 1 2 m
7 3 2 10 5 10 5 4 4 m
10 5 5 3 6 7 7 10 1 m
... ... ... ... ... ... ... ... ... ... ...
4 8 8 5 4 5 10 4 1 m

11. Data upload

12. Data preprocessing

13. Statistical primitives

14. Dimensionality reduction

15. Matrix view of data after
dimensionality reduction by PCA

16. Data classification and clustering

17. Iris graphical representation

18. Experiments