In this presentation for the ESSnet Linked Open Statistics final event, Sergi Segiev is presenting the learned lessons from two implemented use cases with open data for finding valuable insights. You can also refer to the presentation 'Data Reveals Corruption Practices' by Yasen Kiprov - http://bit.ly/2WsFxsP

1. Lessons Learned: Linked Open Data
implemented in 2 Use Cases

2.  Data Science Society intro
 Case 1
 Case 2
 Lessons learned
2

3. Email: sergiev.sergi@gmail.com
LinkedIn: /in/sergiev/3
SERGI SERGIEV

4. Special Interests
Groups
Nerd-up sessions
Meetups
Datathons

5. 60+ meetups and
conferences
Monthlyevents andconferences
where people with expertise in
different areas share their cases and
problems in different industries
from a Data Science prospective.
7 Datathons
A weekend-long online and
physical international
competition with real-world
business cases, top experts, 350+
participants from 20+ countries.
Members
over 50 countries
• Senior developers
• Master and PhD Students in Data
Science, Statistics, Business
Analytics etc.
• Domainexperts passionate about
data
• Mathematical and datageeks
Working with
Universities
Organizing different events,
Academia Datathons and
monthly challengesworking
closely with +10 Universities
150+ solutions
from 28 cases
100+ teams with 1 to 25 years of
expertise involvedin solving
variousbusiness cases
15+ training
sessions
Various trainings, workshops,
master classes, summer school
etc… are organizedwith practical
implications in Data Science
domain.

6. OUR OWN ENVIRONMENT
Data.Platform [THE FOUNDATION OF BEING GLOBAL ]
6
Online Learn repositories
Data.Chat
Data.Cloud
230 + SIG + Scientific Articles
75 000 + Messages
Jupyter Notebook integration
with R and Python

7. DATA SCIENCE SOCIETY
COMMUNITIES COMPANIES

8. DATATHON 2017 CASE
DATA REVEALS CORRUPTION PRACTICES
8
Input data:
 Bulgarian public procurement
 EC Procurement
 Trade Register
Open Government from Council of Ministers
Output:
 The size of the uploaded data is approximately 12.5
million triples (more than 2 GB of uncompressed data).
 A interesting question that can be explored about
conflicts of interest.

9. DATATHON 2017 CASE
BULGARIAN TRADE REGISTER
9
Reference through company
or institution UIC ID (ЕИК):
 Name
 Address
 Legal form
Open Government from Council of Ministers
People of interest are linked:
 has Manager
 has ActiveManager
 has Partner
 has ActivePartner
 has ActiveOwner

10. DATATHON 2017 CASE
BULGARIAN PUBLIC PROCUREMENTS
10
Raw data in CSV format
Total of 207579 contracts for the period 2007 - 2016
Each Procurement has a Contract with
 Title
 Kind (delivery, service, construction)
 Issuing Authority
 Lots
 Awarded tender
 Contract Price
 Actual Price
 Dates
Reference: Data Reveals Corruption Practices, Yasen Kiprov

11. DATATHON 2017 CASE
EC PROCUREMENTS
11
Raw data in XML format
Total of 11798 projects with
 Beneficiaries
 Lots
 Dates
 Payments
Reference: Data Reveals Corruption Practices, Yasen Kiprov

12. DATATHON 2017 CASE
SO …
12
Person: Ясен
Company: Профай
Links:
 hasManager
 hasPartner
 …
EGN is obfuscated
Reference: Data Reveals Corruption Practices, Yasen Kiprov

13. DATATHON 2017 CASE
FURTHER POSSIBLE QUERIES
13
 A conflict of interest may arise if a person A managing a
government entity is also a related party (such as, for example,
owner) of a private contractor of the government entity.
 Connected companies:
 Companies which have a common active member
 Influencers - people who are involved in many companies
 People who are involved both in the Authority and the
Awarded Tender
Reference: Data Reveals Corruption Practices, Yasen Kiprov

14. DATATHON 2017 CASE
RESULTS
14 Reference: Data Reveals Corruption Practices, Yasen Kiprov

15. 15

16. WHAT WE DID?
 Promote in media groups
 Meetup and webinar
 Participate in 2nd Hackathon
 Company exposure and PR
16
DATATHON 2017 CASE
LESSONS
OUTREACH
 No exposure from press
 No support from Open Data
 None is using the result

17. DATATHON 2019 CASE
SOFIA AIR POLLUTION
17
Input data:
 Weather
 Topography
 Industrial pollution data
 Heating data
 Constructions data
 Air Quality data
Output:
 The different factors that affect air pollution levels
NIGGG

19. WHAT WE DID?
 Involve Sofia Municipality
 Community exposure
19
DATATHON 2019 CASE
LESSONS
OUTREACH
 Exposure to press
 BI tool project is initiated
 Support from SDA
NIGGG

20.  Accelerators: Metro, BMW startup garage, Coca Cola, McDonalds
 Open Source Challenges
 Google Deepmind [ www.deepmind.com/research/open-source/open-source-code/]
 Amazon: over 1600 projects on GitHub [www.aws.amazon.com/opensource/]
 Baidu [github.com/baidu]
 Uniliver: R&D €1 billion, staff: 6 200, 30 - 40 %
 OpenAI [github.com/openai]
20
OPEN-SOURCE CULTURE

21. 21
OPEN-SOURCE CULTURE
LESSONS LEARNED
OUTREACH
 Exposure to press
 BI tool project is initiated
 Support from SDA
 Focus
 Find communities
 Listen and work with them - open interesting datasets
 Rely on open-source culture – you are note alone
 Promote
 Be entrepreneur – praise the great results
 PR Visibility
 Support
 Financial, Resources, Logistics, Media

22. 22
Engaged community
Special Interests Groups
Keeping the community strong and active
A monthly meeting where two speakers
present a Data Science topic ending with a
discussion over a beer in Sofia and streamed
for the rest of the world
Meetups
A five days summer training with
more than 10 topics and several
practical tasks.
Summer School
A 3-4 hours meeting, every week where a
group of people work together on their
own data science projects and discuss
ideas on improving their progress.
Coding sessions
A two days comprehensive training with
presentations and workshops with a focus on
big data and data science
Data Science Master class
A 3 to 4 hours introductory presentation or
demonstration with practical exercises on
various topics (Probability programing, Retailer
time series analysis, BI intro with Power BI,
Machine learning intro etc.)
Workshops
Data
Science
Society

23. Become Part of
Data Science Society!
DSS:
What we do and what we achieve
on our website or social networks:
http://datasciencesociety.net
Contacts:
Reach out directly.
Email:
info@datasciencesociety.net
Phone: +359 888 400 290