Graph Database is the new paradigm of Big Data. New insights are discovered in the connected data. Fabricating Big Data into connected data is the cutting edge technology. Graph database is the driver for sustainable growth in the Era of Big Data. Graph Data is already prevailing among the global leading companies. Graph Database will pass the dawn of standards. The most widely adopted method will be the Hybrid Database. Each company needs to prepare for the wave of change. AgenGraph will support your business with superior capabilities. For more information, please visit www.bitnine.net

2. 초일류 기업 성공을 이끈 빅데이터의 패러다임
– 그래프 데이터베이스
Joshua Y. BAE / Marketing Director
Bitnine Global

3. Graph Database
is the new paradigm of Big Data

4. Graph Database
is the new paradigm of Big Data
2012 2013 2014 2015 2016 2017 2018 2019 2020
Data Growth vs. Moore's Law
Data Growth Moore's Law

5. Volume VarietyVelocity/ /

6. (n-1)
2
n X

7. Graph Database is prevailing
Google
PageRank
Facebook
Graph Search
LinkedIn
Economic Graph

8. Graph Database is prevailing

9. A Talk on Graph Database

10. CityPerson :LIVES_IN
In the graph database, relationships are the first-class citizen.
Name: Sunnyvale, CA
Population: 147,559
Established: 1912
Name: Joshua Bae
Email: joshua@bitnine.net
Since: 2016
Nodes
Properties
Relationship
Graph Model

11. Graph Database – Connected Data

12. How Graph Database organizes data

13. How Graph Database organizes data

14. with recursive
as (
select
parent, child as descendant,
1 as level from source
union all
select
d.parent, s.child, d.level + 1
from descendants as d
join source s on d.descendant = s.parent
)
select * from descendants
order by parent, level, descendant ;
Query: Find all ancestor-descendant pairs in the graph.
MATCH
p=(descendant)-[:Parent*]->(ancestor)
RETURN
(ancestor), (descendant), length(p)
ORDER BY (ancestor), (descendant), length(p)
GraphDatabase
descendant ancestor
SQL
Graph Database is Human-Readable

15. Use Cases in various industries

16. Genetics

17. Genetics
Gene Sequencing
T A A G CC T
A T T
G A C G

18. Genetics

19. Neuroscience

20. Smart Medicine

21. IoT – The connected devices

22. Logistics – Geospatial Data

23. Communication Network

24. Semiconductor process control

25. Manufacturing
25

26. Graph Use Case –
Panama Papers

27. Visualized Money-Laundering
27

28. Visualized Money Laundering
Size of leaked files:
Count of
documents:
Number of e-mails: 4.8M
11.5M
2.6T Bytes

29. Panama Papers
29

30. Graph Use Case -
Recommendation Engine

31. Recommendation Engine

32. Graph Use Case –
Cyber Security

33. MAIL.RU
(25,000,000)
History of hacking
http://www.zdnet.co.kr/news/news_view.asp?artice_id=20160411142754
MINECRAFT
(7,000,000)
MOSSACK FONSECA
(11,500,000)
TELEGRAM
(15,000,000)
MYSPACE (427,000,000)
YAHOO (500,000,000)

34. Types
Characteristics
Attacks using
viruses, worms, and
troy
Access to the
unauthorized
information by
file modification
Acquiring
authorization by
attacking
vulnerable targets
Disrupting or
disabling services by
massive loading
attacks
Utilizing extensi
ve resources
Distributed
attack routes
Low cost of
attacks
Targeted points
Cyber Attacks

35. Cyber Attacks

36. Network focused remedy
Attack Information analysis remedy
Current Technologies

37. Unknown user risk analysis
Single attack point IP detections
Multi modulated attack detection
MATCH (l)-[r1]->(n:UK_user)-[r2]->(n:Attack_point)<-[r3]-(m:UK_user)<-[r4]-(o)
WHERE l.danger_flag=1 OR o.danger_flag=1 WITH n,m,
collect(l) AS cols1, collect(o) AS cols2
WITH n,m,reduce(dlevel=0, col IN cols1 | dlevel+col.danger_flag) AS n_dlevel,
reduce(dlevel=0, col IN cols2 | dlevel+col.danger_flag) AS m_dlevel
RETURN n, n_dlevel, m, m_dlevel
MATCH p=(n:Attacker)-[r*]->(m:Attack_point) WITH n, collect(m) AS defacer, length(p) AS defaceDepth
RETURN n, defacer, defaceDepth
MATCH p=(n:Attack_point)<-[r*]-(m:IP_address) WITH m, length(p) as pathLength ORDER BY pathLength DESC
RETURN m, pathLength LIMIT 1
How Graph Database detects cyber attacks

38. Graph visualization

39. Enron Accounting Scandal
https://www.econcrises.org/2016/12/07/enron-corporation-2001/

40. Enron Accounting Scandal
40
https://youtu.be/RdasVHI6D6c

41. Enron Accounting Scandal
41
https://youtu.be/RdasVHI6D6c

42. Visualized Timeline
42

43. Visualized Timeline
43

44. Database Market Trends

45. Total Database Market
30,980
33,248
35,640
38,186
40,988
43,693
7.3% 7.2% 7.1% 7.3% 6.6%
0%
10%
20%
30%
40%
50%
0
10,000
20,000
30,000
40,000
50,000
2015 2016 2017 2018 2019 2020
DBMS
DBMS CAGR
CAGR %
USD Million
IDC 2015

46. NoSQL Database Market
643
869
1,174
1,586
2,142
2,893
35.1% 35.1% 35.1% 35.1% 35.1%
0%
10%
20%
30%
40%
50%
0
500
1,000
1,500
2,000
2,500
3,000
3,500
2015 2016 2017 2018 2019 2020
NoSQL
NoSQL CAGR
CAGR %
USD Million
https://www.alliedmarketresearch.com/NoSQL-market

47. Graph Database Awareness
http://db-engines.com/en/ranking_categories

48. Gartner Magic Quadrant DBMS
Magic Quadrant for Operational Database Management Systems

49. Trend Analysis:
 Graph DBMSs are still in the
Advantage phase.
 The popularity about graph DBMSs
revolves around ad hoc discovery of
relationships.
 Graph capabilities are being
introduced as the first additional
option in many newly multi-model
DBMS offering.
IT Market Clock for Database Management Systems, 2016, Gartner
Gartner’s IT Clock

50. 20152016
IT Market Clock for Database Management Systems, 2016, Gartner
Gartner’s IT Clock
Graph DBMS
Commoditization
: 10/20
Zenith of
Industrialization
Dawn of
Standards
Dusk of
Obsolescence
Market Starts
2:45
2 years
 Levels of standardization
 Level of vendor choice
 Ease of access to appropriate skills.

51. Hybrid Database

52. Graph Database awareness
 It is reported that graph databases — the fastest growing category in
database management systems — will reach more than a quarter of
enterprises by 2017. (Forrester Research )
 By the end of 2018, 70% of leading organizations will have one or more pilot
or proof-of-concept efforts underway utilizing graph database. (Gartner)
2017 2018

53. Hybrid Database
 Graph capabilities are being introduced as the first
additional option in many newly multi-model DBMS
offering. (Gartner)
 By 2017, all leading operational DBMS’s will offer
multiple data models, relational and NoSQL, in a single
DBMS platform.
 By 2018, the NoSQL label will cease to distinguish
DBMSs, which will reduce its value and result in it falling
out of use.

55. AgensGraph Features
AgensGraph Foreign Data Wrapper
< Web data > < File data > < DB data > < Big data >
Data Collection
Providing FDW (Foreign Data Wrapper) which can collect data from the external data sources such as
structured, semi-structured, unstructured.

56. AgensGraph Features
Single Database Engine - multi data models
Table Document Graph
Various Data Storage
As a hybrid database, AgensGraph supports relational, document, and graph data in a single system.

57. AgensGraph Features
Powerful and Flexible Integrated Query
AgensGraph can process the graph data query language, Cypher and
ANSI SQL in parallel and in a single query together.

58. AgensGraph
58
• Multi Model Database with the Advanced SQL and
the Graph Database.
• AgensGraph will make limitations obsolete with its
cutting-edge technology.
• AgensGraph will provides the motivations to create
new values in your business with its Big Data
capability.

59. Summary

60. Versatility of the Graph Database
Pattern matching
• Recommendation Engine
• Fraud Detection
Multi-degree relationship
• Cyber attack detection
Sequence analysis
• Gene project
• NLP

61. Versatility of the Graph Database
Centrality
• Network analysis
Network dependency
• Network monitoring
Geospatial
• Logistics
• Infra-structure

62. Conclusion

63. Conclusion - Big Data
 Big Data strategies of global leading companies.
 New insights are discovered in the connected data.
 Fabricating Big Data into connected data is the cutting edge technology.
 Graph database is the driver for sustainable growth in the Era of Big Data.

64. Conclusion - Graph Database
 Graph Data is already prevailing among the global leading companies.
 Graph Database will pass the dawn of standards.
 The most widely adopted method will be the Hybrid Database.
 Each company needs to prepare for the wave of change.
 AgenGraph will support your business with superior capabilities.

65. Thank you!