Graph Database in Silicon Valley Meetup on 9/8/2016 How to Implement Project Management Data into Graph Database Cypher Query Language

1. How to implement Project Data into
Graph Database

2. (graphians)-[:MEETUP]->(GDBinSV)
meetup.com/Graph-Database-in-Silicon-Valley/
Joshua BAE, Organizer
joshuayb@gmail.com

3. Agenda
• 6:30 - 7:00 Food, refreshments, and mingling
• 7:00 - 7:10 Introduction of the Meetup
• 7:10 - 7:35 A Talk on Graph DB
- History and Status of Graph Databases
- NoSQL Database Summary
• 7:35 - 7:45 How I implemented project data using Graph DB
• 7:45 - 8:25 Discussion
- The Graph DB awareness and Market trend
- Graph DB use cases in the businesses
- Suggestions on the Meetup
• 8:25 - 8:30 Closing
• The 2nd Meetups schedule

4. ( Bitnin)-[:Sponsors]->(GDBinSV)

8. There are approximately 260,000 cell site towers across the US. This chart displays the number of cell towers
per state.

9. TX
25,969
CA
21,835
UT
1,703
Interestingly, there are more cell sites in Texas than in California while California has about 12 million more in
population.

10. The image shows a simplified network upgrade process.
This cell site management is one of the
largest business sectors in the
telecommunications industry.
The carriers are maintaining and upgrading
the cell sites to meet the demand of
mobile users.

11.  To see if the graph database could be helpful for the project management in the
Network upgrade process.
 To see if a new dimensional insight can be retrieved from the linear processes by
using the Graph Database.
 The first step is to import the process data into graph database by creating nodes
and relationships.
(Just for your reference, all the names are randomly generated via fakenamegenerator.com, and this
example does not reflect the actual businesses.)
The goal of implementing the data in to Graph DB

12. Internal Resource Nodes
Cell Site Nodes Cluster Nodes
Construction Vendor Nodes

13. (vendor)-[:ASSIGNED_TO]->(site)
(resource)-[:INCHARGEOF]->(site)
(site)-[:BELONGS_TO]->(cluster)
Every cell site is managed by 3+ internal
resources. A Site Acquisition Manager, An RF
Engineer and a Construction Manager.

14. The traditional DBMS’s have limits in identifying dynamics between the entities when they handle the multiple
relationships across the entities. Imagine how many tables and joins will be required to return a single result.

15. A Situation
One construction manager reported that a vendor
named ‘Castle’ is underperforming.
 Due to the delay of the site id ‘CA164’, the entire
schedule of the ‘Fresno’ Cluster launching might be
affected.

16. An Intuitive approach to the
problem.
Look up the other sites
which belong to this cluster
and see if the other vendors
who are assigned to those
sites have enough crew to
handle this matter.
 ‘CA612’ and ‘CA149’ are
assigned to ‘Maryland’
who has indicated good
performance.
 Both sites are under the
management of the same
internal resource group.
 A less hectic period
when it is reassigned to
another vendor.

17. Look up the other sites which belong to this cluster and see if the other vendors
who are assigned to those sites have enough crew to handle this matter.

Converting the request into a Cypher Query

18. ‘Maryland’ has been assigned to the most sites in ‘Fresno’ cluster. If you want to check this result in
a visual way, the query can be modified slightly by omitting the aggregate function as below.

19. To check this result in a visual way, the query can be modified slightly by omitting
the aggregate function as below.
Look up the other sites which belong to this cluster and see if the other vendors
who are assigned to those sites have enough crew to handle this matter.


20. The same result in a Graph.

21. State
License {expiration:‘mm/dd/yyyy’}
This is a simplified model. Most of the clusters consist of between 30~40 cell sites. It takes even more
considerations in reality. The contractor should hold the valid licenses which are issued by the State
government boards.

22. Conclusion
 Project management is all about time and resource management.
 Graph Database could be utilized in process control and decision makings.
 The result of Graph DB query can be shared with the stakeholders more
intuitively where every time-sensitive decision counts.
 Less chance of biased opinions thanks to the clear visibility.
 The traditional DBMS’s are not designed to handle these types of matters.

23. (graphians)-[:SELECT]->(topics)

24. Discussion
 The Graph DB awareness and Market trend
 Graph DB use cases in the businesses
 Suggestions on the Meetup

25. Thank you!
See you at the next Meetup.