From Internet Usage to Genomics Analysis

with Google BigQuery

Mr. Kanakorn Horsiritham

Prince of Songkla University, THAILAND

Agenda

Big Data

Big Data Analysis

Big Data Tools

Google BigQuery

Prince of Songkla University

Analysis

Google Genomics

Conclusion

Big Data

http://www.viawest.com/sites/default/files/asset/document/ViaWest_Big_Data_Infographic.pdf

(2011)

Source: http://www.intel.com/content/www/us/en/communications/internet-minute-infographic.html

(2013)

Big Data

Big Data Analysis The process of examining big data to uncover

hidden patterns

unknown correlations

useful information

Method

Machine Learning

Data Visualization

Etc ...

http://www.sas.com/en_us/insights/analytics/big-data-analytics.html

Big Data Tools

Tools:

- Hadoop

- Google BigQuery

Hadoop

http://blog.agro-know.com/wp-content/uploads/2015/07/Hadoop_Ecosystem.jpg

Google BigQuery

Google BigQuery

Google Cloud Storage

SQL API

Corperate Data

BI Tools

Google Sheets

Co Worker

Store Data in the cloud Analyze interactively Securely Share

Distribute the Result

Google BigQuery

https://cloud.google.com/bigquery/pricing

Free

Loading/Export Data

Storage Price

$0.02/GB/Month ($20/TB/Month) Storage pricing is prorated per MB, per second.

For example, if you store 1 TB for half of a month, you pay $10

Query Price

First 1 TB of month is FREE

$5/TB

Google BigQuery

Only $7 / Month

Internet Usage Prince of Songkla University

Now, we are here !

Prince of Songkla University

Hadyai

Pattani

Trang

Phuket

Surat

Prince of Songkla University

5 Campus

Credit: Facebook "Care Chayada"

Credit: Facebook "Care Chayada"

Schema

Analysis

How each application uses internet traffic?

How each application uses internet traffic?

How each application uses internet traffic?

What are the URL Category of those SSL Applicaiton?

What are the URL Category of those SSL Applicaiton?

What are the URL Category of those SSL Applicaiton?

How these top 5 applications are used in each hour of day?

How these top 5 applications are used in each hour of day?

How these top 5 applications are used in each hour of day?

How users use internet during a week?

How users use internet during a week?

How users use internet during a week?

How users use internet in week day and weekend?

How users use internet in week day and weekend?

How users use internet in week day?

How users use internet in weekend?

What are the most popular social networking?

What are the most popular social networking?

What are the most popular social networking?

How different do students and staffs use social

networking?

How different do students and staffs use social networking?

How different do students and staffs use social networking?

Which Faculty their students use 'Reference and Research' category?

Google Genomics

Google Genomics

Genomic Browser http://gabrowse.appspot.com/

Genomic Browser http://gabrowse.appspot.com/

VCF Format

Upload into Google Cloud Storage

Google Genomics API

Export to Google BigQuery

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

0 50000000 100000000 150000000 200000000 250000000 300000000

titv

titv

Conclusion

Conclusion

Google BigQuery

• easy to use and not expensive. Just prepare you data, upload and then query

• Process large data within a few seconds

PSU Internet Usage Analysis

• Heavy traffic : video streaming, i.e. youtube and other in the form of web browsing

• When count with number of users, Web browsing and Facebook are used by most users.

Conclusion

• Internet usage peak on workday by office hours and fade down after office hours and weekend

• Staff use internet weekday by office hours and other used by student alone.

• Top 3 Social Networking are Facebook, LINE and Twitter. Both staff and

student use Facebook the most but staff like twitter more than LINE while

student like LINE more than Twitter.

• And Faculty of Science, Mangement Science and Engineering user internet

for Education Purpose the most.

Conclusion

Google Genomics

• Upload the VCF files created by DNA Sequencer to Google Cloud Storage

• Process with Google Genomics

• Export to Google BigQuery

• Then query the data set