Presentation given as part of the 7th biennial Conference on Innovative Data Systems Research (CIDR 2015) gong show.

1. The Big Data – Same Humans
Problem
Alexandros Labrinidis
Advanced Data Management Technologies Lab
Department of Computer Science
University of Pittsburgh
CIDR 2015 – Gong Show – January 5, 2015

2. 2
You know Big Data is an
important problem if...
• It is featured on the cover of Nature and the
Economist!

3. 3
You know Big Data is an even
more important problem if...
• It has a Dilbert cartoon!

4. What is Big Data?
Definition #1:
• Big data is like teenage sex:
o everyone talks about it,
o nobody really knows how to do it,
o everyone thinks everyone else is doing it,
o so everyone claims they are doing it...
Definition #2:
• Anything that Won't Fit in Excel!
Definition #3:
• Using the Vs
4

5. If you have been living under a rock
for the last few years
5
The three Vs

6. • Volume - size does matter!
• Velocity - data at speed, i.e., the data
“fire-hose”
• Variety - heterogeneity is the rule
6
The three Vs

7. 7
Five more Vs
• Variability - rapid change of data characteristics
over time
• Veracity - ability to handle uncertainty,
inconsistency, etc
• Visibility – protect privacy and provide security
• Value – usefulness & ability to find the right-needle
in the stack
• Voracity - strong appetite for data!

8. 8
Enter Moore’s Law
[ Wikipedia Image ]
Moore's law is the observation that, over the
history of computing hardware, the number of
transistors in a dense integrated circuit doubles
approximately every two years. The law is
named after Gordon E. Moore, co-founder of
Intel Corporation, who described the trend in
his 1965 paper.
Source: http://en.wikipedia.org/wiki/Moore's_law

9. 9
Enter Bezos’ Law
Photo: http://www.slashgear.com/google-data-center-hd-photos-hit-where-the-internet-lives-gallery-17252451/
Bezos' law is the observation that, over
the history of cloud, a unit of computing
power price is reduced by 50% approximately
every 3 years
Source: http://blog.appzero.com/blog/futureofcloud

10. 10
Storage capacity increase
0
1000
2000
3000
4000
5000
6000
7000
HDD Capacity (GB)
[ Wikipedia Data ]
Insert other exponentially increasing graphs here
(e.g., data generation rates, world-wide smartphone access rates,
Internet of Things, …)

11. 11
But
• Human processing capacity remains
roughly the same!

12. 12
We refer to this as the:
Big Data – Same Humans
Problem

13. Rethinking
13
Systems point of view Human point of view
• Response time
• Throughput
• Scale-up
• Scale-out
• Making sure humans
do not get lost in a
sea of data!
Scalability

14. Rethinking Scalability
14
Systems point of view Human point of view
• Fantastic! • Terrible!
Example:
Data Stream Management System processing
1,000,000 events per second

15. So now what?
• In addition to “traditional” scalability, it is
important to consider “big data” research
in:
o Summarization
o Personalization
o Ranking
o Recommender Systems
o Visual Analytics
o Crowd-sourcing
o …
15

16. It is important!
16

17. 17

18. Thank you
18
Further reading:
Big Data and Its Technical Challenges
http://bit.ly/bigdatachallenges
By Jagadish, Gehrke, Labrinidis, Papakonstantinou,
Patel, Ramakrishnan, and Shahabi,
Communications of the ACM, July 2014