1. Ramesh Jain jain@ics.uci.edu
Events in Data Science
Ramesh Jain

2. Data/Information/Knowledge
Data, Data, everywhere. Not a bit of information to be found.
Data is important, but people care for Information and Knowledge

3. We Live in Dynamic World.

4. Modeling the World
Objects
Events

5. Remember Newton
Statics
Mechanics
Dynamics

6. Big Data
Multimedia
Realtime
Credibility
ICNC 2015 Anaheim, CA.

7. Events take place in the real world.
Events result in Data and Documents

8. Tsunami 2004 Experiential Data Reports on the event
Visualization using data

9. Events are ‘Connectors’ Events create ‘Context’
People
Things
Places
Time
Experiences
Events
Structural
Temporal
Causal
Spatial
DATA MODEL:
To capture events we need a common event model in order to avoid obfuscating event exploration and event-driven access to media.We used E-Model that introduces 6 facets for events (the circle diagram in the next slide) as the initial basis for our data model. We are currently building the formalizations of our data model which is based on this circle diagram. We are formally defining each facet in our data model. Each facet may involve structures and/or induced sub-graphs in addition to the RDF structure. However this is our future work and we will not talk about that here.
Experiential
Informational 9

10. Massive collection of events. each month.
Facebook and Twitter
Reporting events as micro-blogs
Massive collection of events.
each month.

11. Events: A time-indexed database!!!

12. Events usually cause domino effect.
Does the flap of a butterfly’s wings in Brazil set off a tornado in Texas?

13. From Micro-blogs to Situations
From Tweets to Demonstrations

14. Theory of Control and Communication in
Published first in 1942
Theory of Control and Communication in
Animals
Machines
Societies
What is Cyber Space?
Who invented it?

15. Cybernetics: Real Time Feedback Control
Physical
World
And
Information Systems
Environment and Resources
Information
Action Signals
Matching
Personal Situation and Needs
Information

17. Cybernetics: Personal

19. Data Variety: From Data Streams to Abstracted Event Streams
sensors used to gather climate information, posts to social media sites, digital pictures and videos, purchase transaction records, and cell phone GPS signals, human motions, etc…
Not only heterogeneous, and unstructured, but time is a very important dimension.
They all generate Events
What about frameworks that consume events for knowledge discovery?
In particular, the accommodation of time into mining techniques provides a window into the temporal arrangement of events and, thus, introduce the ability to suggest cause and effect that are overlooked when the temporal component is ignored or treated as a simple numerical attribute. Moreover, temporal data mining has the ability to mine the behavioral aspects of a system as opposed to mining rules that describe their states at a point in time. Hence, temporal data mining helps understanding why rather than merely understanding what.
Data Management

20. From data streams to situations
Conditions
Event Streams
Observation Streams
Aggregation and Classification
Understanding
Verification
Low-level Analysis

21. Computational Model of Events.
Event Stream

22. Daily Life Activities: How we enter them?
Chronicle of Daily Life Activities

23. Daily Life Activities: How we use time.
Daily Life Activity
ATUS: American Time Use Survey

24. We collect diverse signals.

25. Personal Event Streams in a Personicle
Food Score
Food
Activity
Activity Score
Emotion
Emotion Score
Medical
Medical Score
Environment
Environment Score
Daily Activity

26. Future Health: Right Moment, Right Place, Right Decision, Right way.
Personalized
Predictive
Precise
Persuasive
Preventive
Right Moment,
Right Place,
Right Decision,
Right way.

27. Your food and activity scores for the last 3 days are on low side.
Coming Soon Near You
Your food and activity scores for the last 3 days are on low side.
Health Butler

28. Current Status
Expect to see first version of Health Butler in 2nd quarter, 2017.