This document outlines a framework for qualitative causality that combines data-driven and hypothesis-driven analysis. It describes using events extracted from multiple data streams to form qualitative causal patterns and models. An example application to asthma management is discussed where temperature, pollution, wind and other factors are analyzed for patterns related to asthma outbreaks. The framework allows gaining insights from both known and unknown questions by analyzing event patterns and hypotheses.

1. Laleh Jalali, lalehj@ics.uci.edu
Ramesh Jain, jain@ics.uci.edu
University of California, Irvine

2. Outline
• Correla(onal
and
causal
models
• Qualita(ve
Causality
• A
framework
for
Qualita(ve
Causality,
combining
data-­‐driven
and
hypothesis-­‐driven
analysis
• Asthma
Management
applica(on
• Conclusion
2

3. REALITY
DATA
MODEL
Modeling
Explain , Prevent , Understand
Predict
ABSTRACION
Reality, Data, Abstraction, and Model
[Sensors, Web2.0,
Infrastructures, etc.]
[Conceptual,
Mathematical,
Graphical, Statistical,
etc.]
3

4. Good Insight = Induction + Deduction
Correlational Model
4

5. Spurious
Correla(ons
5

6. Spurious
Correla(ons
6

7. Good Insight = Induction + Deduction
Causal Model
7

8. Qualitative Causality
8
"

9. Qualita(ve
Physics
9

10. Events: Building Blocks of Qualitative
Causality
• Asynchronous,
heterogeneous
,
mix-­‐modality
sensory
data
streams.
• Fuse
data
streams
into
a
human-­‐centric
abstrac(on
signal:
Event Streams
10
Different observation
sources help in
recognition and
interpretation of events.
Chronicle"

11. Qualitative Causality (Example)
Qualitative Causal patterns:
1) Pollution increases suddenly
followed by high wind while
temperature increases slightly will
cause an asthma
Outbreak within 2 days.
2) Thunderstorm followed by
temperature decreases steadily
will cause an asthma outbreak
within 1 day.
Temperature
Rain
Wind Speed
PM2.5
11

12. Real World vs. Cyber World
Events
Objects
Abstracted
Cyber
Space
Physical
World
Schema
Properties
{…..}
Model
Sensors
01101011001001101011
010010001110
111010
10
01101011001001101011
010010001111011
11101000011
1001011
10
e6
e1 e2
e4
e5
e7
e8
12

13. Event
Co-
occurrence
Detection
High level
Pattern
Formulation
Pattern
Occurrence
Detection
…
Data Streams
Event Streams
While pollen is high,
person starts exercise,
within T time units she
gets asthma attack
((exercise ;ωT asthma_attach ) ||
high_pollen )
Semi-interval Event Sequences
Qualitative Causal Modeling Framework
Analyzing and
answering questions
you know to ask. the
“known unknowns”
problem
Gaining insights when
you don’t know what
questions to ask. the
“unknown unknowns”
problem
Data-Driven Analysis
Hypothesis-Driven Analysis
13

14. Cause - Effect Pattern Structure
event1
event2
event3
event4
Effect
Time lag
between events
Sequence of events
Events in
parallel
Cause 1!
Cause 2!
Cause 3!
(No medication ; Exercise) è Asthma attack
(Exercise Pollen high) è Asthma attack
(Exercise (Pollen high | pollution high)) è Asthma attack
(Exercise Pollution high) è Asthma attack
Exercise è Asthma attack
Δt!
14

15. Temporal Relations
Interval : [∂, ts, te]
∂.ts = ∂+ and ∂.te = ∂ ͞
Semi-interval : [∂+/-, t]"
Order!
Concurrency!
15

16. Event Model
:
An
interval
series
with
an
ordered
set
of
events
ES(i) = {e1
(i),
e2
(i),…, en
(i)}
where
ek ∈ { pE, iE, sE } (1≤ k ≤ n) .
Ʃ(i)
is
the
alphabet
for
event
types
and
| Ʃ(i) | denotes
the
number
of
event
types
in
an
event
stream.
:
An
interval
sequence
ES = {ES(1) , ES(2) , … ES(|I|)} that
is
a
combina(on
of
mul(ple
event
streams
and
Ʃ = { Ʃ (1) U Ʃ (2) U … U Ʃ (|I|)}.
e = ‹v, [E, ts, te] ›
e = ‹v, [E+/-, t] ›
e =‹v, [E, t] ›
16

17. Pattern Operators
Filters pattern expressions on predicate P, where P refer to event attributes contained in the pattern. "
Detects if pattern expression ρ1 is followed by pattern expressions ρ2.
Detects if pattern expression ρ1 is followed by pattern expressions ρ2 within Δt time units.
Detects multiple patterns occur in parallel. Any order is allowed. There has to be a non-empty
overlap among all the patterns.
Detects if any of the pattern expressions ρ1 to ρk matches the input event stream.
This operator requires a pattern ρ to occur within a certain time interval Δt = [δ1, δ2].
This operator computes if pattern ρ2 is co-occurring with pattern ρ1 within Δt time interval.
17

18. Quantitative Processing Technique
Pattern Example: "
18
Automaton:
A
finite-­‐state
automata
(FSA)
is
a
5-­‐tuple
(OS,
TS,
E,
s0,
sf),
consis(ng
of
a
finite
set
of
ordinary
states
(OS),
(me
states
(TS),
transi(ons
between
states
(Ed),
a
start
state
s0
∈
OS,
and
a
final
or
acceptance
state
sf
∈
OS.

19. Quantitative Processing Technique
Operation
Computational Automaton
Selection
Sequence
Conditional
Sequence
Alternation
Operation
Corresponding Automaton
Concurrency
Time
19

20. Bring the Data to Life for Human-Driven
Analysis
Data-Driven
Hypothesis-
Driven
20

21. Asthma Risk Factor Recognition
21

22. Event Stream Modeling
22
• Apply Symbolic Aggregate
approXimation (SAX)
algorithm with 3 symbols on
time-series data.
a
b
c
• Define meaningful events
for each SAX code.

23. Data
23
PM2.5
Temperature
Rain fall
Wind speed
Tweets with
asthma topic
18 months, from 2013-July-01 to 2014-Dec-30, in Tokyo

24. Results
24
Temperature fluctuation has the most impact in the fall and winter
seasons and it is not a risk factor during spring or summer
During spring and summer, when rain suddenly increases to a very
high level, an asthma outbreak is more probable
The effect of PM2.5 is not noteworthy
in the fall and winter seasons.

25. Results (Cont.)
25
• When PM2.5 increases followed by temperature stay high within 3
days, then asthma outbreak is probable.
• When wind decreases followed by PM2.5 increases within 5 days,
then asthma outbreak is probable.
• When rain increases followed by PM2.5 stay low within 4 days then
an asthma outbreak is probable.

26. Summery
• Using
qualita(ve
analysis
which
combine
induc(ve
and
deduc(ve
can
result
in
more
useful
and
effec(ve
models
while
spurious
correla(ons
are
eliminated.
• Mul(ple
sensors
can
be
combined
by
iden(fying
a
unified
framework
along
the
dimension
of
events
that
converts
a
quan(ta(ve
problem
to
a
qualita(ve
one
that
humans
understand.
26