The integration of emerging data manipulation technologies has enabled a paradigm shift in practitioners' abilities to understand and anticipate events of interest in complex systems. Example events of interest include outbreaks of socio-political violence in nation-states. Rather than relying on human-centric modeling efforts that are limited by the availability of SMEs, automated data processing technologies has enabled the development of innovative automated complex system modeling and predictive analysis technologies. We introduce one such emerging modeling technology - the sequential pattern methodology. We have applied the sequential pattern methodology to automatically identify patterns of observed behavior that precede outbreaks of socio-political violence such as riots, rebellions and coups in nation-states. The sequential pattern methodology is a groundbreaking approach to automated complex system model discovery because it generates easily interpretable patterns based on direct observations of sampled factor data for a deeper understanding of societal behaviors that is tolerant of observation noise and missing data. The discovered patterns are simple to interpret and mimic human's identifications of observed trends in temporal data. Discovered patterns also provide an automated forecasting ability: we discuss an example of using discovered patterns coupled with a rich data environment to forecast various types of socio-political violence in nation-states

Rohloff, Kurt

NASA Technical Reports Server

205
Automated Discovery and Modeling of Sequential
Patterns Preceding Events of Interest
Kurt Rohloff
BBN Technologies
Cambridge MA, 02138
krohloff@bbn.com
Abstract. The integration of emerging data manipulation technologies has enabled a paradigm
shift in practitioners' abilities to understand and anticipate events of interest in complex systems.
Example events of interest include outbreaks of socio-political violence in nation-states. Rather
than relying on human-centric modeling efforts that are limited by the availability of SMEs,
automated data processing technologies has enabled the development of innovative automated
complex system modeling and predictive analysis technologies. We introduce one such
emerging modeling technology - the sequential pattern methodology. We have applied the
sequential pattern methodology to automatically identify patterns of observed behavior that
precede outbreaks of socio-political violence such as riots, rebellions and coups in nation-states.
The sequential pattern methodology is a groundbreaking approach to automated complex system
model discovery because it generates easily interpretable patterns based on direct observations
of sampled factor data for a deeper understanding of societal behaviors that is tolerant of
observation noise and missing data. The discovered patterns are simple to interpret and mimic
human's identifications of observed trends in temporal data. Discovered patterns also provide an
automated forecasting ability: we discuss an example of using discovered patterns coupled with a
rich data environment to forecast various types of socio-political violence in nation-states.
INTRODUCTION
A major challenge in analyzing complex
systems is identifying patterns of behavior
which are symptomatic precursors to Events
of Interest (Eols) such as onsets or
terminations of socio-political violence in
nation-states. By identifying patterns of
behavior that precede Eols such as onsets
or terminations of socio-political violence in
nation-states we can begin to both
understand the underlying causal structures
which drive these events to occur and
consequently forecast these events.
In this paper we discuss a generalizable
sequential pattern concept based on the
supposition that the phenomena which
cause (or at least are related to) the
occurrences of Eols exhibit similar
symptomatic behaviors across multiple Eol
occurrences. For example, countries
experiencing rebellions driven by the desire
for freedom by internal ethnic groups
commonly exhibit increasing ethnic tension
and violence before the occurrence of ethnic
rebellions. We formalize our sequential
pattern concept using a finite-state machine
model of countries' behaviors and use
collections of sampled factor data to define
the "states" of a complex system such as a
country.
The sampled factor data represents
quantifiable measurements of systems such
as countries at discrete, regular points in
time. We use a discrete clock-tick formalism
to model the updating of state locations.
Example factors from our socio-political
domain include GOP, the rates of
occurrence of various words in the national
press, the average caloric intake, Goldstein
measures of conflict/cooperation between
governmental entities, etc. These example
factors change continuously over time which
motivates our use of sampled data. We
map the sampled factor data to observed
"trends" in this factor data where a factor's
sampled measurement can be increasing,
decreasing, or fluctuating over either the
short-term or the long-term.
Although there are numerous published
works on pattern discovery, the innovation in
our approach to pattern discovery comes
from our handling of approximate matches
necessitated by noisy data in an application
context where rigorous matching may not
https://ntrs.nasa.gov/search.jsp?R=20100012853 2019-08-29T18:56:07+00:00Z
206
always be relevant. In particular, we have
developed technology to:
• Define loose matching of observed
data trends as part of pattern
discovery and matching.
• Numerically optimize pattern
matching parameters that are Eol-
independent for improved
forecasting.
• Discover an algorithm to quickly
identify loosely matching patterns
Taken together, these innovations enable
our pattern discovery and forecasting
approach.
In our motivating context of socio-political
violence we are interested in patterns that
match the trends of observed behaviors
preceding at least two instances of Eol
occurrences and which are not present in
countries when an Eol does not occur over
historical data. We have a generalizable,
computationally efficient branch-and-bound
back-chaining method to identify the set of
factors which define a state space in
patterns that match the behavior preceding
Eol occurrences in at least two countries
from historical data. The backwards
chaining methodology permits us to identify
which factors change similarly for multiple
countries for several time steps leading up
to the socio-political violence onset or
termination in selected countries in a
computationally efficient manner.
As a result of our hypothesis that the
phenomena which cause (or at least are
related to) the onsets and terminations of
socio-political violence exhibit similar
symptomatic behaviors across multiple
onsets and terminations of socio-political
violence, we can generate real-time early-
warning forecasts of Eols if early portions of
the patterns are observed in a specific
country. This forecasting process is based
around the notion of matching a country's
behavior to early parts of historical patterns.
If the country's behavior matches the early
parts of the pattern then we forecast that
onsets or terminations of socio-political
violence will occur in the country in the near
future. We found that this approach to
forecasting using single patterns is
inadequate in practice because individual
patterns provide a limited representation of
the full breadth of all possible behaviors that
may precede onsets and terminations of
socio-political violence. This motivates our
need to generate libraries of patterns that
provide a broader representation of the
observed preceding dynamics associated
with the occurrence of onsets and
terminations of socio-political violence.
We demonstrate our pattern discovery and
forecasting methodologies over data of
onsets and terminations of ethnic-religious
violence in Pacific-region countries from
1998-2006. We show that by discovering
patterns for ethnic-religious violence onset
and ethnic-religious violence termination
over Pacific-region countries from 1998-
2004, we can use these patterns to forecast
ethnic-religious violence onset and ethnic-
religious violence termination over 2005-
2006 with a very low false-alarm rate.
Previous versions of our pattern discovery
approach are presented in [2], [3]. A more
in-depth version of the work presented in
this paper is provided in [4]. An introduction
to our underlying rich data environment to
support the experiments discussed here is
provided in [1], [5].
PATTERNS
We define our patterns to be sequences of
trends of behaviors observed in factors
before Eols. We look for trends in sampled
factor data where the sampled factors either
increase, decrease or fluctuate over either
the short-term or the long-term. For
instance, in India in the quarters preceding
the onset of ethnic-religious violence in early
2002, we see that the level of cooperating
expressed by the government towards
opposition parties (as measured by a
Goldstein metric) holds fluctuating for
several quarters before increasing over a
short term and then decreasing shortly
before the onset of violence.
With this in mind, we formally define the 6
possible types of trends that can be
observed in factors as:
• Long-Term Increasing
• Short-Term Increasing
• Long-Term Fluctuating
• Short-Term Fluctuating
• Long-Term Decreasing
• Short-Term Decreasing
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Our definition of patterns around these
observed factor trends is one of our
innovations in our pattern definition. This
approach to pattern definition allows for the
loose matching of patterns to observed
factor data. This loose matching procedure
is generally simple for humans, but
exceedingly difficult to automate in a
computation environment.
For our application context of socio-political
violence we define short-term trends as
those occurring over 3 quarterly time
samples or less, and long-term trends
occurring over 3 quarterly time samples or
more. We allow the definitions of
increasing, decreasing, and fluctuating to be
system- and factor-specific. In our socio-
political violence context, the definitions of
increasing, decreasing, and fluctuating vary
from country to country and factor to factor.
Our motivation for this intuition is that
"normal" observed factor behaviors change
differently not only from factor to factor (as
may be intuitive because different factors
measure different phenomena), but that
"normal" observed factor behavior varies
from country to country for the same factor.
As an example, any small change in the
level of cooperation expressed by the
Chinese government towards potential
opposition parties is unusual and significant,
but relatively dramatic observed changes in
the level of cooperation expressed by the
Indian government towards opposition
parties is fairly routine.
To map observed changes in factor data to
increasing, decreasing or fluctuating trends,
we use a weighted threshold test based on
the standard deviation of the changes in the
factor over a set of training data. This
parameter is Eol-independent and is our
method for finding this threshold is one of
our other innovations in this pattern
approach. Our general approach to setting
the increasing, decreasing, and fluctuating
thresholds is to find the thresholds that
would result in maximum forecasting
performance over some set of training data.
FORECASTING
After discovering a set of patterns that
precede Eols in complex systems over
some training data, we can use these
patterns to make out-of-sample forecasts for
the Eols over test data. We found that a
relatively simplistic approach to forecasting
is generally very effective - we used a
weighting voting mechanism where the
discovered patterns matched out-of-sample
observations in the test data to generate
forecasts.
To implement our weighted voting
mechanism in our socio-political violence
domain for a given country at a given time,
we determine which patterns match the
observed factor data leading up to that time.
If the number of patterns matching the data
exceeds a voting threshold v, then we
forecast the onset/termination of socio-
political violence in that country at that time.
Similar to the weight threshold for
increasing, decreasing and fluctuating, we
compute v to maximize forecasting
performance over some training data that
wasn't also used for pattern discovery.
EXAMPLES OF FORECASTING ONSET
AND TERMINATION OF ETHNIC-
RELIGIOUS VIOLENCE
Using the trend weight threshold wand the
voting threshold v that maximized the f-
measure of forecasts for the onset of coups
in our training data, we applied our approach
to forecast the onset and termination of
ethnic-religious violence in Pacific-region
countries. We ran this experiment to
forecast the onset and termination of ethnic-
religious violence using a set of quarterly
sampled Goldstein metric factor data that
expressed the relative levels of
conflict/cooperation between political groups
operating in the countries (such as the
government, opposition parties, international
organizations, etc...).
We split our data into training and test sets.
The training data ran from 1998-2004 and
the test data ran from 2005-2006. Over the
training data there were onsets of ethnic-
religious violence in the following countries
at the following times:
• China 01-2004
• India 01-2002
• . Indonesia 01-1999
• Solomon Islands 01-2000
• Solomon Islands 01-2003
• Sri Lanka 01-2003
208
Similarly, there were terminations of ethnic-
religious violence in the following countries
at the following times:
• India 01-2004
• Solomon Islands 01-2001
• Solomon Islands 01-2004
Using these two sets of events and the w =
0.2 threshold, we discovered 55 single-
factor patterns for the onset of ethnic-
religious violence and 19 single-factor
patterns for the termination of ethnic
religious violence. When then used these
patterns to forecast the onset/termination of
ethnic-religious violence using our threshold
voting mechanism..
For the onset of ethnic-religious violence, we
generated the following forecasts:
• India 01-2005
• Nepal 02-2005
• Taiwan 04-2006
Over the 2005-2006 test data, the only onset
of ethnic-religious violence is in India in the
beginning of 2005. There are no true ethnic-
religious violence outbreaks in Nepal or
Taiwan so we generated two false-positive
forecasts. It is interesting to note however,
that in early to mid-2005 in Nepal there was
an uptick in the level of violence associated
with the smoldering Maoist insurgency in
that country.
For the onset of ethnic-religious violence, we
generated the following forecasts:
• \item China 01-2005
• \item Sri Lanka 01-2006
• \item Sri Lanka 03-2006
Over the 2005-2006 test data, the only
termination of ethnic-religious violence is in
China in the beginning of 2005. There are
no true ethnic-religious violence terminations
in Sri Lanka so we generated two false-
positive forecasts. It is similarly interesting to
note however there was a dip in the ongoing
ethnic Tamil insurgency in the beginning of
2006 in Sri Lanka, but this violence picked
up again several months later.
For both forecasting both the onset and
termination of ethnic-religious violence, we
were able to forecast both occurrences of
onset/termination correctly along with a
reasonably low false-positive rate. Because
we were forecasting over 29 countries and
two years, the false-positive rate is
approximately one false alarm every 100
country-quarters.
A RICH DATA ENVIRONMENT TO
SUPPORT PATTERN DISCOVERY AND
FORECASTING
In implementing our socio-political violence
forecasting methodology, our
experimentation environment was supported
through the application of emerging
information technologies to run our pattern
discovery and forecasting methodology. Key
technologies in our environment include
automated data collection, knowledge
representation, model integration and data
visualization. To support our pattern
discovery and forecasting activities we
constructed an end-to-end distributed
knowledge system that supports:
• Automated collection and classification of
unstructured data (such as raw news
feeds and communications data); and
collection of structured data.
• Automated fusing of extracted information
with historical structural and geospatial
datasets using Semantic Web
technologies to support distributed
modeling and analysis.
• Context-dependent data visualizations,
including faceted browsing and spatial-
temporal displays, to reveal underlying
structures, patterns, and correlations.
Our system utilizes technologies for
automated collection and classification of
unstructured data including a set of named
entity, relationship, and event extraction
capabilities that operate over the entire
content of articles and can "learn" or evolve
over time. We applied these technologies to
analyze a voluminous corpus of news feed
data that covered a wide geographic region
over a period of ten years. The extracted
information formed the basis of theory-
based independent variables (such as
general tension metrics, non-state actor
attributes, and leadership characteristics) as
well as augmenting the more stale historical
factors extracted from existing social
science and econometric datasets.
Using Semantic Web technologies, our
knowledge system fuses information
extracted via the natural language tools
209
described with data from numerous social
science datasets to develop a knowledge
environment. This capability also provides a
basis for automated reasoning and inference
for model and analysis results integration.
By directly encoding the semantics typically
stored in dataset codebooks, the system
fuses multiple datasets that goes beyond
superficial dataset alignment (such as
merely sorting data from various datasets by
county and year). Our system's knowledge
infrastructure supports model-agnostic
access to the stored data for further
manipulation and analysis. Modeling and
analysis results can be fed back into the
knowledge system for access by other
models.
The capstone interface of our knowledge
system employs data visualization
techniques to display data analysis results
and provide interactive "drill-down"
capabilities to better study results. Faceted
browsing of factors and patterns based on
these data values allows a user to select
different events and the associated variables
associated with the events in various
countries.
DISCUSSION
The sequential pattern methodology is an
approach to identify temporal patterns of
behavior that precede Eols such as multiple
occurrences of socio-political violence such
as riots, rebellion onset, coups, etc. Our
current development of the sequential
pattern methodology mines regularly
sampled factor data collected from our data
collection processes and aggregated by our
Semantic Web technology. This
methodology identifies commonalities
preceding multiple events of interest
occurrences but not before non-
occurrences. The patterns rely on the pre-
defined of equivalence classes of factor
values. Our latest experimentation with the
sequential pattern methodology relies on
equivalence classes defined by both static
and dynamic quantization operations.
The sequential pattern methodology is a
groundbreaking approach to pattern
discovery because it generates easily
interpretable patterns based on direct
observations of sampled factor data. The
resulting patterns are easily to visually
interpret as timed finite-state-machine
models. Additionally, they permit a clear
audit trail to guide the replication of
discovered patterns and for the forecast of
political instability based on already
discovered patterns. We foresee additional
development of our sequential pattern
technology to natively incorporate discrete-
event behaviors to capture the underlying
"mode-switching" behavior inherent in many
processes.
An important property of our sequential
pattern approach is its generalizability. The
sequential pattern methodology can be
applied in other complex system application
contexts where patterns of behavior in
sampled factor data preceding events needs
to be identified and represented.
REFERENCES
1. Battle, R., D. Reid & K. Rohloff. "CWEST:
Disruptive Integration of Computation
Technology for Data Analysis and
Visualization." Visualizing the Past: Tools
and Techniques for Understanding Historical
Processes, February 2009.
2. Rohloff, K. & A. Victor. "The Identification of
Sequential Patterns Preceding the
Occurrence of Political Events of Interest."
Second International Conference on
Computational Cultural Dynamics,
September 2008.
3. Rohloff, K. & A. Victor. "Computational
Methods to Discover Sets of Patterns of
Behaviors that Precede Political Events of
Interest." AAAI Spring Symposium on
Technosocial Predictive Analytics, March
2009.
4. Rohloff, K. "Trend Pattern Approach to
Forecasting Socio-Political Violence." Third
International Conference on Computational
Cultural Dynamics, September 2009.
5. Rohloff K. and W. Thornton. "A Knowledge
Environment for Social Science Exploration."
Human Behavior-Computational Intelligence
Modeling Conference, June 2009.


Automated Discovery and Modeling of Sequential Patterns Preceding Events of Interest

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