IEEE Technology and Society Magazine - Fall 2014 - 37

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Detect - Vehicles, along with
their speed, are detected in a road
sector and/or at a specific point
through a multi-technology sensor network. This information
is used to recognize traffic flow
and density patterns along the
selected road. Traffic accidents
are also recognized on the basis
of acceleration/deceleration patterns, as well as violations of
road safety (vehicles driving too
close to each other, long vehicles
driving too fast, etc.).
Forecast - Traffic flow, traffic
density, and subsequently duration of travel will be forecast
for different time horizons in
the future. The carbon footprint
(carbon dioxide emissions) and
energy consumption will also
be forecast for different waiting
times (5, 10, 15, and 20 minutes).
Decide - Calculate within 30
seconds the optimal variable
speed limits and duty cycles
for the ramp metering lights.
Act - Change the actual values
of the variable speed limit panels and the operation of lights
on the ramp-metering course.
Actions will be taken in a matter of seconds for ramp metering and a matter of minutes for
the variable speed limits.

Traffic forecasting requires the
analysis of massive data streams
storming from various sensors,
including fixed sensors installed in
highways and mobile sensors, such as
smart phones and GPS traces, as well
as large amounts of historical data.
Similarly, in proactive credit
card fraud management, the goal is
to forecast fraudulent activity and
make decisions in order to prevent
a financial loss. In 2010, fraud in the
Single Euro Payments Area (which
includes 27 EU member states) was
estimated at 1.26 billion Euros [12].
Nevertheless, fraud detection is still
a "needle in a haystack" type of
problem, as fraudulent transactions
constitute at most 0.1 percent of the
total number of transactions, while

new fraud patterns appear on almost
a weekly basis. However, proactive
credit card fraud management can
be adapted as follows:
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Detect - Fraudulent activities,
such the following, must be
detected:
*	 "Cloned card event" - a credit card is being used simultaneously in different countries.
*	 "Risky usage event" - the
card is frequently being used
by a "risky" merchant.
*	 "Potential batch fraud event" -
many transactions from multiple cards are being used
in the same point-of-sale in
high amounts.
Forecast - Recognize fraudulent
activity that has started to take
place but is not yet complete. For
example, forecast with a certain
probability, a "risky usage event,"
when there are several transactions by "risky" merchants in
some specified period of time.
Decide - Decide to block or
review the transaction in less
than a second after the forecast.
Act - Depending on the type
of fraud, add the corresponding credit card to the black/
gray/watch list.

Credit card fraud forecasting
requires analysis of very large,
noisy transaction streams storming
from all over the world, as well as
massive amounts of historical data.

processing, scalable data processing, optimization for decision making, and decision support through
visual analytics. The methodology
needs to be recognized in a distributed system comprising the following elements:
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Real-time event recognition and
forecasting under uncertainty.
Events of special significance
are recognized and forecast,
and then communicated to the
decision-making component.
To enable timely recognition,
move as little data as possible
from one place to another.
Real-time event-based decision
making under uncertainty. The
forecast and recognized events
are leveraged for real-time
operational decision making.
Visual analytics for proactive
decision support. Visualization techniques explain the
decisions made and the possible proactive actions, enabling
human operators to reach and
execute informed decisions.

Fig. 1 illustrates this methodology in the context of proactive
traffic management. The following
sections present in detail the main
aspects of the methodology.

Event Recognition
and Forecasting
Systems for symbolic event recognition [4], also known as event pattern

We are moving to an "intelligent
economy," where we have the
capability not only to access
information, but also to analyze
and act upon it.
Methodology
We propose that a highly synergetic
methodology to proactive eventdriven decision making should be
taken. This can be achieved by combining the research areas of event

IEEE TECHNOLOGY AND SOCIETY MAGAZINE

|

FALL 2014

matching, identify composite events
of interest, which are collections
of events that reveal some pattern.
The "definition" of a composite
event describes temporal, logical,
and possibly spatial constraints on
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