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semantics and are sensitive to environment
dynamics.
To achieve proactive behavior, the proposed
architecture includes models of the processes behind
the moving objects. The prototype application uses
workflows to model truck trips. To monitor moving
objects, the architecture includes support for real-
time trajectory data stream processing. Finally, to
account for trajectory semantics and support
sensitivity to environment dynamics, the architecture
features additional data sources, classified as
(geospatial) static structured data sources (SSD
sources) and dynamic structured data sources (DSD
sources). The prototype application uses geospatial
databases and georeferenced facts posted in feeds
and tweets about the road conditions that may affect
the predicted behavior of the trucks.
The contributions of the paper are therefore
threefold: a discussion of the basic requirements for
proactive monitoring applications; a proposal for an
architecture for such applications; and a prototype
application to assess the proposed architecture. The
central argument is that proactive monitoring based
on process models, such as workflows, is a
promising strategy to enhance applications that
control moving objects.
The rest of the paper is organized as follows.
Section 2 describes a motivating scenario. Section 3
discusses basic requirements for proactive
monitoring. Section 4 introduces an architecture for
proactive monitoring applications. Section 5
presents a prototype application to validate the ideas.
Section 6 discusses related work. Finally, Section 7
contains the conclusions.
2 A MOTIVATING APPLICATION
Consider an application to monitor a fleet of
delivery trucks, abstractly defined as follows.
Each truck is modeled as a moving object M and
each trip is described as a workflow W
M
that defines
the customers to be serviced in the trip and the
routes to be followed. Each step p of W
M
either
represents delivering merchandize at a customer C
p
located at place L
p
, or moving from a place O
p
,
called the origin of p, to a place D
p
, called the
destination of p, through a route R
p
.
For each moving object M, the system receives a
data stream containing the date, time, geographic
position and speed. The system transforms this raw
data into meaningful events with the help of a
geospatial database storing the location of points-of-
interest.
The application monitors several trucks, sharing
the same underlying road network and the same
emergency workflows. A centralized application is
desired to integrate the monitoring of the individual
trucks, as well as of the events that affect the road
network where the trucks move. The application also
reduces human interference on the monitoring
process to minimize failures due to fatigue.
Consider now the problem of improving the
truck monitoring application to become proactive
and sensitive to the environment.
Briefly, the first change in the application design
is to use the truck delivery workflows to infer their
future behavior. The second change is to detect
anomalies in the conditions of the roads where the
trucks are expected to drive in the next steps of their
trips (defined by their workflows). As an example,
the system may issue an alert to the driver to
proceed more carefully (or even to take an alternate
route) when detected that a vehicle, carrying a
flammable load, is driving along a road with wet
floor ahead.
Finally, we note that we may describe similar
scenarios related to other classes of moving vehicles,
such as planes and ships. Workflows in this case will
be abstractions for flight or sailing plans.
3 PROPOSED ARCHITECTURE
Figure 1 illustrates the proposed architecture. The
Proative Central Monitor (PCM) is the core
component that, as the name implies, coordinates the
other components to pro-actively monitor moving
objects. The Planning Manager (PM) stores and
controls the workflows that model the behavior of
the moving objects. The Application Databases
contain auxiliary data such as names and addresses
of customers, the road network, etc. The Moving
Objects Monitor (MOM) sends to the PCM the
structured data stream containing information
relative to the real-time monitoring of moving
objects: position, trajectory semantic data (i.e.,
interpreted trajectory data) and other signals from
moving objects. The Mediators facilitate access to
either dynamic or static external data sources.
4 A PROTOTYPE APPLICATION
This section outlines some of the features of a
prototype application to monitor a fleet of delivery
trucks, along the lines of the application presented in
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