Software engineers at the University of Kansas have developed SmartXAutofill,
an intelligent data entry assistant for predicting and automating
inputs for eXtensible Markup Language (XML) and other text forms
based on the contents of historical documents in the same domain.
SmartXAutofill utilizes an ensemble classifier, which is a collection
of a number of classification algorithms where each individual internal
classifier predicts the optimum value for a particular data field.
As the system operates, the ensemble classifier learns which individual
internal classifier works better for a particular domain and adapts
to the domain without the need to develop special classifiers. The
ensemble classifier has proven that it performs at least as well
as the best individual internal classifier. The ensemble classifier
contains a voting and weighting system for inputting values into
a particular data field.
Because the existing technology can predict, suggest
and automate data fields, the investigator tested whether the same
technology can be used to identify incorrect data. Given existing
data transmitted by sensors and other instruments, the investigator
studied whether the ensemble classifier technology can identify
data abnormalities and correctness in future sensor data transmission.
The solution would be applied in a project funded by the National
Science Foundation, Polar Radar for Ice Sheet Measurements (PRISM),
using innovative sensors to measure the thickness and characteristics
of the ice sheets in Greenland and Antarctica, with the goal of
understanding how the ice sheets are being affected by global climate
PRISM sensors continuously send information that
is collected and catalogued. The ensemble classifier will check
the data for correctness by predicting which values should be there,
and if the actual values are different, it will flag the data as
possibly corrupted, and allow an operator to later study it and
determine if it is correct or not. This technology will allow the
PRISM intelligent systems to automatically determine the correctness
of sensor and other data, and contributes to the PRISM project by
adding a level of intelligence and prediction to the sensor suite.
Hurricanes, tornados, thunderstorms, and other natural disasters
can have many devastating outcomes. Global warming, the prime investigated
natural disaster of the PRISM (Polar Radar for Ice Sheet Measurements)
project, has a tremendous effect on the sea level rise. Scientists
and researchers have theorized that the excess water is being allocated
from the polar ice sheets of Greenland and Antarctica due to the
long-term results of global warming; however, there are few resources
to confirm the gain or loss ice.
and researchers of the PRISM project have applied their expertise
on teams based on the areas of robotics, communications, intelligent
systems, and radar. These areas were essential in measuring the
ice thickness an determining the bedrock below the ice sheets in
Greenland and Antarctica. In this research, the investigator worked
with the Intelligent Systems team by learning the messaging patterns
between the data producing agents and the requesting agents. The
investigator also created Unified Modeling Language (UML) class
diagrams of a messaging subsystem to represent the collaboration
and communication between these two types of agents using eXtensible
Markup Language (XML) with the Foundations for Intelligent Physical
Agents (FIPA) standard codes. The class diagrams assisted scientists
and researchers in planning new features for the multi-agent system.