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Invited
Speakers
In
addition to the oral presentation of accepted papers, there will be four
invited speakers:
Invited
Speaker Bio-Sketches and Abstracts:
Dr.
Ashok N. Srivastava,
is the Project Manager for the System-Wide Safety and Assurance Technologies
Project at NASA. He is formerly the Principal Investigator for the Integrated
Vehicle Health Management research project at NASA. His current research
focuses on the development of data mining algorithms for anomaly detection
in massive data streams, kernel methods in machine learning, and text
mining algorithms. Dr. Srivastava is also the leader of the Intelligent
Data Understanding group at NASA Ames Research Center. The group performs
research and development of advanced machine learning and data mining
algorithms in support of NASA missions. He performs data mining research
in a number of areas in aviation safety and application domains such as
earth sciences to study global climate processes and astrophysics to help
characterize the large-scale structure of the universe.
Greener Aviation with the Virtual Sensors: a Case Study
Abstract:
The environmental
impact of aviation is enormous given the fact that in the US alone there
are nearly 6 million flights per year of commercial aircraft. This situation
has driven numerous policy and procedural measures to help develop environmentally
friendly technologies which are safe and affordable and reduce the environmental
impact of aviation. However, many of these technologies require significant
initial investment in newer aircraft fleets and modifications to existing
regulations which are both long and costly enterprises. We demonstrate the
use of an anomaly detection method based on Virtual Sensors to help detect
overconsumption of fuel in aircraft which relies only on the data recorded
during flight of most existing commercial aircraft, thus significantly reducing
the cost and complexity of implementing this method. The Virtual Sensors
developed here are ensemble-learning regression models for detecting the
overconsumption of fuel based on instantaneous measurements of the aircraft
state. This approach requires no additional information about standard operating
procedures or other encoded domain knowledge. We present experimental results
on three data sets and compare five different Virtual Sensors algorithms.
The first two data sets are publicly available and consist of a simulated
data set from a flight simulator and a real-world turbine disk. We show
the ability to detect anomalies with high accuracy on these data sets. These
sets contain seeded faults, meaning that they have been deliberately injected
into the system. The second data set is from real-world fleet of 330 jet
aircraft where we show the ability to detect fuel overconsumption which
can have a significant environmental and economic impact. To the best of
our knowledge, this is the first study of its kind in the aviation domain
Dr.
Hillol Kargupta is
a Professor of Computer Science at the University of
Maryland, Baltimore County. He is also a co-founder of AGNIK, a vehicle
performance data analytics company for mobile, distributed, and embedded
environments. He received his Ph.D. in Computer Science from University
of Illinois at Urbana-Champaign in 1996. His research interests include
mobile and distributed data mining. Dr. Kargupta is an IEEE Fellow. He
won the IBM Innovation Award in 2008 and a National Science Foundation
CAREER award in 2001 for his research on ubiquitous and distributed data
mining. He and his team received the 2010 Frost and Sullivan Enabling
Technology of the Year Award for the MineFleet vehicle performance data
mining product and the IEEE Top-10 Data Mining Case Studies Award. His
other awards include the best paper award for the 2003 IEEE International
Conference on Data Mining for a paper on privacy-preserving data mining,
the 2000 TRW Foundation Award, and the 1997 Los Alamos Award for Outstanding
Technical Achievement. His dissertation earned him the 1996 Society for
Industrial and Applied Mathematics annual best student paper prize.He
has published more than one hundred peer-reviewed articles. His research
has been funded by the US National Science Foundation, US Air Force, Department
of Homeland Security, NASA and various other organizations. He has co-edited
several books. He serve(s/d) as an associate editor of the IEEE Transactions
on Knowledge and Data Engineering, IEEE Transactions on Systems, Man,
and Cybernetics, Part B and Statistical Analysis and Data Mining Journal.
He is/was the Program Co-Chair of 2009 IEEE International Data Mining
Conference, General Chair of 2007 NSF Next Generation Data Mining Symposium,
Program Co-Chair of 2005 SIAM Data Mining Conference and Associate General
Chair of the 2003 ACM SIGKDD Conference, among others.
Next
Generation of Machine-to-Machine Environments, and Distributed Data Mining
Abstract:
Next
generation of Machine-to-Machine (M2M) networks will be dealing with billions
of devices connected over wireless networks. Most large wireless network
carriers of the world are now gearing up with major investments in the
M2M world. This talk will focus on data analytics in M2M and use in-vehicle
platforms for illustrations. Modern vehicles are embedded with varieties
of sensors monitoring different functional components of the car and the
driver behavior. With vehicles getting connected over wide-area wireless
networks, many of these vehicle diagnostic-data along with location and
accelerometer information are now accessible to a wider audience through
wireless aftermarket devices. This data offer rich source of information
about the vehicle and driver performance. Once this is combined with other
contextual data about the car, environment, location, and the driver,
it can offer exciting possibilities. Distributed data mining technology
powered by onboard analysis of data is changing the face of such vehicle
telematics applications for the consumer market, insurance industry, car
repair chains and car OEMs. This talk will offer an overview of the market,
emerging product-types, and identify some of the core technical challenges.
It will describe how advanced data analysis has helped creating new innovative
products and made them commercially successful. The talk will offer a
perspective on the algorithmic issues and describe their practical significances.
It will end with remarks on how the next generation of data mining researchers
can play an important role in shaping that.
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Dr.
Ian Davidson is
an Associate Professor in the Department of Computer Science at the University
of California at Davis. His research interests include constrained clustering,
behavioral analysis using network analysis, tensor decomposition and semi-supservised
and unsupervised models of transfer learning among others. He has published
numerous articles in peer-reviewed conferences, journals and books. His
research has been funded by NSF (CAREER Award), Office of Naval Research,
DoD and Google (Research Award). Dr. Davidson has served in the editorial
boards of IEEE Transactions of Knowledge Discovery and Data Mining, Knowledge
Discovery and Data Mining, Knowledge and Information Systems and program
committees of SDM 2012, ICDM 2011 and KDD 2012 at various capacity.
New
Approaches for Analyzing raw fMRI Data
Abstract:
fMRI
data is arguably one of the most complex forms of sensor data. Typically
there may be many readings per second over a spatial region consisting
of close to one quarter million zones. With the advent of cheaper scanners
there now exist the possibility to collect multiple readings from the
same people over time and multiple readings from a population of individuals.
This offers a whole range of challenges and opportunities. These include
how to fuse this data together and importantly discovery knowledge beyond
simple labels, anomalies and clusters without heavy pre-processing is
a challenge common to many fields in sensor analysis. Furthermore, with
such complex and plentiful data comes the reality that there may be many
explanations of the data and finding explanations that are plausible and
usable requires injecting domain expertise. In this talk we will provide
a high level overview of these problems and our initial progress at addressing
them.
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Dr.
Ralf Birken
is a Research Assistant Professor of Civil and Environmental Engineering
at Northeastern University, Boston. He received his Ph.D. in Geophysical
and Geological Engineering from The University of Arizona in 1997 and
a MS in Geophysics from the University of Cologne, Germany in 1992. He
has over 15 years of research and engineering experience in industry and
academia in near-surface geophysical mapping designing new measurement
systems and sensor technology. His current research interest focuses on
the integration of multi-channel multi-domain sensor technology into geophysical
subsurface imaging systems. Dr. Birken is an expert in applied electromagnetic
geophysics and also interested in the efficient, large-scale, and accurate
mapping of subsurface utilities by combining modern positioning systems
with geophysical array technology. Dr. Birken is actively involved with
the VOTERS (Versatile Onboard Traffic-Embedded Roaming Sensors) project
as project manager and lead scientist. The VOTERS
project provides a framework to shift from periodical localized inspections
to continuous network-wide health monitoring of roadways and bridge decks.
Research focuses on the development of a cost-effective, lightweight package
of advanced radar, acoustic, and optical sensor technology that is compatible
with this framework. VOTERS’ technology, once installed beneath
a fleet vehicle, can monitor road conditions at both the surface and sub-surface
levels. At the same time hazardous, congestion-prone work zones, that
are typically set up to gather these critical inspection data sets, are
eliminated as the traffic-embedded vehicle roams through daily traffic
going about its normal business.
VOTERS:
Design of a Mobile Multi-Modal Multi-Sensor System
Abstract:
The VOTERS (Versatile Onboard
Traffic-Embedded Roaming Sensors) project (www.neu.edu/voters) provides
a framework to complement periodical localized inspections of roadways
and bridge decks with continuous network-wide health monitoring. Utilizing
traffic-embedded Vehicles Of Opportunity (VOOs) roaming through daily
traffic eliminates hazardous, congestion-prone work zones, that are typically
set up to gather these critical inspection data sets. It also provides
maintenance decision makers and researchers with a temporal and spatial
data set not available in roadway and bridge deck inspection today.
Research focuses on the development of a cost-effective, lightweight package
of multi-modal sensor systems compatible with this framework. At the same
time an innovative software infrastructure is created that collects, processes,
and evaluates these large time-lapse multi-modal data streams with the
purpose of detecting anomalies without having to discard any data unseen.
Part of the overall VOTERS system is a VOTERS control center which is
in constant wireless communication with the VOOs equipped with the autonomous
VOTERS sensing system.
VOTERS’ technology, once installed beneath multiple VOOs can frequently
inspect road conditions at both the surface and subsurface levels using
advanced ground penetrating radar, acoustic, and optical sensors, and
a compatible controller technology. Each VOO requires an on-board controller
that manages the individual sensor systems, synchronizes data streams,
registers all data streams in time and space, and an access point to the
control center.
The control center manages multiple VOOs and the data for further analysis,
visualization, and decision making. The two most distinctive features
of this data set are the network-wide coverage and the constant repeats,
which create a time-lapse data set that allows for the monitoring of the
deterioration process at unprecedented time intervals, thereby providing
experimental results that can be used in life-cycle cost analysis models.
Various software infrastructure design and implementation strategies that
are compatible with the requirements given by the VOTERS system and framework
will be explored. A hierarchical multi-tier architecture leads to the
distribution of responsibilities throughout the system. System communication
is a key feature that ties the various subcomponents of the system together.
Sensor fusion aspects will be discussed considering the need for accurate
spatial and temporal registration of all sensor data streams. A multi-level
processing strategy is desired as the amount of data collected exceeds
the communication bandwidth. The multi-domain and heterogeneous Operating
System environment requires special attention.
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