Sixth
International Workshop on Knowledge |
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To be held in conjunction with |
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Sensor-KDD '12 Workshop |
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August
12, 2012
Beijing, China. |
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Submissions | |||||||||||||
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 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. 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. -->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.
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Organizers | |||||||||||||
Invited Speakers |
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Accepted Papers |
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Program Schedule |
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SensorKDD Cup | |||||||||||||
Contacts | |||||||||||||
Links | |||||||||||||
KDD 2012 | |||||||||||||
Travel Information |
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Datastreams Mining in Wikipedia |
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SDS Lab @ NEU | |||||||||||||
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