by Theresa Pepin
We are all vaguely aware that sensors in the mobile devices we carry in our everyday lives quietly go about gathering all kinds of information in aggregate amounts previously unimaginable.
While some of the data is a record of our direct bidding—what individuals actively do with a particular device itself—other data is implicit in the context detected by sensors, such as location and time.
If the various streams of data can be accurately characterized and analyzed in real time and full context—as is the ambitious aim of Qing Cao’s exceptionally promising JDRD project—their “fusion” would allow us to infer patterns of behavior and activity, enabling even smarter, data driven, context-”aware” applications for immediate point-of-use.
Existing systems attempting to exploit the rich semantics of mass heterogeneous data are rudimentary. Cao’s initiative targets head on the challenge of coming up with tools to make sense of the huge amounts of collected data. Essential to achieving success is its attention to scalability and fundamentals by undertaking the first systematic research on programming abstractions for real-time sensor data.
In year one, the JDRD team led by Cao, with PhD students Yanjun Yao and Kefa Lu, demonstrated the feasibility of a distributed client-server model for collecting and processing real-time location information. In year two, their efforts are proceeding from small-scale testbeds to larger-scale communities of volunteer participants. Curriculum and laboratory designs for undergraduate and graduate students are also part of ongoing activities. The project has produced four applications so far—Friend Book, SmartDiary, PhoneCon, and Uno—with two conference papers delivered and another two under submission.
On the LDRD side of the collaboration is James Horey of ORNL’s Computational Sciences and Engineering Division. For Horey, the complementary effort serves as an opportunity to acquire unique sensor data while sharing his group’s design of a new distributed programming model to express spatiotemporal data fusion. In effect, the JDRD team is extending the original LDRD project to include innovative techniques for location-based sensor networks and data mining of novel data sources.
In addition to several publications, Cao and Horey are jointly pursuing opportunities to leverage the success of their JDRD-LDRD collaboration with next-stage funding from the National Science Foundation, where Cyber-Physical Systems has been designated as an important emerging area of research. Four proposals representing nearly $2 million in potential grants are in the pipeline—three to NSF and one to Google.
Distributed computational framework for massive heterogeneous data fusion: A location-centric approach (Year 2)
Qiang He, UT Department of Civil and Environmental Engineering Department
Distributed Computational Framework for Massive Heterogeneous Data Fusion
James Horey, ORNL Computational Sciences and Engineering Division