Alfonso F. Cardenas
Computer Science Department
University of California, Los Angeles
3731 Boelter Hall
Los Angeles, Ca 90024
Tel. (310) 825-7550
Fax. (310) 825-2578
cardenas@cs.ucla.edu
http://www.cs.ucla.edu/csd/people/faculty_pages/cardenas.html
B2-165 Center for Health Sciences
(310) 825-9704
daberle@mednet.ucla.edu
http://www.radsci.ucla.edu:8000/professors/aberle.html
62-215 Center for Health Sciences
(310)794-7333
rcameron@mednet.ucla.edu
http://www.surgery.medsch.ucla.edu/asp/Doctors.asp?emp_name=Cameron,
Robert B. M.D.
Multimedia systems, Stream
modeling, Stream querying, Timeline
We investigate and develop
stream-based data model constructs and the corresponding querying facilities
using a timeline paradigm, in response to the growing requirements of advanced
multimedia database applications. Using the basic concept of a stream, we
develop substreams, aggregated streams, and derived streams. The various types of relationships between
and within streams, and with standard database entities, are explored and
developed as part of the data model. These stream constructs add to existing
SQL database facilities and object-oriented database facilities. We focus on
the challenges and requirements of medical applications and of geophysical
satellite remote sensing, requiring similar underlying stream data management
and timeline access advances that we put forth. A patient data and physician
tested and an environmental satellite data test bed will be used to validate
and evaluate the effectiveness of the proposed technologies.
Cárdenas, A. F., Pon, R. K.,
Michael, P. A., Hsiao, J. T.,
"Image Stack Viewing and Access," Journal of Visual Language and
Computing, 2003, in press.
Cárdenas, A. F., Pon, R. K.,
and Cameron, R. B., "Management of
Streaming Body Sensor Data for Medical Information Systems," The 2003
International Conference on Mathematics and Engineering Techniques in Medicine
and Biological Sciences (METMBS '03),
Cárdenas,
A. F. and Michael, P. A.,
"Image Stack Stream Model of Multimedia Data," Proceedings of the
2002 Conference on Distributed Multimedia Systems,
Cárdenas,
A. F., Michael, P. A. and Islam, B. S. , "Stack Database Model/View of Multimedia Data," Proceedings
of the 2002 International Conference on Information and Knowledge Engineering,
Las Vegas, Nevada, June 24-27, 2002.
There is an explosive growth
in the amount and variety of data streams, generated by the large numbers of
sensors being deployed in many domains, and becoming more readily available
through the internet as well as from stored traditional databases. Our efforts
are aimed at enhancing the management and access to multiple related data
streams from different sources through a timeline paradigm. We focus first on
the medical domain and the opportunities of enhancing the access to patient
data by not only doctors and the traditional health care providers but also by
patients. We will test our advances and hypothesis at the UCLA medical center.
We also investigate the data management challenges of streams of medical data
coming on a possible real time basis from new generation mobile on-body medical
sensors. In particular we are using the LifeShirt, with on board medical
sensors, as part of a collaborative effort with its inventor and vendor VivoMetrics, Inc. We focus second on the geophysical domain
and the challenges and opportunities for enhancing the management and access to
a variety of data streams collected by real-time sensors on new NASA
satellites. We will test our advances using NASA satellite data needed by
colleagues dealing with environmental challenges. Thus, we will gauge the
impact of our R&D advances.
We
have been developing the substream, aggregated stream and generic stream
derivation constructs and explicit relationships constructs between streams
beyond the base stream model that we developed in prior research needed by
medical applications and geographic information systems (GIS) applications. We
are pursuing the image stack data model for querying and viewing data coming
from different types of data streams, which map to the same location of
interest. For example, in the medical domain for superimposing MRI and CT
images for the same patient body location, and in the GIS domain for
superimposing co-registered pollution level, population density, and street
location data. We are devoting efforts to the challenge of co-registering
images needed for the image stack. We are developing the database model
extensions needed for the SQL, ODMG, and JDO database standards to handle the
multimedia stream database constructs and the image stack model. We have also
initiated the extension to the SQL language and the native object query
language OQL for both ODMG and JDO, to deal with a number of sample queries on
multimedia streams corresponding to these stream model developments.
We
are furthering the timeline paradigm as the high-level user-interface to the
variety of data streams. We are developing proof-of-concept prototypes for
medical patient data and geophysical data, including NASA satellite data.
Within in this paradigm, we are researching different visualization techniques
for a variety of data in both domains.
We
have addressed the various physical database challenges and prototype
implementation alternatives, focusing on a “quick and dirty” approach for fast
proof of concept and a subsequent one for large and varied real-time data
streams. We have initiated the development of the image stack model and
viewing, and are working on the querying strategies to access data. Several key
queries in the medical domain and the GIS domain are the focus of current work.
Provides an overview and
details of our overall project.
http://www.mmss.cs.ucla.edu/TimeLine.htm
Provides a screen shot tour
or glimpses of the anticipated eventual prototype we are developing.
http://goliath.cs.ucla.edu/ImageArchive/
This data set provides web
access to several hundred real life medical images (mostly CR, CT, and MR) of
patients, mostly afflicted by cancer. Actual patient identity and demographic
identity is deleted as required by confidentiality regulations. This dataset content was gathered during a
prior NSF funded project, the KMeD project.