My research interests include scientific database
systems, access methods, data clustering, and software architecture. This
research is related to three ongoing efforts in computational sciences that
will have significant impact on future scientific research: the development of data
storage systems, which must provide high-performance access to data regardless
of the storage media and the way it is interconnected; the development of
advanced data-mining techniques, which will serve as primary means of analyzing
scientific data; and the development of scientific data grids, which will
provide a generic platform for widely distributed extraction of complex
information from extremely large collections of scientific data.
The broad objective of this research is to advance
the science of database systems in a direction that will illuminate the
enormous problems of scale in the management of persistent data and lead to appropriate
solutions. Due to the emerging petabyte data stores, growing dimensionality of
data, and mounting complexity of DBMS development, these problems of scale
attract considerable interest. An appropriate solution to a problem of scale
must gracefully adapt to a wide range of the increasing magnitudes of the
problem. The issues that have attracted most of my attention include the
problems of data volume, data dimensionality, system load, and DBMS development
complexity.
Data volume. A broad infrastructure for scientific research must provide
efficient solutions for widely distributed extraction of complex information
from extremely large collections of scientific data. One of the objectives of
my research is to support this infrastructure by developing original techniques
for efficient storage and retrieval of massive scientific data. To learn more
click here.
Data dimensionality. Another objective of my research is to
systematically analyze and effectively attack the limitations of contemporary
retrieval and clustering techniques in spaces with many dimensions. In this
area, I have been designing new access methods and clustering techniques for
high-dimensional data. To learn more click here.
System load. Part of my work involves the design of new transformation
schemes for the deployment of advanced indexing techniques in transactional
DBMS environments. The goal is to enable a rapid deployment of
multi-dimensional access methods by reusing the existing indexing techniques of
transactional DBMSs and to develop efficient concurrency and recovery protocols
specialized for compact indexing structures. To learn more click here.
DBMS development complexity. A stumbling block for component DBMS
construction is a high potential for mismatch between the DBMS artifacts. In
this area, my focus has been on the definition of a formal model of software
architecture that can be used to reason about architectural mismatch and on the
development of a formal design methodology that can guide the designers of
component DBMS architecture in their attempts to uncover and prevent potential
mismatch. To learn more click here.
·
J. Lukaszuk and
R. Orlandic, “On Accessing Data in High-Dimensional Spaces: A
Comparative Study of Three Space Partitioning Strategies,” Journal of Systems and Software 73(1):147--157, 2004.
·
R. Orlandic and B. Yu, “Scalable
QSF-Trees: Retrieving Regional Objects in High-Dimensional Spaces,” Journal of
Database Management 15(3):45--59, 2004.
·
J. Lukaszuk and
R. Orlandic, “Efficient High-Dimensional Indexing by Superimposing
Space-Partitioning Schemes,” Intern'l
Database Engineering and Applications Symposium IDEAS'04, Coimbra, Portugal, 257--264, 2004.
·
R. Orlandic and
Y. Lai, “Clustering Technology of a Data Engine for Analytical Computing,” Proc. IEEE 4th Intern'l Conf. on
Intelligent Systems Design and Applications ISDA'04, Budapest, Hungary,
699--704, 2004.
·
R. Orlandic, “ITR:
Development of a Data Engine for Grid-Enabled Analytical Computing”,
NSF Information and Data Management Workshop IDM’2004, Cambridge, MA, 3
pages, 2004.
·
R. Orlandic, “Retrieval
and Clustering of High-Dimensional Scientific Data,” Proc. 7th Workshop on
Mining Scientific and Engineering Datasets, Lake Buena Vista, Florida, 1
page, 2004. (abstract of the invited keynote speech)
· R. Orlandic, “Effective Management of Hierarchical Storage Using Two Levels of Data Clustering,” Proc. 20th IEEE / 11th NASA Goddard Conference on Mass Storage Systems and Technologies MSST’2003, San Diego, CA, 270--279, 2003.
·
B. Yu, T. Bailey, R. Orlandic and J. Somavaram, “KDBKD-Tree:
A Compact KDB-Tree Structure for Indexing Multidimensional Data,” Proc. Intern'l Conf. on Information Technology:
Coding and Computing ITCC'2003,
Las Vegas, Nevada, 676--680, 2003.
· J. Lee, D. Grossman and R. Orlandic, “Adopting a Hierarchical Category Dimension into A Multidimensional Information Retrieval Engine,” Proc. Intern’l Conf. on Information and Knowledge Engineering IKE’2003, Las Vegas, Nevada, 7--10, 2003.
·
J. Lee, D. Grossman and R. Orlandic, “An Evaluation of the Incorporation of a
Semantic Network Into a Multidimensional Retrieval Engine,” Proc. 12th Intern'l Conf. on Information and
Knowledge Management CIKM'03,
New Orleans, Louisiana, 572--575, 2003.
·
R. Orlandic, “While
Waiting for High-Dimensional Indexing,”
Database Management, Auerbach
Publications, 11 pages, 2003.
·
R. Orlandic and
B. Yu, “A Retrieval Technique for High-Dimensional Data and Partially
Specified Queries,” Data and Knowledge
Engineering 42(1):1--21, 2002.
· R. Orlandic, J. Lukaszuk and C. Swietlik, “The Design of a Retrieval Technique for High-Dimensional Data on Tertiary Storage,” SIGMOD Record 31(2):15--21, ACM, 2002.
·
R. Orlandic,
J.L. Pfaltz and Y. Lepouchard,
“Multi-Dimensional Retrieval of Widely Varying Objects,” Proc. IASTED Intern'l
Conf. on Applied Informatics AI’2002, Innsbruck, Austria, 503--508, 2002.
·
Y. Lepouchard, R. Orlandic and J.L. Pfaltz, “Performance
of KDB-Trees with Query-Based Splitting,”
Proc. Intern'l Conf. on Information Technology: Coding and Computing ITCC'2002,
Las Vegas, Nevada, 218--222, 2002.
·
J. Lee, D.
Grossman and R. Orlandic, “MIRE: A Multi-Dimensional Information
Retrieval Engine for Structured Data and Text,” Proc. Intern'l Conf. on Information Technology: Coding and Computing
ITCC'2002, Las Vegas, Nevada, 224--229, 2002.
·
R. Orlandic and
J.L. Pfaltz, “Preventing Mismatch of Homogenous Components in the Design of
Software Architecture,” Intern'l Journal of Software Engineering and Knowledge
Engineering 11(6):731--760, 2001.
·
R. Orlandic and
B. Yu, “Implementing KDB-trees to Support High-Dimensional Data,” Proc.
Intern'l Database Engineering and Applications Symposium IDEAS'2001, Grenoble,
France, 58--67, 2001.
·
R. Orlandic and
B. Yu, “Inverted-Space Storage Organization for Persistent Data of Very High
Dimensionality,” Proc. Intern'l Conf. on Information Technology: Coding and
Computing ITCC'2001, Las Vegas, Nevada, 616--621, 2001.
·
R. Orlandic and
B. Yu, “A Study of MBR-Based
Spatial Access Methods: How Well They Perform in High-Dimensional Spaces,” Proc.
Intern'l Database Engineering and Applications Symposium IDEAS'2000, Yokohama,
Japan, 306--315, 2000.
·
B. Yu and R.
Orlandic, “Object and Query Transformation: Supporting Multi-Dimensional
Queries through Code Reuse,” Proc. 9th Intern'l Conf. on Information and
Knowledge Management CIKM'2000, McLean, VA, 141--149, 2000.
·
R. Orlandic, “A Layered Multi-Model Design Framework
for DBMS Construction,” Proc. 6th Intern'l Conf. on Information Systems
Analysis and Synthesis ISAS'2000, Orlando, FL, 150--155, 2000.
·
B. Yu, R.
Orlandic and M. Evens, “Simple QSF‑Trees: An Efficient and Scalable Spatial
Access Method,” Proc. 8th Intern'l Conf. on Information and Knowledge
Management CIKM'99, Kansas City, MO, 5--14, 1999.
·
J.L. Pfaltz and
R. Orlandic, “A Scalable DBMS for Scientific Simulations,” Proc. Intern'l
Symposium on Database Applications for Non-traditional Environments DANTE'99,
Kyoto, Japan, 230--234, 1999.
·
S. Coutre, M.
Evens, S. Armato and R. Orlandic, “Automatic Radionuclide Lung Scan
Registration: A Comparison of Four Methods,” 1999 IEEE Medical Imaging
Conference, Seattle, WA, 4 pages, 1999.
·
R. Orlandic, “A
Methodology of Developing Software Architectures Supporting Implementation
Independence,” First Working IFIP Conference on Software Architecture WICSA1,
San Antonio, Texas, 5 pages, 1999.
·
R. Orlandic,
“Methodical Design of Mismatch‑Free Architectures for Information Management,” Proc.
5th Intern'l Conf. on Information Systems Analysis and Synthesis ISAS'99,
Orlando, FL, 185--192, 1999.
·
R. Orlandic,
“Foundations of a Methodology of DBMS Decoupling for Evolutionary Component
DBMS Design,” Proc. Intern'l Database Engineering and Applications Symposium
IDEAS'98, Cardiff, UK, 178--187, 1998.
·
R. Orlandic, “A
Theory of Implementation Independence: Architectural Specificity vs.
Architectural Mismatch,” Proc. 10th Intern'l Conf. on Software Engineering and
Knowledge Engineering SEKE'98, San Francisco, CA, 140--145, 1998.
·
R. Orlandic and
H. Mahmoud, “Storage Overhead of O‑trees, B‑trees, and Prefix B‑trees: A
Comparative Analysis,” Intern'l Journal of Foundations of Computer Science
7(3):209--226, 1996.
·
H. Mahmoud and R.
Orlandic, “Toward a Formal Derivation of the Expected Behavior of Prefix
B-trees,” Probability in the Engineering and Informational Sciences
2(9):183--192, 1995.
·
R. Orlandic,
“File Access Methods,” in Concise Encyclopedia in Software Engineering, editors
D. Morris and B. Tamm, Pergamon Press, Oxford, England, 128--132, 1993.
·
R. Orlandic, “A
High-Precision Spatial Access Method Based on a New Linear Representation of
Quadtrees,” Proc. First Intern'l Conf. on Information and Knowledge Management
CIKM'92, Baltimore, MD, 499--508, 1992.
·
R. Orlandic,
“Problems of Content-Based Retrieval in Image Databases,” Proc. Third Symposium
on 'New Generation' Knowledge Engineering IAKE'92, Washington, DC, 374--384,
1992.
·
R. Orlandic and
J.L. Pfaltz, “Retrieving Data Based on Long Keys,” Proc. Navy Environmental
Systems Workshop, Snowbird, Utah, 111--119, 1992.
·
R. Orlandic and
J.L. Pfaltz, “A Highly Compressed B‑tree Index for Long, Variable Length
Strings,” Data Compression Conf. DCC’92, Snowbird, Utah, 1 page, 1992.
·
R. Orlandic and
J.L. Pfaltz, “Analysis of Compact 0‑complete Trees: A New Access Method to
Large Databases,” Proc. 7th Intern'l Conf. Fundamentals of Computation Theory
FCT'89, Szeged, Hungary, in Lecture Notes in Computer Science
380, Berlin, Springer-Verlag, 362--371, 1989.
·
R. Orlandic and
J.L. Pfaltz, “Compact 0‑complete Trees,” Proc. 14th Intern'l VLDB Conf. on Very
Large Data Bases, Long Beach, CA, 372--381, 1988.
R. Orlandic, “Effective Management of Hierarchical Storage Using Two Levels of Data Clustering,” MSST’03, 2003. pdf , postscript
R. Orlandic and B.
Yu, “A Retrieval Technique for High-Dimensional Data and Partially
Specified Queries,” DKE, 2002.
R. Orlandic and
J.L. Pfaltz, “Preventing Mismatch of Homogenous Components in the Design of
Software Architecture,” IJSEKE,
2001.
B. Yu and R. Orlandic, “Object and Query Transformation:
Supporting Multi-Dimensional Queries through Code Reuse,” CIKM’00, 2000.
B. Yu, R. Orlandic and M. Evens, “Simple QSF-Trees: An
Efficient and Scalable Spatial Access Method,” CIKM'99, 1999.
R. Orlandic and H. Mahmoud, “Storage Overhead of O-trees,
B-trees, and Prefix B-trees: A Comparative Analysis,” IJFCS, 1996.
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