Edmond Mesrobian

Mining geophysical data for knowledge

Oasis is a flexible, extensible, and seamless environment for scientific data analysis, knowledge discovery, visualization, and collaboration. The authors describe how Oasis can help explore data analysis and data mining of spatio-temporal phenomena from large geophysical data sets.

Extracting spatio-temporal patterns from geoscience datasets

A major challenge facing geophysical science today is the unavailability of high-level analysis tools with which to study the massive amount of data produced by sensors or long simulations of climate models. We have developed a prototype information system called QUEST to provide content-based access to massive datasets. QUEST employs workstations as well as teraFLOP computers to analyze geoscience data to produce spatial-temporal features that can be used as high-level indexes. Our first application area is global change climate modeling. In the initial prototype, the first features extracted are cyclones trajectories from the output of multi-year climate simulations produced by a General Circulation Model. We present an algorithm for cyclone extraction and illustrate the use of cyclone indexes to access subsets of GCM data for further analysis and visualization.<<ETX>>

OASIS: an open architecture scientific information system

Motivated by the premise that heterogeneity of software applications and hardware systems is here to stay, we are developing OASIS, a flexible, extensible, and seamless environment for scientific data analysis, knowledge discovery, visualization, and collaboration. We discuss our OASIS design goals and present the system architecture and major components of our prototype environment.

A software environment for studying computational neural systems

UCLA-SFINX is a neural network simulation environment that enables users to simulate a wide variety of neural network models at various levels of abstraction. A network specification language enables users to construct arbitrary network structures. Small, structurally irregular networks can be modeled by explicitly defining each neuron and can be modeled by explicitly defining each neuron and corresponding connections. Very large networks with regular connectivity patterns can be implicitly specified using array constructs. Graphics support, based on X Windows System, is provided to visualize simulation results. Details of the simulation environment are described, and simulation examples are presented to demonstrate SFINXs capabilities. >

OASIS: an EOSDIS science computing facility

In the course of global change studies, a scientist would often like to efficiently store, retrieve, analyze and interpret selected data sets from a large collection of scientific information scattered across heterogeneous computational environments, Earth observing system data repositories, and to share the gleaned information with other scientific communities. To facilitate the above activities, we have developed OASIS, a flexible, extensible, and seamless environment for scientific data analysis, knowledge discovery, visualization, and collaboration.

A general purpose simulation environment for neural models

Current interest in neural networks has produced a diverse set of algorithms and architectures that vary in connectivity pattern, temporal behavior, update rules, and convergence properties. We have designed a flexible simulation system that can support the implementation of a wide range of neural network approaches. The UCLA-SFINX simulator is especially suited for the exploration of structured, irregular, and layered connectivity patterns. Func tions, such as those in early vision, are modeled using the regular connectivity of center/surround antagonistic receptive fields and can be implemented as the difference of concentric gaussians. Higher level cognitive functions, such as supervised and unsuper vised learning, have more irregular, dynamic connectivity structures and update mechanisms that are also supported. To visualize weight spaces, input/output training sets, image data, or other network characteristics, SFINX provides an X- windows based graphical output that assists in rapidly assessing the consequences of altering connectivity patterns, parameter tuning, and other experiments.

Extensible Parallel Query Processing for Exploratory Geoscientific Data Mining

Exploratory data mining and analysis requires a computing environment which provides facilities for the user-friendly expression and rapid execution of “scientific queries.” In this paper, we address research issues in the parallelization of scientific queries containing complex user-defined operations. In a parallel query execution environment, parallelizing a query execution plan involves determining how input data streams to evaluators implementing logical operations can be divided to be processed by clones of the same evaluator in parallel. We introduced the concept of “relevance window” that characterizes data lineage and data partitioning opportunities available for an user-defined evaluator. In addition, we developed a query parallelization framework by extending relational parallel query optimization algorithms to allow the parallelization characteristics of user-defined evaluators to guide the process of query parallelization in an extensible query processing environment. We demonstrated the utility of our system by performing experiments mining cyclonic activity, blocking events, and the upward wave-energy propagation features from several observational and model simulation datasets.

A Connectionist Architecture For Computing Textural Segmentation

This project examines some parallel architectures designed for image processing, and then addresses their applicability to the problem of image segmentation by texture analysis. Using this information, and research into the structure of the human visual system, an architecture for textural segmentation is proposed. The underlying premise is that textural segmentation can be achieved by recognizing local differences in texture elements (texels). This approach differs from most of the previous work where the differences in global, second-order statistics of the image points are used as the basis for segmentation. A realistic implementation of this approach requires a parallel computing architecture which consists of a hierarchy of functionally different nodes. First, simple features are extracted from the image. Second, these simple features are linked together to form more complex texels. Finally, local and more global differences in texels or their organization are enhanced and linked into boundaries.