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<HTML><HEAD><TITLE>UCLA Data Mining Laboratory Publications</TITLE></HEAD><!-- <BODY bgcolor="#ffffff" link="#6C2755" vlink="#F29F01"alink="#ffffff"> --><BODY background="../images/dml_bk4.gif" link="#6C2755" vlink="#F29F01" alink="#ffffff"><!-- Just in case, define the font size --><basefont = 4><!--<!WA0><IMG SRC="http://nugget.cs.ucla.edu:8001/images/earth.gif"><FONT COLOR=#cc0000><EM>UCLA Data Mining Laboratory Publications</EM></FONT> --><!--<!WA1><A HREF="http://nugget.cs.ucla.edu:8001/"><!WA2><img width=100% src="http://nugget.cs.ucla.edu:8001/images/dml_bar2.gif"></A>--><HR width=100% SIZE=2 NOSHADE><P><P><TABLE border=0 width=540><tr valign><td rowspan=0 align=left nowrap><!WA3><img src="http://nugget.cs.ucla.edu:8001/images/strips/pub.gif"></td><TD><UL><P><!WA4><IMG HEIGHT=12 WIDTH=100% SRC="http://nugget.cs.ucla.edu:8001/images/year_bar96.gif"><P><LI> <!WA5><A HREF="#acmgis_96">"Mapping a Common Geoscientific Object Model to Heterogeneous SpatialData Repositories"</A>,The 4th ACM International Workshop on Advances in GeographicInformation Systems,Rockville, Maryland, Nov. 1996.<P><LI> <!WA6><A HREF="#cs960039">"The Design of the FALCON Framework for Application Level CommunicationOptimization"</A>,UCLA CSD Technical Report #960039, Nov. 1996.<P><LI> <!WA7><A HREF="#ieee_expert_96">"Mining Geophysical Data for Knowledge"</A>,IEEE Expert, 11(5):34-44,Oct. 1996.<P><LI> <!WA8><A HREF="#spie_96">"OASIS: An EOSDIS Science Computing Facility"</A>,International Symposium on Optical Science, Engineering, andInstrumentation, Conference on Earth Observing System,Denver, Colorado, Aug. 1996.<P><LI> <!WA9><A HREF="#kdd_96">"Scalable Exploratory Data Mining of Distributed Geoscientific Data"</A>,Second International Conference on Knowledge Discovery and Data Mining,Portland, Oregon, Aug. 1996.<P><LI> <!WA10><A HREF="#ride_nds_96_conquest">"On Heterogeneous Distributed Geoscientific Query Processing"</A>,Sixth International Workshop on Research Issues in Data Engineering: Interoperability of Nontraditional Database Systems,New Orleans, Louisiana, Feb. 1996.<P><LI> <!WA11><A HREF="#ride_nds_96_oasis">"OASIS: An Open Architecture Scientific Information System"</A>,Sixth International Workshop on Research Issues in Data Engineering: Interoperability of Nontraditional Database Systems,New Orleans, Louisiana, Feb. 1996.<P><!WA12><IMG HEIGHT=12 WIDTH=100% SRC="http://nugget.cs.ucla.edu:8001/images/year_bar95.gif"><P><LI> <!WA13><A HREF="#sisic_95_conquest">"Optimization of Access to Heterogeneous Data Repositoriesin a Geoscientific Query Processing System"</A>,Science Information Systems Interoperability Conference,College Park, MD, Nov. 1995.<P><LI> <!WA14><A HREF="#sisic_95_oasis">"OASIS: An Open Architecture Scientific Information System"</A>,Science Information Systems Interoperability Conference,College Park, MD, Nov. 1995.<P><LI> <!WA15><A HREF="#kdd_95">"Fast Spatio-Temporal Data Mining of Large Geophysical Datasets"</A>,The First International Conference on Knowledge Discovery and DataMining,Montreal, Quebec, Canada, Aug 1995. <P><LI> <!WA16><A HREF="#gis_95">"Integrating Distributed Object Management into EOS"</A>,Geo Info Systems, 5(5):58-59, May 1995.<P><LI> <!WA17><A HREF="#ieee_pacrim_95">"Exploratory Data Mining and Analysis Using Conquest"</A>,IEEE Pacific Rim Conference on Communications, Computers, Visualization,and Signal Processing,Victoria, British Columbia, Canada, May 1995.<P><!WA18><IMG HEIGHT=12 WIDTH=100% SRC="http://nugget.cs.ucla.edu:8001/images/year_bar94.gif"><P><LI> <!WA19><A HREF="#sc_94">"Real Time Data Mining, Management, and Visualization of GCM Output"</A>,Supercomputing 94 Poster,Washington, DC, Nov 1994.<P><LI> <!WA20><A HREF="#cs940039">"The Conquest Modeling Framework for Geoscientific Data"</A>,UCLA CSD Technical Report #940039, Oct 1994.<P><LI> <!WA21><A HREF="#igarss_94">"Quest: An Environment for Content-Based Access to Geoscienitfic Datasets"</A>,1994 International Geoscience and Remote Sensing Symposium,Pasadena, CA, Aug. 1994.<P><LI> <!WA22><A HREF="#aaai_env_tech_94">"QUEST: Content-based Access to Geophysical Databases"</A>,AAAI Workshop on AI Technologies in Environmental Applications,Seattle, WA, Jul-Aug 1994.<P><LI> <!WA23><A HREF="#ieee_vis_94">"Extracting Spatio-Temporal Patterns from Geoscience Datasets"</A>,IEEE Workshop on Visualization and Machine Vision,Seattle, WA, Jun 1994.<P></UL></TD></TR></TABLE><!-- ----------------------------------------------------------- --><HR width=100% SIZE=2 NOSHADE><P><A NAME="acmgis_96"><H3>S. Nittel, J. Yang, and R.R. Muntz,<!WA24><A HREF="http://nugget.cs.ucla.edu:8001/publications/acm_gis96.ps.Z">"Mapping a Common Geoscientific Object Model to Heterogeneous SpatialData Repositories"</A>,The 4th ACM International Workshop on Advances in GeographicInformation Systems,Rockville, Maryland, Nov. 1996.</H3><P>Lately, a need to integrate specialized data management systemssuch as geographic information systems (GIS), or multimediasystems has gained importance. A large variety of differentdata sets are available in various specialized repositories, andusers would like to access and manipulate these data sets ina uniform way. Additionally, it is desirable to make thespecialized functionality provided by the individualrepositories available to the user application through ahomogeneous interface. At UCLA Data Mining Laboratory, we aredeveloping geoPOM (geoscientific Persistent Object Manager), aheterogeneous geoscientific object system which provides ahomogeneous interface to heterogeneous spatial data repositories. geoPOM provides an object-oriented spatial data model forthe definition of user-defined spatial object types.Internally, geoPOM maps user-defined spatial object typesto different specialized spatial data repositories, andemploys their storage, search and spatial query capabilities.In this paper, we focus on the goals, problems and approachtaken in geoPOM towards defining the spatial functionalityof the heterogeneous geoscientific object system available to the user, as well as, the mapping of the spatial objectmodel to the diverse semantic and functional characteristicsof the heterogeneous spatial data repositories.<HR><P><A NAME="cs960039"><H3>E.C. Shek, R.R. Muntz, and L. Fillion,<!WA25><A HREF="http://nugget.cs.ucla.edu:8001/publications/cs960039.ps.Z">"The Design of the FALCON Framework for Application Level CommunicationOptimization"</A>,UCLA CSD Technical Report #960039, Nov. 1996.</H3><P>There exist a wide-variety of communication-intensive applications which run in networks and platforms of greatly varying characteristics.This implies the need for application level communicationoptimization, which is the optimization of network communicationby exploiting application semantics as well as network and computenode characteristics. In this paper, we propose a flexible object-oriented framework calledFALCON for application level communication optimization by allowingcomplementary network communication optimization techniques to becombined in the form of matching stack layers at the endpoints of acommunication channel. Each stack layer is composed of a pair of matching modules, executedin the sender and receiver endpoints respectively.To exploit application knowledge that is only available to one of thecommunicating peers, the framework allows an executable stack layermodule to be supplied by either of the communication peers and providesfor safe transport to and execution at the other end of the channel.Automatic rule-based optimization techniques similar to those used for extensible database query optimization are developed to optimize the communication channel stacks based on the characteristicsof available stack layers, required properties of the channel, and anapplication-dependent cost model. In addition to providing architectural support for optimizing networkcommunication, FALCON can also be used to introduce support fornew communication and computing paradigms in high-level distributedcomputing environments. For example, while OMG's CORBA distributedobject management architecture adopts remote method invocation as itsprimary communication mechanism, data streaming and service migrationcan be easily accommodated within the FALCON framework. <P><HR><P><A NAME="ieee_expert_96"><H3>E. Mesrobian, R.R. Muntz, E.C. Shek, S. Nittel, M. La Rouche, M. Kriguer, C.R. Mechoso, J.D. Farrara, P. Stolorz, and H. Nakamura,"Mining Geophysical Data for Knowledge",IEEE Expert, 11(5):34-44,Oct. 1996.</H3>Exploratory data mining and analysis requires a computing environmentwhich provides facilities for the user-friendly expression and rapidexecution of "scientific queries". OASIS exploits emerging distributed object management technologies to present a flexible, extensible, and seamlessenvironment for scientific data analysis,knowledge discovery, visualization, and collaboration.In this article we illustrate the use of OASISfor exploratory data analysis and data mining of spatio-temporal phenomena from large geophysical datasets.<P><HR><P><A NAME="spie_96"><H3>E. Mesrobian, R.R. Muntz, E.C. Shek, S. Nittel, M. Kriguer,and M. LaRouche,<!WA26><A HREF="http://nugget.cs.ucla.edu:8001/publications/spie_96.ps.Z">"OASIS: An EOSDIS Science Computing Facility"</A>,International Symposium on Optical Science, Engineering, andInstrumentation, Conference on Earth Observing System,Denver, Colorado, Aug. 1996.</H3>In the course of global change studies, a scientist would often liketo efficiently store, retrieve, analyze and interpret selected datasets from a large collection of scientific information scatteredacross heterogeneous computational environments, Earth ObservingSystem data repositories, and to share the gleaned information withother scientific communities. To facilitate the above activities, wehave developed OASIS, a flexible, extensible, and seamless environmentfor scientific data analysis, knowledge discovery, visualization, andcollaboration. <P><HR><P><A NAME="kdd_96"><H3>E.C. Shek, R.R. Muntz, E. Mesrobian, and K. Ng,<!WA27><A HREF="http://nugget.cs.ucla.edu:8001/publications/kdd_96.ps.Z">"Scalable Exploratory Data Mining of Distributed Geoscientific Data"</A>,<!WA28><A HREF="http://www-aig.jpl.nasa.gov/kdd96/">Second International Conference on Knowledge Discovery and Data Mining</A>,Portland, Oregon, Aug. 1996.</H3><P>Geoscience studies produce data from various observations,experiments, and simulations at an enormous rate. Exploratory data mining extracts "content information" frommassive geoscientific datasets to extract knowledge and provide a compact summary of the dataset.In this paper, we discuss how database query processing anddistributed object management techniques can be used to facilitategeoscientific data mining and analysis. Some special requirements of large scale geoscientific data miningthat are addressed include geoscientific data modeling, parallel queryprocessing, and heterogeneous distributed data access. <P><HR><P><A NAME="ride_nds_96_conquest"><H3>E.C. Shek, E. Mesrobian, and R.R. Muntz,<!WA29><A HREF="http://nugget.cs.ucla.edu:8001/publications/ride_nds_96_conquest.ps.Z">"On Heterogeneous Distributed Geoscientific Query Processing"</A>,<!WA30><A HREF="http://www.cs.pitt.edu/ride96/ride96.html">Sixth International Workshop on Research Issues in Data Engineering: Interoperability of Nontraditional Database Systems</A>,New Orleans, Louisiana, Feb. 1996.</H3><P>Geoscience studies produce data from various observations,experiments, and simulations at an enormous rate. In this paper, wepresent an overview of the Conquest parallel scientific queryprocessing system that we are developing at UCLA to tackle some of thescientific data management problems presented by the proliferation of⌨️ 快捷键说明
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