vldb_1994_elementary.txt

利用lwp：：get写的

traditional solutions require significant changes to the types, the
type hierarchy and the code of existing methods. Such operations are
very expensive in a database context. To ease schema evolution, we
propose to support exceptions to the consistency rules without
sacrificing type safety for all that. The basic idea is to detect
unsafe statements at compile-time and check them at run-time. The
run-time check is performed by a specific clause that is automatically
inserted around unsafe statements. This check clause warns the
programmer of the safety problem and lets him provide
exception-handling code. This way, some schema updates can be
performed with only minor changes to the code of methods.</abstract></paper><paper><title>Bulk Loading into an OODB: A Performance Study.</title><author><AuthorName>Janet L. Wiener</AuthorName><institute><InstituteName></InstituteName><country></country></institute></author><author><AuthorName>Jeffrey F. Naughton</AuthorName><institute><InstituteName></InstituteName><country></country></institute></author><year>1994</year><conference>International Conference on Very Large Data Bases</conference><citation><name>The Object Database Standard: ODMG-93.</name><name>Shoring Up Persistent Applications.</name><name>A Survey of DBMS Research Issues in Supporting Very Large Tables.</name><name>IBM's Relational DBMS Products: Features and Technologies.</name><name>Query Processing in the ObjectStore Database System.</name><name>Identification of Database Objects by Key.</name><name>A Layered Approach to Scientific Data Management at Lawrence Berkeley Laboratory.</name><name>Moving Structures between Smalltalk Images.</name><name>A Moose and a Fox Can Aid Scientists with Data Management Problems.</name></citation><abstract>Object-oriented database (OODB) users bring with them large quantities
of legacy data (megabytes and even gigabytes). In addition, scientific
OODB users continually generate new data. All this data must be loaded
into the OODB.  Every relational database system has a load utility,
but most OODBs do not. The process of loading data into an OODB is
complicated by inter-object references, or relationships, in the data.
These relationships are expressed in the OODB as object identifiers,
which are not known at the time the load data is generated; they may
contain cycles; and there may be implicit system-maintained inverse
relationships that must also be stored.
We introduce seven algorithms for loading data into an OODB that
examine different techniques for dealing with circular and inverse
relationships.  We present a performance study based on both an
analytic model and an implementation of all seven algorithms on top of
the Shore object repository. Our study demonstrates that it is
important to choose a load algorithm carefully; in some cases the best
algorithm achieved an improvement of one to two orders of magnitude
over the naive algorithm.</abstract></paper><paper><title>NAOS - Efficient and Modular Reactive Capabilities in an Object-Oriented Database System.</title><author><AuthorName>Christine Collet</AuthorName><institute><InstituteName></InstituteName><country></country></institute></author><author><AuthorName>Thierry Coupaye</AuthorName><institute><InstituteName></InstituteName><country></country></institute></author><author><AuthorName>T. Svensen</AuthorName><institute><InstituteName></InstituteName><country></country></institute></author><year>1994</year><conference>International Conference on Very Large Data Bases</conference><citation><name>On Maintaining Priorities in a Production Rule System.</name><name>A New Perspective on Rule Support for Object-Oriented Databases.</name><name>Behavior of Database Production Rules: Termination, Confluence, and Observable Determinism.</name><name>Building an Object-Oriented Database System, The Story of O2.</name><name>A Model for Active Object Oriented Databases.</name><name>Rule Management and Evaluation: An Active DBMS Perspective.</name><name>The HiPAC Project: Combining Active Databases and Timing Constraints.</name><name>Rules Are Objects Too: A Knowledge Model For An Active, Object-Oriented Database System.</name><name>Organizing Long-Running Activities with Triggers and Transactions.</name><name>Rete: A Fast Algorithm for the Many Patterns/Many Objects Match Problem.</name><name>Integrating Active Concepts into an Object-Oriented database System.</name><name>Ode as an Active Database: Constraints and Triggers.</name><name>Composite Event Specification in Active Databases: Model &amp; Implementation.</name><name>Event Specification in an Active Object-Oriented Database.</name><name>Rule Condition Testing and Action Execution in Ariel.</name><name>An Execution Model for Active Data Base Management Systems.</name><name>Extensions to Starburst: Objects, Types, Functions, and Rules.</name><name>The Architecture Of An Active Data Base Management System.</name><name>Object Integrity Using Rules.</name><name>On Rules, Procedures, Caching and Views in Data Base Systems.</name><name>The Postgres Next Generation Database Management System.</name><name>Implementing Set-Oriented Production Rules as an Extension to Starburst.</name></citation><abstract>This paper describes the design and implementation
of NAOS, an active rule component in the object-oriented
database system O2. The contribution of this
work is related to two main aspects. The first concerns the
integration of the rule concept within the O2 model, providing a way
to structure applications.  Rules are part of a schema and do not
belong to a class. Program execution and data manipulation, including
method calls, can be driven on rules.  The second aspect concerns
the way NAOS interacts with the kernel of the O2 system. To support
a reactive capability the object manager semantics has been extended,
thus providing an efficient event detection. 
Applications produce events and the subscribed event
types react to these events. As a result, rules are triggered.</abstract></paper><paper><title>Efficient and Effective Clustering Methods for Spatial Data Mining.</title><author><AuthorName>Raymond T. Ng</AuthorName><institute><InstituteName></InstituteName><country></country></institute></author><author><AuthorName>Jiawei Han</AuthorName><institute><InstituteName></InstituteName><country></country></institute></author><year>1994</year><conference>International Conference on Very Large Data Bases</conference><citation><name>An Interval Classifier for Database Mining Applications.</name><name>Mining Association Rules between Sets of Items in Large Databases.</name><name>Optimization for Spatial Query Processing.</name><name>Loading Data into Description Reasoners.</name><name>Efficient Processing of Spatial Joins Using R-Trees.</name><name>Efficient Computation of Spatial Joins.</name><name>Knowledge Discovery in Databases: An Attribute-Oriented Approach.</name><name>Randomized Algorithms for Optimizing Large Join Queries.</name><name>Query Optimization by Simulated Annealing.</name><name>Supporting Data Mining of Large Databases by Visual Feedback Queries.</name><name>Knowledge Discovery in Databases.
 AAAI/MIT Press 1991, ISBN 0-262-62080-4</name><name>The Design and Analysis of Spatial Data Structures.
 Addison-Wesley 1990</name><name>Constraint-based clustering in large databases.</name></citation><abstract>Spatial data mining is the discovery of interesting relationships and
characteristics that may exist implicitly in spatial databases. In this
paper, we explore whether clustering methods have a role to play in
spatial data mining. To this end, we develop a new clustering method
called CLARANS which is based on randomized search. We also develop two
spatial data mining algorithms that use CLARANS. Our analysis and
experiments show that with the assistance of CLARANS, these two
algorithms are very effective and can lead to discoveries that are
difficult to find with current spatial data mining algorithms.
Furthermore, experiments conducted to compare the performance of CLARANS
with that of existing clustering methods show that CLARANS is the most
efficient.</abstract></paper><paper><title>Performance of Data-Parallel Spatial Operations.</title><author><AuthorName>Erik G. Hoel</AuthorName><institute><InstituteName></InstituteName><country></country></institute></author><author><AuthorName>Hanan Samet</AuthorName><institute><InstituteName></InstituteName><country></country></institute></author><year>1994</year><conference>International Conference on Very Large Data Bases</conference><citation><name>The R*-Tree: An Efficient and Robust Access Method for Points and Rectangles.</name><name>Prototyping Bubba, A Highly Parallel Database System.</name><name>Efficient Processing of Spatial Joins Using R-Trees.</name><name>The Ubiquitous B-Tree.</name><name>Data Placement In Bubba.</name><name>GAMMA - A High Performance Dataflow Database Machine.</name><name>Parallel Database Systems: The Future of Database Processing or a Passing Fad?</name><name>Fundamentals of Database Systems.
 Benjamin/Cummings 1989</name><name>Analysis of Object Oriented Spatial Access Methods.</name><name>A Performance Analysis of Alternative Multi-Attribute Declustering Strategies.</name><name>R-Trees: A Dynamic Index Structure for Spatial Searching.</name><name>A Qualitative Comparison Study of Data Structures for Large Line Segment Databases.</name><name>Parallel R-trees.</name><name>Research Directions in Object-Oriented Database Systems.</name><name>The Design and Analysis of Spatial Data Structures.
 Addison-Wesley 1990</name><name>Parallel R-Tree Search Algorithm on DSVM.</name></citation><abstract>The performance of data-parallel algorithms for spatial operations
using data-parallel variants of the bucket PMR quadtree, R-tree, and
R+-tree spatial data structures is compared.  The studied
operations are data structure build, polygonization, and spatial join
in an application domain consisting of planar line segment data.  The
algorithms are implemented using the scan model of parallel
computation on the hypercube architecture of the Connection Machine.
The results of experiments reveal that the bucket PMR quadtree
outperforms both the R-tree and R+-tree.  This is primarily because
the bucket PMR quadtree yields a regular disjoint decomposition of
space while the R-tree and R+-tree do not.  The regular disjoint
decomposition increases the potential for interprocessor communication
and parallelism in the bucket PMR quadtree, thereby enabling the
execution times to decrease relative to those needed by the R-tree and
R+-tree.</abstract></paper><paper><title>The Impact of Global Clustering on Spatial Database Systems.</title><author><AuthorName>Thomas Brinkhoff</AuthorName><institute><InstituteName></InstituteName><country></country></institute></author><author><AuthorName>Hans-Peter Kriegel</AuthorName><institute><InstituteName></InstituteName><country></country></institute></author><year>1994</year><conference>International Conference on Very Large Data Bases</conference><citation><name>A Storage and Access Architecture for Efficient Query Processing in Spatial Database Systems.</name><name>Efficient Processing of Spatial Joins Using R-Trees.</name><name>The R*-Tree: An Efficient and Robust Access Method for Points and Rectangles.</name><name>Multi-Step Processing of Spatial Joins.</name><name>Query-Adaptive Data Space Partitioning using Variable-Size Storage Clusters.</name><name>Properties of Geographic Data: Requirements for Spatial Access Methods.</name><name>The BANG File: A New Kind of Grid File.</name><name>Transaction Processing: Concepts and Techniques.</name><name>R-Trees: A Dynamic Index Structure for Spatial Searching.</name><name>A Qualitative Comparison Study of Data Structures for Large Line Segment Databases.</name><name>Globally Order Preserving Multidimensional Linear Hashing.</name><name>The LSD tree: Spatial Access to Multidimensional Point and Nonpoint Objects.</name><name>The Grid File: An Adaptable, Symmetric Multikey File Structure.</name><name>Redundancy in Spatial Databases.</name><name>The Design and Analysis of Spatial Data Structures.
 Addison-Wesley 1990</name><name>The Sequoia 2000 Benchmark.</name><name>The Buddy-Tree: An Efficient and Robust Access Method for Spatial Data Base Systems.</name><name>Reading a Set of Disk Pages.</name><name>The R+-Tree: A Dynamic Index for Multi-Dimensional Objects.</name><name>Set-Oriented Disk Access to Large Complex Objects.</name></citation><abstract>Global clustering has rarely been investigated in the area of spatial
database systems although dramatic performance improvements can be