vldb_1996_elementary.txt

利用lwp：：get写的

<proceedings><paper><title>The Changing Landscape of the Software Industry and its Implications for India.</title><author><AuthorName>Umang Gupta</AuthorName><institute><InstituteName></InstituteName><country></country></institute></author><year>1996</year><conference>International Conference on Very Large Data Bases</conference><citation></citation><abstract>As we approach the next century, the software industry landscape is
undergoing massive technology and business changes. The client/server
revolution has barely reached its half-life and it is already
being eclipsed by the Internet revolution. Software development is
moving away from the direction of being labour-intensive. Customers
are buying more pre-packaged software solutions or software
components that can easily be assembled on site by in-house
personnel or systems integrators.  Except for a handful of players
like Microsoft, Oracle and Computer Associates, very few leading
software companies of the seventies and eighties have survived into
the nineties. A whole new generation of software
companies have emerged that are focussed on selling advanced
software components based on industry standards. For Indian software
companies with superior technology development skills, the Internet
will open up opportunities to build products that have never been
built before and to enter global markets on a scale that was never
attempted before.</abstract></paper><paper><title>Very Large Databases in a Commercial Application Environment.</title><author><AuthorName>Karl-Heinz Hess</AuthorName><institute><InstituteName></InstituteName><country></country></institute></author><year>1996</year><conference>International Conference on Very Large Data Bases</conference><citation></citation><abstract>When very large databases are mentioned, one generally thinks of video 
servers, document servers, etc. However classical commercial applications, such 
as accounting or logistics also generate enormous ammounts of data when several 
thousand end users are accessing a central database. Since mission critical 
applications are involved here, such databases have to satisfy high demands 
regarding administration, throughput and reliability. The presentation focuses 
on these aspects from the
SAP point of view.</abstract></paper><paper><title>Of Objects and Databases: A Decade of Turmoil.</title><author><AuthorName>Michael J. Carey</AuthorName><institute><InstituteName></InstituteName><country></country></institute></author><author><AuthorName>David J. DeWitt</AuthorName><institute><InstituteName></InstituteName><country></country></institute></author><year>1996</year><conference>International Conference on Very Large Data Bases</conference><citation><name>Combining Language and Database Advances in an Object-Oriented Development Environment.</name><name>Types and Persistence in Database Programming Languages.</name><name>The Object-Oriented Database System Manifesto.</name><name>Data Model Issues for Object-Oriented Applications.</name><name>GENESIS: A Project to Develop an Extensible Database Management System.</name><name>Data Access for the Masses through OLE DB.</name><name>Object and File Management in the EXODUS Extensible Database System.</name><name>The Architecture of the EXODUS Extensible DBMS.</name><name>A Data Model and Query Language for EXODUS.</name><name>Shoring Up Persistent Applications.</name><name>Towards Heterogeneous Multimedia Information Systems: The Garlic Approach.</name><name>The Object Database Standard: ODMG-93 (Release 1.2).</name><name>Third-Generation Database System Manifesto - The Committee for Advanced DBMS Function.</name><name>Making Smalltalk a Database System.</name><name>The Third Manifesto.</name><name>A Storage System for Complex Objects.</name><name>Client-Server Paradise.</name><name>1986 International Workshop on Object-Oriented Database Systems, September 23-26, 1986, Asilomar Conference Center, Pacific Grove, California, USA, Proceedings.
 IEEE Computer Society 1986, ISBN 0-8186-0734-3</name><name>The TSIMMIS Approach to Mediation: Data Models and Languages.</name><name>Object-Oriented Database Systems: Promises, Reality, and Future.</name><name>Modern Database Systems: The Object Model, Interoperability, and Beyond.
 ACM Press and Addison-Wesley 1995, ISBN 0-201-59098-0</name><name>Development of an Object-Oriented DBMS.</name><name>Implementation of Data Abstraction in the Relational Database System Ingres.</name><name>Extensibility in the Starburst Database System.</name><name>The Functional Data Model and the Data Language DAPLEX.</name><name>Inclusion of New Types in Relational Data Base Systems.</name><name>Object Management in Postgres Using Procedures.</name><name>Readings in Database Systems, Second Edition.</name><name>Object-Relational DBMSs: The Next Great Wave.</name><name>The Database Language GEM.</name><name>Readings in Object-Oriented Database Systems.</name></citation><abstract>A decade ago, the connection between objects and databases was new
and was being explored in a number of different ways within our community.
Driven by the perception that managing traditional business data was
largely a solved problem, projects were investigating ideas such as
adding abstract data types to relational databases and building
extensible database systems, object-oriented database systems, and
toolkits for constructing special-purpose database systems.
In addition, work was underway elsewere in the computer science research
community on extending programming languages with database-inspired
features such as persistence and transactions.
In this paper, we take a look at where our filed was a decade ago and
where it is now in terms of database support for objects (and vice versa).
We look both at research projects and at commercial database products.
We share our vision and our biases about the future of objects and
databases, and we identify a number of research challenges that remain
to be addressed in order to ultimately achieve our vision.</abstract></paper><paper><title>Filter Trees for Managing Spatial Data over a Range of Size Granularities.</title><author><AuthorName>Kenneth C. Sevcik</AuthorName><institute><InstituteName></InstituteName><country></country></institute></author><author><AuthorName>Nick Koudas</AuthorName><institute><InstituteName></InstituteName><country></country></institute></author><year>1996</year><conference>International Conference on Very Large Data Bases</conference><citation><name>Space-Filling Curves: Their Generation and Their Application to Bandwidth Reduction.</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>R-Trees: A Dynamic Index Structure for Spatial Searching.</name><name>The R-File: An Efficient Access Structure for Proximity Queries.</name><name>Linear Clustering of Objects with Multiple Atributes.</name><name>On Packing R-trees.</name><name>Hilbert R-tree: An Improved R-tree using Fractals.</name><name>Efficient and Effective Clustering Methods for Spatial Data Mining.</name><name>PROBE Spatial Data Modeling and Query Processing in an Image Database Application.</name><name>The Design and Analysis of Spatial Data Structures.
 Addison-Wesley 1990</name><name>The R+-Tree: A Dynamic Index for Multi-Dimensional Objects.</name><name>Spatial Searching in Geometric Databases.</name></citation><abstract>We  introduce  a  new  file  organization  for  the  storage  and
manipulation  of spatial (or multi-dimensional) data that is able
to execute spatial join operations with  great  efficiency.   The
Filter  Tree information structure is a hierarchical organization
that tends to separate spatial entities by size, placing larger e
ntities  at  the  higher  levels  of the Filter Tree, and smaller
entities at lower levels.  Within each level, index entries   for
the  entities  are  ordered  by  a  space-filling  curve (Hilbert
curve).  This allows the algorithms to  use  bulk  I/O  requests,
exploiting  the locality in the index information, and minimizing
the number of I/O transfers from disk.  We provide algorithms for
constructing  Filter  Trees,  for  performing  range queries on a
Filter Tree, and for performing spatial joins between a  pair  of
Filter Trees.
Finally, we include results from experiments  using  a  prototype
implementation of Filter Trees to treat both synthetic and real s
ets of spatial entities. Our experimental results show that  full
spatialjoins can always be done more efficiently with Filter Trees 
than with  curre  nt  competitive  methods, and that in some cases 
the improvement in performance is very large.</abstract></paper><paper><title>The X-tree : An Index Structure for High-Dimensional Data.</title><author><AuthorName>Stefan Berchtold</AuthorName><institute><InstituteName></InstituteName><country></country></institute></author><author><AuthorName>Daniel A. Keim</AuthorName><institute><InstituteName></InstituteName><country></country></institute></author><author><AuthorName>Hans-Peter Kriegel</AuthorName><institute><InstituteName></InstituteName><country></country></institute></author><year>1996</year><conference>International Conference on Very Large Data Bases</conference><citation><name>Efficient Similarity Search In Sequence Databases.</name><name>The R*-Tree: An Efficient and Robust Access Method for Points and Rectangles.</name><name>An Introduction to Mathematical Taxonomy.
 Cambridge University Press 1982</name><name>Efficient and Effective Querying by Image Content.</name><name>FastMap: A Fast Algorithm for Indexing, Data-Mining and Visualization of Traditional and Multimedia Datasets.</name><name>R-Trees: A Dynamic Index Structure for Spatial Searching.</name><name>Spatial Database Indices for Large Extended Objects.</name><name>A Retrieval Technique for Similar Shapes.</name><name>Techniques for Automatically Correcting Words in Text.</name><name>The TV-Tree: An Index Structure for High-Dimensional Data.</name><name>Feature-Based Retrieval of Similar Shapes.</name><name>Feature-Index-Based Similar Shape Retrieval.</name><name>The Grid File: An Adaptable, Symmetric Multikey File Structure.</name><name>Nearest Neighbor Queries.</name><name>The K-D-B-Tree: A Search Structure For Large Multidimensional Dynamic Indexes.</name><name>The Buddy-Tree: An Efficient and Robust Access Method for Spatial Data Base Systems.</name><name>The R+-Tree: A Dynamic Index for Multi-Dimensional Objects.</name><name>Similarity Indexing with the SS-tree.</name><name>On Optimizing Nearest Neighbor Queries in High-Dimensional Data Spaces.</name></citation><abstract>In this paper, we propose a new method for indexing large amounts of point 
and spatial data in high-dimensional space. An analysis shows that index 
structures such as the R*-tree are not adequate for indexing high-dimensional 
data sets. The major problem of R-tree-based index structures is the overlap 
of the bounding boxes in the directory, which increases with growing 
dimension. To avoid this problem, we introduce a new organization of the 
directory which uses a split algorithm minimizing overlap and the concept 
of supernodes. The basic idea of overlap-minimizing split and supernodes is 
to keep the directory as hierarchical as possible, and at the same time 
avoiding splits in the directory that would result in high overlap. Our 
experiments show that for high-dimensional data, the X-tree outperforms the 
well-known TV-tree and the R*-tree by orders of magnitude.</abstract></paper><paper><title>Analysis of n-Dimensional Quadtrees using the Hausdorff Fractal Dimension.</title><author><AuthorName>Christos Faloutsos</AuthorName><institute><InstituteName></InstituteName><country></country></institute></author><author><AuthorName>Volker Gaede</AuthorName><institute><InstituteName></InstituteName><country></country></institute></author><year>1996</year><conference>International Conference on Very Large Data Bases</conference><citation><name>QBISM: Extending a DBMS to Support 3D Medical Images.</name><name>Estimating the Selectivity of Spatial Queries Using the `Correlation' Fractal Dimension.</name><name>The R*-Tree: An Efficient and Robust Access Method for Points and Rectangles.</name><name>Analysis of the n-Dimensional Quadtree Decomposition for Arbitrary Hyperectangles.</name><name>Beyond Uniformity and Independence: Analysis of R-trees Using the Concept of Fractal Dimension.</name><name>Fast Subsequence Matching in Time-Series Databases.</name><name>Geometric Information Makes Spatial Query Processing More Efficient.</name><name>Optimal Redundancy in Spatial Database Systems.</name><name>An Effective Way to Represent Quadtrees.</name><name>Spatial Access Methods and Query Processing in the Object-Oriented GIS GODOT.</name><name>R-Trees: A Dynamic Index Structure for Spatial Searching.</name><name>An Introduction to Spatial Database Systems.</name><name>A Retrieval Technique for Similar Shapes.</name><name>Fractal Geometry of Nature.
 W. H. Freeman 1977</name><name>A Class of Data Structures for Associative Searching.</name><name>PROBE Spatial Data Modeling and Query Processing in an Image Database Application.</name><name>Redundancy in Spatial Databases.</name><name>Fractals, Chaos, Power Laws: Minutes From an Infinite Paradise.
 W. H. Freeman 1991</name><name>The R+-Tree: A Dynamic Index for Multi-Dimensional Objects.</name><name>When Is ''Nearest Neighbor'' Meaningful?</name></citation><abstract>There is mounting evidence
[Man77, Sch91]
that real datasets are statistically self-similar,
and thus, `fractal'.
This is an important insight since it permits a compact statistical
description of
spatial datasets;
subsequently, as we show,
it also forms the basis for the theoretical analysis of spatial access 
methods, without using the typical,