vldb_1999_elementary.txt

利用lwp：：get写的

<proceedings><paper><title>Repeating History Beyond ARIES.</title><author><AuthorName>C. Mohan</AuthorName><institute><InstituteName></InstituteName><country></country></institute></author><year>1999</year><conference>International Conference on Very Large Data Bases</conference><citation><name>Semantics-Based Concurrency Control: Beyond Commutativity.</name><name>Concurrency Control and Recovery in Database Systems.
 Addison-Wesley 1987, ISBN 0-201-10715-5</name><name>Principles of Transaction Processing for Systems Professionals.</name><name>An Efficient Scheme for Providing High Availability.</name><name>Robustness to Crash in a Distributed Database: A Non Shared-memory Multi-Processor Approach.</name><name>A History and Evaluation of System R.</name><name>Applying Database Technology in the ADSM Mass Storage System.</name><name>Shoring Up Persistent Applications.</name><name>A History of System R and SQL/Data System (Invited Paper).</name><name>OS/2 EE Database Manager Overview and Technical Highlights.</name><name>Application System/400 Performance Characteristics.</name><name>Implementing Atomicity in Two Systems: Techniques, Tradeoffs, and Experience.</name><name>Data Recovery in IBM Database 2.</name><name>The Gamma Database Machine Project.</name><name>ARIES/NT Modified for Advanced Transactions Support.</name><name>The Notions of Consistency and Predicate Locks in a Database System.</name><name>Database Transaction Models for Advanced Applications.</name><name>Crash Recovery in Client-Server EXODUS.</name><name>Varieties of Concurrency Control in IMS/VS Fast Path.</name><name>The Recovery Manager of the System R Database Manager.</name><name>Transaction Processing: Concepts and Techniques.</name><name>IBM Database 2 Overview.</name><name>Principles of Transaction-Oriented Database Recovery.</name><name>Concepts for Transaction Recovery in Nested Transactions.</name><name>Concurrency Control Issues in Nested Transactions.</name><name>Starburst Mid-Flight: As the Dust Clears.</name><name>Advanced Transaction Models and Architectures.
 Kluwer 1997, ISBN 0-7923-9880-7</name><name>Analysis of Recovery in a Database System Using a Write-Ahead Log Protocol.</name><name>DB2's Use of the Coupling Facility for Data Sharing.</name><name>Concurrency and Recovery in Generalized Search Trees.</name><name>Model and Verification of a Data Manager Based on ARIES.</name><name>Linear Hashing with Separators - A Dynamic Hashing Scheme Achieving One-Access Retrieval.</name><name>MLR: A Recovery Method for Multi-level Systems.</name><name>Access Method Concurrency with Recovery.</name><name>Redo Recovery after System Crashes.</name><name>Toward Formalizing Recovery of (Advanced) Transactions.</name><name>ARIES: A Transaction Recovery Method Supporting Fine-Granularity Locking and Partial Rollbacks Using Write-Ahead Logging.</name><name>Single Table Access Using Multiple Indexes: Optimization, Execution, and Concurrency Control Techniques.</name><name>Recent Work on Distributed Commit Protocolls, and Recoverable Messaging and Queuing.</name><name>Algorithms for Flexible Space Management in Transaction Systems Supporting Fine-Granularity Locking.</name><name>ARIES/KVL: A Key-Value Locking Method for Concurrency Control of Multiaction Transactions Operating on B-Tree Indexes.</name><name>Commit_LSN: A Novel and Simple Method for Reducing Locking and Latching in Transaction Processing Systems.</name><name>Interactions Between Query Optimization and Concurrency Control.</name><name>Less Optimism About Optimistic Concurrency Control.</name><name>ARIES/LHS: A Concurrency Control and Recovery Method Using Write-Ahead Logging for Linear Hashing with Separators.</name><name>IBM's Relational DBMS Products: Features and Technologies.</name><name>A Cost-Effective Method for Providing Improved Data Availability During DBMS Restart Recovery After a Failure.</name><name>A Survey of DBMS Research Issues in Supporting Very Large Tables.</name><name>Disk Read-Write Optimizations and Data Integrity in Transaction Systems Using Write-Ahead Logging.</name><name>A Database Perspective on Lotus Domino/Notes.</name><name>ARIES/IM: An Efficient and High Concurrency Index Management Method Using Write-Ahead Logging.</name><name>Transaction Management in the  R* Distributed Database Management System.</name><name>Recovery and Coherency-Control Protocols for Fast Intersystem Page Transfer and Fine-Granularity Locking in a Shared Disks Transaction Environment.</name><name>Efficient Locking and Caching of Data in the Multisystem Shard Disks Transaction Environment.</name><name>Data Base Recovery in Shared Disks and Client-Server Architectures.</name><name>Algorithms for Creating Indexes for Very Large Tables Without Quiescing Updates.</name><name>An Efficient and Flexible Method for Archiving a Data Base.</name><name>ARIES/CSA: A Method for Database Recovery in Client-Server Architectures.</name><name>ARIES-RRH: Restricted Repeating of History in the ARIES Transaction Recovery Method.</name><name>Efficient and Flexible Methods for Transient Versioning of Records to Avoid Locking by Read-Only Transactions.</name><name>Algorithms for the Management of Remote Backup Data Bases for Disaster Recovery.</name><name>LOG Write-Ahead Protocols and IMS/VS Logging.</name><name>Database Management Systems.
 WCB/McGraw-Hill 1998, ISBN 0-07-050775-9</name><name>ARIES/NT: A Recovery Method Based on Write-Ahead Logging for Nested Transactions.</name><name>VM/ESA CMS Shared File System.</name><name></name></citation><abstract>In this paper, I describe first the background behind
the development of the original ARIES recovery
method, and its significant impact on the
commercial world and the research community.
Next, I provide a brief introduction to the various
concurrency control and recovery methods in the
ARIES family of algorithms. Subsequently, I discuss
some of the recent developments affecting the
transaction management area and what these mean
for the future. In ARIES, the concept of repeating
history turned out to be an important paradigm. As I
examine where transaction management is headed
in the world of the internet, I observe history
repeating itself in the sense of requirements that
used to be considered significant in the mainframe
world (e.g., performance, availability and reliability)
now becoming important requirements of the
broader information technology community as well.</abstract></paper><paper><title>Online Feedback for Nested Aggregate Queries with Multi-Threading.</title><author><AuthorName>Kian-Lee Tan</AuthorName><institute><InstituteName></InstituteName><country></country></institute></author><author><AuthorName>Cheng Hian Goh</AuthorName><institute><InstituteName></InstituteName><country></country></institute></author><author><AuthorName>Beng Chin Ooi</AuthorName><institute><InstituteName></InstituteName><country></country></institute></author><year>1999</year><conference>International Conference on Very Large Data Bases</conference><citation><name>Processing Queries for First Few Answers.</name><name>Of Nests and Trees: A Unified Approach to Processing Queries That Contain Nested Subqueries, Aggregates, and Quantifiers.</name><name>Optimization of Nested SQL Queries Revisited.</name><name>Large-Sample and Deterministic Confidence Intervals for Online Aggregation.</name><name>Ripple Joins for Online Aggregation.</name><name>Selectivity and Cost Estimation for Joins Based on Random Sampling.</name><name>Online Aggregation.</name><name>Statistical Estimators for Relational Algebra Expressions.</name><name>Processing Aggregate Relational Queries with Hard Time Constraints.</name><name>On Optimizing an SQL-like Nested Query.</name><name>Efficient Sampling Strategies for Relational Database Operations.</name><name>Optimization of Nested Queries in a Distributed Relational Database.</name><name>Random Sampling from Databases.
Ph.D. thesis,  University of California at Berkeley LBL Technical Report 1993</name><name>On Getting Some Answers Quickly, and Perhaps More Later.</name><name>Dataflow Query Execution in a Parallel Main-Memory Environment.</name></citation><abstract>In the paper, we study the progressive evaluation of
nested queries with aggregates (i.e., the inner query
block is an aggregate query), where users are
provided progressively with (approximate) answers as the
inner block is evaluated.
We propose an incremental evaluation strategy to present
answers that are certainly in the final answer space
first, before presenting those whose validity may be
affected as the inner query aggregates are refined.
We also propose a multithreaded model in
evaluating such queries: the outer query is assigned to a
thread, and the inner query is assigned to another thread.
The time-sliced across the two subqueries is
nondeterministic in the sense that the user
controls the relative rate at which these subqueries are
being evaluated.
We implemented a prototype system using JAVA,
and evaluated our system.
Our results show the effectiveness of the proposed
mechanisms in providing online feedback that reduce the
initial waiting time of users significantly without
sacrificing on the quality of the answers.</abstract></paper><paper><title>Generalised Hash Teams for Join and Group-by.</title><author><AuthorName>Alfons Kemper</AuthorName><institute><InstituteName></InstituteName><country></country></institute></author><author><AuthorName>Donald Kossmann</AuthorName><institute><InstituteName></InstituteName><country></country></institute></author><author><AuthorName>Christian Wiesner</AuthorName><institute><InstituteName></InstituteName><country></country></institute></author><year>1999</year><conference>International Conference on Very Large Data Bases</conference><citation><name>Implementing a Relational Database by Means of Specialized Hardware.</name><name>Space/Time Trade-offs in Hash Coding with Allowable Errors.</name><name>Hashing Methods and Relational Algebra Operations.</name><name>Analysis and Performance of Inverted Data Base Structures.</name><name>Bitmap Index Design and Evaluation.</name><name>Efficient Evaluation of Aggregates on Bulk Types.</name><name>A Signature Access Method for the Starburst Database System.</name><name>Including Group-By in Query Optimization.</name><name>Hash Joins and Hash Teams in Microsoft SQL Server.</name><name>Multi-Table Joins Through Bitmapped Join Indices.</name><name>Query Evaluation Techniques for Large Databases.</name><name>Sort-Merge-Join: An Idea Whose Time Has(h) Passed?</name><name>Quickly Generating Billion-Record Synthetic Databases.</name><name>Seeking the Truth About ad hoc Join Costs.</name><name>Evaluation of Main Memory Join Algorithms for Joins with Set Comparison Join Predicates.</name><name>Join Algorithm Costs Revisited.</name><name>Diag-Join: An Opportunistic Join Algorithm for 1:N Relationships.</name><name>Model 204 Architecture and Performance.</name><name>Improved Query Performance with Variant Indexes.</name><name>Join Processing in Database Systems with Large Main Memories.</name><name>Principles of Database  and Knowledge-Base Systems, Volume II.
 Computer Science Press 1989, ISBN 0-7167-8162-X</name><name>Join and Semijoin Algorithms for a Multiprocessor Database Machine.</name><name>Encoded Bitmap Indexing for Data Warehouses.</name><name>Performing Group-By before Join.</name></citation><abstract>We propose a new class of algorithms that can be used to
speed up the execution of multi-way join queries or of
queries that involve one or more joins and a group-by.
These new evaluation techniques allow to perform several
hash-based operations (join and grouping) in one pass
without repartitioning intermediate results. These
techniques work particularly well for joining hierarchical
structures, e.g., for evaluating functional join chains
along key/foreign-key relationships. The idea is to
generalize the concept of hash teams as proposed by
Graefe et.al
[GBC98]
by indirectly partitioning the input data.
Indirect partitioning means to partition the input data
on an attribute that is not directly needed for the next
hash-based operation, and it involves the construction of
bitmaps to approximate the partitioning for the attribute
that is needed in the next hash-based operation.
Our performance experiments show that such generalized
hash teams perform significantly better than conventional
strategies for many common classes of decision support
queries.</abstract></paper><paper><title>Explaining Differences in Multidimensional Aggregates.</title><author><AuthorName>Sunita Sarawagi</AuthorName><institute><InstituteName></InstituteName><country></country></institute></author><year>1999</year><conference>International Conference on Very Large Data Bases</conference><citation><name>An Overview of Data Warehousing and OLAP Technology.</name><name>Discovery of Multiple-Level Association Rules from Large Databases.</name><name>Discovery-Driven Exploration of OLAP Data Cubes.</name><name>Knowledge Discovery in Data Warehouses.</name></citation><abstract>Our goal is to enhance multidimensional
database systems with advanced mining primitives.
Current Online Analytical Processing (OLAP) products
provide a minimal set of basic aggregate operators like
sum and average and a set of basic navigational operators
like drill-downs and roll-ups. These operators have to be
driven entirely by the analyst's intuition. Such ad hoc
exploration gets tedious and error-prone as data
dimensionality and size increases. In earlier work we
presented one such advanced primitive where we premined
OLAP data for exceptions, summarized the exceptions at
appropriate levels, and used them to lead the analyst to
the interesting regions.
In this paper we present a second enhancement:
a single operator that lets the analyst get summarized
reasons for drops or increases observed at an aggregated
level. This eliminates the need to manually drill-down
for such reasons. We develop an information theoretic
formulation for expressing the reasons that is compact
and easy to interpret. We design a dynamic programming
algorithm that requires only one pass of the data improving
significantly over our initial greedy algorithm that
required multiple passes. In addition, the algorithm
uses a small amount of memory independent of the data size.
This allows easy integration with existing OLAP products.
We illustrate with our prototype on the DB2/UDB ROLAP
product with the Excel Pivot-table frontend. Experiments on
this prototype using the OLAP data benchmark demonstrate
(1) scalability of our algorithm as the size and
dimensionality of the cube increases and