11 research outputs found
Adaptive Database Buffer Allocation Using Query Feedback
In this paper, we propose the concept of using query execution feedback
for improving database buffer management. A query feedback model which
adaptively quantifies the page fault characteristics of all query access
patterns including sequential, looping and most importantly random, is
defined. Based on this model, a load control and a marginal gain ratio
buffer allocation scheme are developed. Simulation experiments show that
the proposed method is consistently better than the previous methods and
in most cases, it significantly outperforms all other methods for random
access reference patterns.
(Also cross-referenced as UMIACS-TR-93-49
The Implementation and Performance Evaluation of the ADMS Query Optimizer: Integrating Query Result Caching and Matching
In this paper, we describe the design and evaluation of the
ADMS optimizer. Capitalizing on a structure called Logical Access Path
Schema to model the derivation relationship among cached query results,
the optimizer is able to perform query matching coincidently with the
optimization and generate more efficient query plans using cached
results. The optimizer also features data caching and pointer caching,
different cache replacement strategies, and different cache update
strategies. An extensive set of experiments were conducted, and the
results showed that pointer caching and dynamic cache update strategies
substantially speedup query computations and, thus, increase query
throughput under situations with fair query correlation and update
load. The requirement of the cache space is relatively small and the
extra computation overhead introduced by the caching and matching
mechanism is more than offset by the time saved in query processing.
(Also cross-referenced as UMIACS-TR-93-106
Adaptive Cost Estimation for Client-Server based Heterogeneous Database Systems
In this paper, we propose a new method for estimating query cost in
client-server based heterogeneous database management system.
The cost estimation parameters are adjusted by an
Adaptive Cost Estimation (ACE) module which uses query execution feedback
yielding more and more accurate cost estimates.
The most important features of ACE are its
detailed cost model which accounts for all costs incurred,
its rapid convergence to the actual parameter values,
and its low overhead which
permits continuous adaptation during the run time of the system.
ACE has been implemented and tested
with Oracle 6, Oracle 7, Ingres, and ADMS. Extensive experiments performed
on these systems show that the ACE's time estimates are
within 20% of the real wall-clock time
for more than 92% of the queries. This percentage surpasses 98%
for queries over 20 seconds.
(Also cross-referenced as UMIACS-TR-96-37
Abstract Adaptive Selectivity Estimation Using Query Feedback
In this paper, we propose a novel approach for estimating the record selectivities of database queries. The real attribute value distribution is adaptively approximated by a curvetting function using a query feedback mechanism. This approach has the advantages of requiring no extra database access overhead for gathering statistics and of being able to continuously adapt the value distribution through queries and updates. Experimental results show that the estimation accuracy of this approach is comparable to traditional methods based on statistics gathering.
Adaptive Selectivity Estimation Using Query Feedback
In this paper, we propose a novel approach for estimating the record selectivities of database queries. The real attribute value distribution is adaptively approximated by a curve-fitting function using a query feedback mechanism. This approach has the advantages of requiring no extra database access overhead for gathering statistics and of being able to continuously adapt the value distribution through queries and updates. Experimental results show that the estimation accuracy of this approach is comparable to traditional methods based on statistics gathering. 1 Introduction In most database systems, the task of query optimization is to choose an efficient execution plan. Best plan selection requires accurate estimates of the costs of alternative plans. One of the most important factors that affects plan cost is selectivity, which is the number of tuples satisfying a given predicate. Therefore, in most cases, the accuracy of selectivity estimates directly affects the choice of best p..