In a multidatabase system (MDBS), some query optimization information related to local database systems may not be available at the global level because of local autonomy. To perform global query optimization, a method is required to derive the necessary local information. This paper presents a new method that employs a query sampling technique to estimate the cost parameters of an autonomous local database system. We introduce a classification for grouping local queries and suggest a cost estimation formula for the queries in each class. We present a procedure to draw a sample of queries from each class and use the observed costs of sample queries to determine the cost parameters by multiple regression. Experimental results indicate that the method is quite promising for estimating the cost of local queries in an MDBS.

. To meet users' growing needs for accessing pre-existing heterogeneous databases, a multidatabase system (MDBS) integrating multiple databases has attracted many researchers recently. A key feature of an MDBS is local autonomy. For a query retrieving data from multiple databases, global query optimization should be performed to achieve good system performance. There are a number of new challenges for global query optimization in an MDBS. Among them, a major one is that some local optimization information, such as local cost parameters, may not be available at the global level because of local autonomy. It creates difficulties for finding a good decomposition of a global query during query optimization. To tackle this challenge, a new query sampling method is proposed in this paper. The idea is to group component queries into homogeneous classes, draw a sample of queries from each class, and use observed costs of sample queries to derive a cost formula for each class by multiple regres...

A multidatabase system (MDBS) integrates information from autonomous local databases managed by different database management systems (MDBS) in a distributed environment. A number of challenges are raised for query optimization in such an MDBS. One of the major challenges is that some local optimization information may not be available at the global level. We recently proposed a query sampling method to drive cost estimation formulas for local databases in an MDBS [22] . To use the derived formulas to estimate the costs of queries, we need to know the selectivities of the qualifications of the queries. Unfortunately, existing methods for estimating selectivities cannot be used efficiently in an MDBS environment. This paper discusses difficulties of estimating selectivities in an MDBS. Based on the discussion, this paper presents an integrated method to estimate selectivities in an MDBS. The method integrates and extends several existing methods so that they can be used in an MDBS eff...

A major challenge for global query optimization in a multidatabase system (MDBS) is lack of local cost information at the global level due to local autonomy. A number of methods to derive local cost models have been suggested recently. However, these methods are only suitable for a static multidatabase environment. In this paper, we propose a new multi-states query sampling method to develop local cost models for a dynamic environment. The system contention level at a dynamic local site is divided into a number of discrete contention states based on the costs of a probing query. To determine an appropriate set of contention states for a dynamic environment, two algorithms based on iterative uniform partition and data clustering, respectively, are introduced. A qualitative variable is used to indicate the contention states for the dynamic environment. The techniques from our previous (static) query sampling method, including query sampling, automatic variable selection, regression analy...

In virtually every organization, data is stored in a variety of ways and managed by different database and file systems. Applications that require data from multiple sources are complex because they must be aware of and deal with the specifics of each data source. They must also perform any data integration needed, for example, joining data from multiple sources. The objective of a multidatabase system is to provide application developers and end users with an integrated view of and a uniform interface to all the required data. The view and the interface should be independent of where the data is stored and how it is managed. cords is a research project focussed on distributed applications. It is a collaborative effort involving ibm and several universities. As part of this project, we are designing and prototyping a multidatabase system. This paper provides an overview of its architecture and describes the approach taken in the following areas: management of catalog information, sch...

This paper discusses the issues on how to classify local queries so that a good cost formula can be derived for each query class. Two classication approaches, i.e., bottom-up and top-down, are suggested. The relationship between these two approaches is discussed. Classication rules that can be used in the approaches are identied. Problems regarding composition and redundancy of classication rules are studied. Classication algorithms are given. To test the membership of a query in a class, an ecient algorithm based on ranks is introduced. In addition, a hybrid classication approach that combines the bottom-up and top-down ones is also suggested

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. 1 Introduction Advances in relational database technologies have enabled large organizations and companies to store and manage unprecedented...