A distributed algorithm for global query optimization in multidatabase systems

1998 ◽  
pp. 95-106 ◽  
Author(s):  
Silvio Salza ◽  
Giovanni Barone ◽  
Tadeusz Morzy
Keyword(s):  
Query Optimization ◽  
Distributed Algorithm ◽  
Multidatabase Systems ◽  
Global Query

CLASSIFYING LOCAL QUERIES FOR GLOBAL QUERY OPTIMIZATION IN MULTIDATABASE SYSTEMS

2000 ◽  
Vol 09 (03) ◽  
pp. 315-355 ◽  
Author(s):  
QIANG ZHU ◽  
P.-Å. LARSON
Keyword(s):  
Query Optimization ◽  
Database Systems ◽  
Cost Models ◽  
Classification Rules ◽  
Top Down ◽  
Bottom Up ◽  
Multidatabase System ◽  
Local Database ◽  
Hybrid Classification ◽  
Global Query

A multidatabase system (MDBS) integrates information from multiple pre-existing local databases. A major challenge for global query optimization in an MDBS is that some required local information about local database systems such as local cost models may not be available at the global level due to local autonomy. A feasible method to tackle this challenge is to group local queries on a local database system into classes and then use the costs of sample queries from each query class to derive a cost formula for the class via regression analysis. 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 classification approaches, i.e. bottom-up and top-down, are suggested. The relationship between these two approaches is discussed. Classification rules that can be used in the approaches are identified. Problems regarding composition and redundancy of classification rules are studied. Classification algorithms are given. To test the membership of a query in a class, an efficient algorithm based on ranks is introduced. In addition, a hybrid classification approach that combines the bottom-up and top-down ones is also suggested. Experimental results demonstrate that the suggested query classification techniques can be used to derive good local cost formulas for global query optimization in an MDBS.

Global query optimization

1986 ◽  
Vol 15 (2) ◽  
pp. 191-205 ◽  
Author(s):  
Timos K. Sellis
Keyword(s):  
Query Optimization ◽  
Global Query

A Fuzzy Query Optimization Approach for Multidatabase Systems

1997 ◽  
Vol 05 (06) ◽  
pp. 701-722 ◽  
Author(s):  
Qiang Zhu ◽  
Per-Åke Larson
Keyword(s):  
Query Optimization ◽  
Cost Model ◽  
Fuzzy Optimization ◽  
Optimization Criterion ◽  
Optimization Techniques ◽  
Optimization Approach ◽  
Fuzzy Query ◽  
Execution Strategy ◽  
Fuzzy Cost ◽  
Global Query

A crucial challenge for global query optimization in a multidatabase system (MDBS) is that some local optimization information, such as local cost parameters, may not be accurately known at the global level because of local autonomy. Traditional query optimization techniques using a crisp cost model may not be suitable for an MDBS because precise information is required. In this paper we present a new approach that performs global query optimization using a fuzzy cost model that allows fuzzy information. We suggest methods for establishing a fuzzy cost model and introduce a fuzzy optimization criterion that can be used with a fuzzy cost model. We discuss the relationship between the fuzzy optimization approach and the traditional (crisp) optimization approach and show that the former has a better chance to find a good execution strategy for a query in an MDBS environment, but its complexity may grow exponentially compared with the complexity of the later. To reduce the complexity, we suggest to use so-called k-approximate fuzzy values to approximate all fuzzy values during fuzzy query optimization. It is proven that the improved fuzzy approach has the same order of complexity as the crisp approach.

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