SQL Extension for Multidatabase System

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.

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Preserving autonomy in a heterogeneous multidatabase system

Proceedings COMPSAC 88: The Twelfth Annual International Computer Software & Applications Conference ◽

10.1109/cmpsac.1988.17181 ◽

2003 ◽

Cited By ~ 3

Author(s):

B. Holtkamp

Keyword(s):

Multidatabase System

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A prototype multidatabase system for soil databases

Computers & Geosciences ◽

10.1016/0098-3004(90)90067-4 ◽

1990 ◽

Vol 16 (3) ◽

pp. 331-339

Author(s):

Vassiliki J. Kollias ◽

Anastasios G. Malliris

Keyword(s):

Multidatabase System

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A CORBA-based multidatabase system—Panorama project

Wuhan University Journal of Natural Sciences ◽

10.1007/bf02902059 ◽

2003 ◽

Vol 8 (1) ◽

pp. 21-27

Author(s):

Lou Qin-jian ◽

Sarem Mudar ◽

Li Rui-xuan ◽

Xiao Wei-jun ◽

Lu Zheng-ding ◽

...

Keyword(s):

Multidatabase System

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View Definition and Generalization for Database Integration in a Multidatabase System

IEEE Transactions on Software Engineering ◽

10.1109/tse.1984.5010292 ◽

1984 ◽

Vol SE-10 (6) ◽

pp. 628-645 ◽

Cited By ~ 184

Author(s):

Umeshwar Dayal ◽

Hai-Yann Hwang

Keyword(s):

Database Integration ◽

Multidatabase System

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A TWO-LEVEL METADATA DICTIONARY APPROACH FOR SEMANTIC QUERY PROCESSING IN MULTIDATABASE SYSTEMS

International Journal of Software Engineering and Knowledge Engineering ◽

10.1142/s0218194093000112 ◽

1993 ◽

Vol 03 (02) ◽

pp. 231-255

Author(s):

MIIN-JENG PAN ◽

SHI-KUO CHANG ◽

CHIEN-CHIAO YANG

Keyword(s):

Query Processing ◽

Database Systems ◽

Database Integration ◽

Inference Control ◽

Semantic Query ◽

Multidatabase System ◽

Integration Strategy ◽

Experimental Implementation ◽

Schema Translation ◽

Two Phases

A multidatabase system (MDBS) is a system that integrates several autonomous database systems and provides users with a uniform access to all the databases. In this paper we developed a two-level active metadata dictionary approach for semantic query processing. To capture the global view of data schemas of participating databases which may be heterogeneous, a Hornclause data model is used. The lower-level metadata dictionaries (LLMDs) keep metadata for each corresponding local database in MDBS. The higher-level metadata dictionary (HLMD) integrates the metadata about all LLMDs. The database integration strategy includes two phases: schema translation and schema integration. It is a bottom-up approach integrating schema from the underlying database schemas. The evaluation strategy is a top-down approach. It starts with a query as a global goal to be achieved, unifies and optimizes the query to decompose the goal into subgoals that can be evaluated against extensional database, then translates these subgoals into corresponding queries against underlying DBMSs. To solve the control problem, we employ a G-net model for procedure control and inference control. An experimental implementation in Prolog is described.

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