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ABSTRACT
Distributed data processing is becoming a reality. Businesses want to do it for many reasons, and they often must do it in order to stay competitive. While much of the infrastructure for distributed data processing is already there (e.g., modern network technology), a number of issues make distributed data processing still a complex undertaking: (1) distributed systems can become very large, involving thousands of heterogeneous sites including PCs and mainframe server machines; (2) the state of a distributed system changes rapidly because the load of sites varies over time and new sites are added to the system; (3) legacy systems need to be integrated—such legacy systems usually have not been designed for distributed data processing and now need to interact with other (modern) systems in a distributed environment. This paper presents the state of the art of query processing for distributed database and information systems. The paper presents the “textbook” architecture for distributed query processing and a series of techniques that are particularly useful for distributed database systems. These techniques include special join techniques, techniques to exploit intraquery paralleli sm, techniques to reduce communication costs, and techniques to exploit caching and replication of data. Furthermore, the paper discusses different kinds of distributed systems such as client-server, middleware (multitier), and heterogeneous database systems, and shows how query processing works in these systems.
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INDEX TERMS
Primary Classification:
E.
Data
Additional Classification:
H.
Information Systems
H.2
DATABASE MANAGEMENT
H.2.4
Systems
Subjects:
Query processing;
Distributed databases
H.2.5
Heterogeneous Databases
Subjects:
Data translation**
General Terms:
Algorithms,
Performance
Keywords:
caching,
client-server databases,
database application systems,
dissemination-based information systems,
economic models for query processing,
middleware,
multitier architectures,
query execution,
query optimization,
replication,
wrappers
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