Scaling access to heterogeneous data sources with DISCO

Abstract

Abstract| Accessing many data sources aggravates problems for users of heterogeneous distributed databases. Database administrators must deal with fragile mediators, that is, mediators with schemas and views that must be signi cantly changed to incorporate a new data source. When implementing translators of queries from mediators to data sources, database implementors must deal with data sources that do not support all the functionality required by mediators. Application programmers must deal with graceless failures for unavailable data sources. Queries simply return failure and no further information when data sources are unavailable for query processing. The Distributed Information Search COmponent ( Disco) addresses these problems. Data modeling techniques manage the connections to data sources, and sources can be added transparently to the users and applications. The interface between mediators and data sources exibly handles di erent query languages and different data source functionality. Query rewriting and optimization techniques rewrite queries so they are e ciently evaluated by sources. Query processing and evaluation semantics are developed to process queries over unavailable data sources. In this article we describe (a) the distributed mediator architecture of Disco (b) the data model and its modeling of data source connections (c) the interface to underlying data sources and the query rewriting process and (d) query processing semantics. We describe several advantages of our system.

Peer‐To‐Peer Grid Databases for Web Service Discovery

“…However, the set of information tuples in the universe is partitioned over one or more database nodes from a wide range of system topologies, for reasons including autonomy, scalability, availability, performance and security. As in a data integration system [9,10,11], the goal is to exploit several independent information sources as if they were a single source. This enables queries for information, resource and service discovery and collective collaborative functionality that operate on the system as a whole, rather than on a given part of it.…”

A Unified Peer-to-Peer Database Framework for Scalable Service and Resource Discovery

“…As in a data integration system [8][9][10], the goal is to exploit several independent information sources as if they were a single source. This is important for distributed systems in which node topology or deployment model change frequently.…”

“…As in a data integration system [8][9][10], the goal is to exploit several independent information sources as if they were a single source. However, in a large distributed database system spanning many administrative domains, the set of information tuples in the universe is partitioned over one or more distributed nodes, for reasons including autonomy, scalability, availability, performance and security.…”

Evaluation of Join Strategies for Distributed Mediation

“…[7,16,19]) propose centralized query compilation and execution coordination. In [3] it is indicated that a distributed mediation framework is a promising research direction, but to the extent of our knowledge the results in this area are sketchy without experimental support.…”

“…In [3] it is indicated that a distributed mediation framework is a promising research direction, but to the extent of our knowledge the results in this area are sketchy without experimental support. The protocols for execution of joins between data in different sources are in most cases based on retrieving the data from the sources and assembling the results in the mediator [16,19]. In the DIOM project [13], a distributed mediator system is presented where the query execution is performed in two phases: subquery execution and result assembly.…”