Scaling heterogeneous databases and the design of Disco

TLDR

Large-scale heterogeneous distributed databases create challenges for end users, application programmers, database administrators, and implementers, such as unavailable data sources, integration of new sources, and translation of queries across languages and schemas. This paper presents the distributed mediator architecture of Disco, detailing its query processing semantics, data model for source connections, and interface to underlying data sources. Disco implements query processing semantics that handle missing answers, uses data modeling techniques to manage source connections, and provides a flexible component interface that translates queries across languages and schemas. Disco effectively resolves the challenges of heterogeneous distributed databases by providing a mediator that handles unavailable sources, integrates new sources, and translates queries.

Abstract

Access to large numbers of data sources introduces new problems for users of heterogeneous distributed databases. End users and application programmers must deal with unavailable data sources. Database administrators must deal with incorporating new sources into the model. Database implementers must deal with the translation of queries between query languages and schemas. The Distributed Information Search COmponent (Disco) addresses these problems. Query processing semantics are developed to process queries over data sources which do not return answers. Data modeling techniques manage connections to data sources. The component interface to data sources flexibly handles different query languages and translates queries. This paper describes (a) the distributed mediator architecture of Disco, (b) its query processing semantics, (C) the data model and its modeling of data source connections, and (d) the interface to underlying data sources.