Federated database systems for managing distributed, heterogeneous, and autonomous databases

By Amit P. Sheth. 1990

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Citations in the corpus

Federated Machine Learning (2019, background): “Federated database systems [57] are systems that integrate multiple database units andmanage the integrated system as a whole.”
Semantic E-Workflow Composition (2003, background): “…Numerous of the information interoperability problems that the composition of workflows involving Web services face are already well known within the distributed database systems community (Sheth and Larson 1990; Kashyap and Sheth 1996; Calvanese, Giacomo et al. 1998; Parent and Spaccapietra 1998).”
A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection (2019, methodology): “Like federated databases [153], a practical FLS has to consider the autonomy of the parties.”
Design of fusion technique-based mining engine for smart business (2015, methodology): “The techniques are applied for tracking object, event detection, context recognition, medical diagnosis and information management in military, academic, commercial, and industrial fields [14, 15].”
Quality-Driven Query Answering for Integrated Information Systems (2002, background): “To take into account these new dimensions for database systems, the architecture of such systems changed from the three level architecture for centralized dbms, also known as the ANSI/SPARC architecture [125], to a five level architecture for federated dbms [120].”
Global Viewing of Heterogeneous Data Sources (2001, methodology): “With federated architectures, an integrated data schema is defined which provides a unified representation of all the elements in the source schemas [ 41 ].”
Business-to-business interactions: issues and enabling technologies (2003, background): “Previous work dealing with interoperation in loosely coupled systems mostly focused on databases and digital libraries [80,73].”
Corpus-based schema matching (2005, background): “Semantic heterogeneity is a key problem in any data sharing system, be it a federated database [22], data integration system [25], message passing system [1, 14, 16], web service, or peer-data management system [18].”
A taxonomy of Data Grids for distributed data sharing, management, and processing (2005, methodology): “…database can be formed either by taking an existing single site database and splitting it over different sites (top-down approach) or by federating existing database management systems so that they can be accessed through a uniform interface (bottomup approach) [Sheth and Larson 1990].”
An interactive clustering-based approach to integrating source query interfaces on the deep Web (2004, background): “Schema and Interface Matching: There is a large body of works on schema matching and integration [6, 7, 9, 10, 11, 14, 16, 18, 19, 23, 29].”
A survey of approaches to automatic schema matching (2001, background): “Most work on schema match has been motivated by schema integration, a problem that has been investigated since the early 1980s: Given a set of independently developed schemas, construct a global view [BLN86, EP90, SL90 , PS98].”
SEMINT: A tool for identifying attribute correspondences in heterogeneous databases using neural networks (2000, background): “Determining such relationships can be time consuming and tedious [34].”
Triple Pattern Fragments: A low-cost knowledge graph interface for the Web (2016, methodology): “We use the term “federation” in the context of Sheth and Larson, who define a federated database system as “a collection of cooperating database systems that are autonomous and possibly heterogeneous” [12].”
Federated Learning on Non-IID Data Silos: An Experimental Study (2021, background): “Query onFederatedDatabases:Aswe focus on distributed databases due to privacy concerns, federated databases [57] also need to be revisited.”
Ontology Evolution: Not the Same as Schema Evolution (2004, unknown): “The development and maintenance of integrated databases [2] and federated database systems [27] is already much more de-centralized, but at the very least, database-schema developers usually know which databases use their schema.”
Composing Mappings Among Data Sources (2003, background): “To copy otherwise, or to republish, requires a fee and/or special permission from the Endowment. with federated databases [27], followed by data integration systems [30, 14, 18], peer data management systems [12, 10, 2, 16, 17, 25], and data exchange systems [24, 26, 8].”