Marko Smiljanić, Henk Blanken, Maurice Van Keulen et al. · 2002
Invention of XML as a universal standard for data representation triggered enormously wide efforts for its adaptation into almost every IT activity. Databases took one of the focusing places of the XML research. This paper investigates the development path of XML from its origins to its current place in distributed database systems. Through enumeration of features and analysis of the problems related to XML and distributed database systems it forms a platform for
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Johann Rost, Robert L. Glass, Norman F. Schneidewind et al. · 2011
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Sevinç, Ender · OpenMETU (Middle East Technical University) · 2019
The increasing performance of computers, reduced prices and ability to connect systems with low cost gigabit ethernet LAN and ATM WAN networks make distributed database systems an attractive research area. However, the complexity of distributed database query optimization is still a limiting factor. Optimal techniques, such as dynamic programming, used in centralized database query optimization are not feasible because of the increased problem size. The recently developed genetic algorithm (GA) based optimization techniques presents a promising alternative. We compared the best known GA with a random algorithm and showed that it achieves almost no improvement over the random search algorithm generating an equal number of random solutions. Then, we analyzed a set of possible GA parameters and determined that two-point truncate technique using GA gives the best results. New mutation and crossover operators defined in our GA are experimentally analyzed within a synthetic distributed database having increasing the numbers of relations and nodes. The designed synthetic database replicated relations, but there was no horizontal/vertical fragmentation. We can translate a select-project-join query including a fragmented relation with N fragments into a corresponding query with N relations. Comparisons with optimal results found by exhaustive search are only 20% off the results produced by our new GA formulation showing a 50% improvement over the previously known GA based algorithm.
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M. Tamer Özsu · 1991
A distributed database (DDB) is a collection of multiple, logically interrelated databases distributed over a computer network. A distributed database management system (distributed DBMS) is the software system that permits the management of the distributed database and makes the distribution transparent to the users [1]. The term distributed database system (DDBS) is typically used to refer to the combination of DDB and the distributed DBMS. Distributed DBMSs are similar to distributed file systems (see Distributed File Systems) in that both facilitate access to distributed data. However, there are important differences in structure and functionality, and these characterize a distributed database system: 1. Distributed file systems simply allow users to access files that are located on machines other than their own. These files have no explicit structure (i.e., they are flat) and the relationships among data in different files (if there are any) are not managed by the system and are the users responsibility. A DDB, on the other hand, is organized according to a schema that defines both the structure of the distributed data, and the relationships among the data. The schema is defined according to some data model, which is usually relational or object-oriented (s e e Distributed Database Schemas). 2. A distributed file system provides a simple interface to users which allows them to open, read/write (records or bytes), and close files. A distributed DBMS system has the full functionality of a DBMS. It provides high-level,
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2003
In this paper, the typical problems in distributed multimedia database systems, such as real time requirements, synchronization, QoS specifications, are analyzed. Policies and methods to solve these problems are provided. And various implementation paradigms for distributed systems, such as RPC (remote process call), middleware, Agent, are investigated.
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Stefano Ceri, Barbara Pernici, Gio Wiederhold · Proceedings of the IEEE · 1987
This paper surveys methodological approaches for distributed database design. The design of distribution can be performed top-down or bottom-up; the first approach is typical of a distributed database developed from scratch, while the second approach is typical of the development of a multi-database as the aggregation of existing databases. We review the design problems and methodologies along both directions, and we describe DATAID-D, a top-down methodology for distribution design. We indicate how the methodology is part of a global approach to database design; how to collect the requirements about the distribution of data and applications; and how to progressively build the distribution of a schema. Our approach is exemplified through one case study.
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Julie M. Hurd · Information Processing & Management · 1983
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Bentson,T · Defense Technical Information Center (DTIC) · 1985
This document discusses a simulation design tool for distributed database management systems. The ability to model database transaction processing has been added to an existing communications software testbed. The information architecture used is an extension of the ANSI/SPARC standard three-schema DBMS architecture. This extended architecture was developed by the Honeywell Computer Science Center. This document discusses the standard DBMS architecture, the extended distributed DBMS architecture, the logical transaction model of database operations, and the method of simulating this logical model. Measures of data management effectiveness are also discussed. These algorithms, called 'global schemas' and 'local schemas', are unique to distributed DBMSs. These govern the location, correlation, storage, retrieval, and deletion of multiple data copies. A distributed DBMS relies on the service of a communications system to transmit both control information (e.g., 'read' or 'write') and data. The test and evaluation methodology allows the distributed DBMS to be modeled and tested together with the underlying communications system in order to allow these systems to be tuned to each other.
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Raju Kocharekar · NCSU Libraries Repository (North Carolina State University Libraries) · 1986
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SCHREIBER, FABIO ALBERTO · Virtual Community of Pathological Anatomy (University of Castilla La Mancha) · 1987
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SpringerReference · 2011
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John G. Boeckman, John G. Boeckman · 1984
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Alice Orrell, Kamila Kazimierczuk, Lindsay Sheridan et al. · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 2022
The PNNL research team continually collects cost, incentive, generation, and customer data from turbine manufacturers, operations and maintenance providers, state and federal agencies, and other stakeholders for distributed wind projects installed in the United States. These data are critical for identifying trends, opportunities for growth, and prioritizing investments for both WETO and industry stakeholders. Making this information available allows interested parties to better understand distributed wind market trends and characteristics.
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2001
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M. Tamer Özsu · 2009
In this article, we discuss the fundamentals of distributed DBMS technology. We address the data distribution and architectural design issues as well as the algorithms that need to be implemented to provide the basic DBMS functions such as query processing, concurrency control, reliability, and replication control. Glossary Atomicity: The property of transaction processing whereby either all the operations of a transaction are executed or none of them are (all-or-nothing). Client/server architecture: A distributed/parallel DBMS architecture where a set of cli-ent machines with limited functionality access a set of servers which manage data. Concurrency control algorithm: Algorithms that synchronize the operations of concur-rent transactions that execute on a shared database. Distributed database management system: A database management system that man-ages a database that is distributed across the nodes of a computer network and makes this distribution transparent to the users. 1
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Saeed K. Rahimi, Frank S. Haug · 2010
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SpringerReference · 2011
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Mounir Bechchi, Guillaume Raschia, Noureddine Mouaddib · 2007
The database summarization system coined S<scp>aint</scp>E<scp>ti</scp>Q provides multi-resolution summaries of structured data stored into acentralized database. Summaries are computed online with a conceptual hierarchical clustering algorithm. However, most companies work in distributed legacy environments and consequently the current centralized version of S<scp>aint</scp>E<scp>ti</scp>Q is either not feasible (privacy preserving) or not desirable (resource limitations).
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Microelectronics Reliability · 1988
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Greef, Greet De, Louvain, Katholieke Univ., Heverlee (Belgium). Departement Computerwetenschappen · OpenGrey (Institut de l'Information Scientifique et Technique) · 1988
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