scholarly journals Securing Distributed Queries via Synchronized MAC-Based Authentication Algorithm

2018 ◽  
Vol 1 (2) ◽  
Keyword(s):  
Distributed Queries

Optimization of Distributed Queries

2011 ◽  
pp. 245-295 ◽  
Author(s):  
M. Tamer Özsu ◽  
Patrick Valduriez
Keyword(s):  
Distributed Queries

scholarly journals Quantum Associative Memory with Improved Distributed Queries

2012 ◽  
Vol 52 (6) ◽  
pp. 1787-1801 ◽  
Author(s):  
J.-P. Tchapet Njafa ◽  
S. G. Nana Engo ◽  
Paul Woafo
Keyword(s):  
Associative Memory ◽  
Distributed Queries

liquid: Context-Aware Distributed Queries

2003 ◽  
pp. 140-148 ◽  
Author(s):  
Jeffrey Heer ◽  
Alan Newberger ◽  
Chris Beckmann ◽  
Jason I. Hong
Keyword(s):  
Context Aware ◽  
Distributed Queries

Optimization of Distributed Queries in Grid Via Caching

2005 ◽  
pp. 387-396 ◽  
Author(s):  
Piotr Cybula ◽  
Hanna Kozankiewicz ◽  
Krzysztof Stencel ◽  
Kazimierz Subieta
Keyword(s):  
Distributed Queries

scholarly journals DISTRIBUTED QUERY SCHEDULING SERVICE: AN ARCHITECTURE AND ITS IMPLEMENTATION

1998 ◽  
Vol 07 (02n03) ◽  
pp. 123-166 ◽  
Author(s):  
LING LIU ◽  
CALTON PU ◽  
KIRILL RICHINE
Keyword(s):  
Scheduling Algorithm ◽  
Unified Framework ◽  
Distributed Query Processing ◽  
Distributed Query ◽  
Three Phase ◽  
Distributed Queries ◽  
Mediation System ◽  
Conventional Optimization ◽  
Execution Models ◽  
Query Scheduling

We present the systematic design and development of a distributed query scheduling service (DQS) in the context of DIOM, a distributed and interoperable query mediation system.26 DQS consists of an extensible architecture for distributed query processing, a three-phase optimization algorithm for generating efficient query execution schedules, and a prototype implementation. Functionally, two important execution models of distributed queries, namely moving query to data or moving data to query, are supported and combined into a unified framework, allowing the data sources with limited search and filtering capabilities to be incorporated through wrappers into the distributed query scheduling process. Algorithmically, conventional optimization factors (such as join order) are considered separately from and refined by distributed system factors (such as data distribution, execution location, heterogeneous host capabilities), allowing for stepwise refinement through three optimization phases: Compilation, parallelization, site selection and execution. A subset of DQS algorithms has been implemented in Java to demonstrate the practicality of the architecture and the usefulness of the distributed query scheduling algorithm in optimizing execution schedules for inter-site queries.

Integrity for distributed queries

2014 ◽  
Author(s):  
Sabrina De Capitani di Vimercati ◽  
Sara Foresti ◽  
Sushil Jajodia ◽  
Giovanni Livraga ◽  
Stefano Paraboschi ◽  
...  
Keyword(s):  
Distributed Queries
Sign in / Sign up

Export Citation Format

Share Document