The state of the art in distributed query processing

By Donald Kossmann. CSUR, 2000

962 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/f1893b89d7dc2c67131b6619762a4a6c593be7b7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

In Signal Processing

Citations in the corpus

Low Latency Geo-distributed Data Analytics (2015, background): “1) Distributed databases: While big-data frameworks currently operate only within a single cluster, work on distributed databases has been a popular topic [22, 27]; see surveys in [40, 44].”
FedX: Optimization Techniques for Federated Query Processing on Linked Data (2011, methodology): “Figure 1 depicts our federated query processing model, which closely follows the common workflow for general distributed query processing [11].”
TinyDB: an acquisitional query processing system for sensor networks (2005, background): “See [Kossman 2000] for a survey of relevant research on distributed indexing in conventional database systems.”
Linked Data: Evolving the Web into a Global Data Space (2011, background): “The drawbacks of the federation approach have been extensively studied in the database community [72].”
Query Processing in Sensor Networks (2003, background): “There has been a lot of work on query processing in distributed database systems [38, 7, 22, 37, 18].”
Routing indices for peer-to-peer systems (2002, background): “The problem of indexing a P2P network is related to the problem of indexing a distributed database [ 13 ].”
Network-Aware Operator Placement for Stream-Processing Systems (2006, background): “This is related to multi-query optimization in traditional database systems [15].”
YARS2: A Federated Repository for Querying Graph Structured Data from the Web (2007, background): “Iterators that return sets instead of tuples to increase performance have been described in [18] as row blocking.”
Querying Distributed RDF Data Sources with SPARQL (2008, methodology): “Because the algorithm only relies on a very small amount of statistical information we expect that further improvements are possible using techniques as described in [16, 13] An important issue when dealing with data from multiple data sources are differences in the used vocabularies and the represe”
The cougar approach to in-network query processing in sensor networks (2002, methodology): “[26] D. Kossmann.”
The design of an acquisitional query processor for sensor networks (2003, background): “See [29] for a survey of relevant research on distributed indexing in conventional database systems.”
PNUTS: Yahoo!'s hosted data serving platform (2008, background): “Much of the work has focused on distributed query processing [19] and distributed transactions [16].”
Design and Implementation of a General Purpose Parallel Programming System (1996, neutral): “A cluster is typically controlled by a task scheduler, separate from Dryad, which manages a batch queue of jobs and executes a few at a time subject to cluster policy.”
Survey of research towards robust peer-to-peer networks: Search methods (2006, background): “A similar argument motivated the deployment of distributed databases—one thousand , off-the-shelf PC processors are more powerful and much less expensive than a large mainframe computer [19].”