Query Processing in R*

Lohman, Guy M.; Mohan, C.; Haas, Laura M.; Daniels, Dean; Lindsay, Bruce G.; Selinger, Patricia G.; Wilms, Paul F.

doi:10.1007/978-3-642-82375-6_2

Cited by 102 publications

(32 citation statements)

References 21 publications

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“…[13] provides a thorough survey on this area. The optimizers in Distributed INGRES [9] and System R* [14] consider both CPU and I/O cost as well as the communication cost of processing a whole dataset. In these systems the I/O cost are so high that they cannot be omitted.…”

Section: Related Workmentioning

confidence: 99%

PMJoin: Optimizing Distributed Multi-way Stream Joins by Stream Partitioning

Zhou

Yan

et al. 2006

Lecture Notes in Computer Science

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Abstract. In emerging data stream applications, data sources are typically distributed. Evaluating multi-join queries over streams from different sources may incur large communication cost. As queries run continuously, the precious bandwidths would be aggressively consumed without careful optimization of operator ordering and placement. In this paper, we focus on the optimization of continuous multi-join queries over distributed streams. We observe that by partitioning streams into substreams we can significantly reduce the communication cost and hence propose a novel partitionbased join scheme -PMJoin. A few partitioning techniques are studied. To generate the query plan for each substream, a heuristic algorithm is proposed based on a rate-based model. Results from an extensive experimental study show that our techniques can sufficiently reduce the communication cost.

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Section: Related Workmentioning

confidence: 99%

PMJoin: Optimizing Distributed Multi-way Stream Joins by Stream Partitioning

Zhou

Yan

et al. 2006

Lecture Notes in Computer Science

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“…The System R* project [14] is one of the first distributed database prototypes. In System R*, both ship-in and ship-out strategies are examined.…”

Section: Related Workmentioning

confidence: 99%

“…This type of implementation leads to considerably different results where the ship-out method always outperforms ship-in. Disk-based semi-join algorithms are described in [1,2,5,14]. A sort-merge join, bloom filter semi-join, and sort-based semi-join are evaluated in [15] for a distributed database environment.…”

Section: Related Workmentioning

confidence: 99%

“…One of the major reason for this is the different cost model in this environment. The I/O and CPU costs used in the traditional query optimization [14] are largely insignificant here compared to the cost of accessing data in external sources. While new cost models have been developed for use in mediator frameworks with centralized architecture [18], no experimental results are reported using a distributed mediator framework.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Join Strategies for Distributed Mediation

Josifovski

Katchaounov

Risch

2001

Advances in Databases and Information Systems

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Abstract. Three join algorithms are evaluated in an environment with distributed main-memory based mediators and data sources. A streamed ship-out join ships bulks of tuples to a mediator near a data source, followed by post-processing in the client. An extended streamed semi-join in addition builds a main-memory hash index in the client mediator. A ship-in algorithm materializes and joins the data in the client mediator. The first two algorithms are suitable for sources that require parameters to execute a query, as web search engines and computational software, and the last is suitable otherwise. We compare the execution times for obtaining all and the first N tuples, and analyze the percentage time spent in subsystems, varying the network communication speed, bulk size, and data duplicates. The join algorithm leads to orders of magnitude performance difference in different mediation environments.

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“…[371, [38]). Since Garlic queries are formulated in an object-extended SQL dialect, we will also draw upon early results in the area of object query processing (e.g., 1401, [41], [42]); this is still an active research area, so we also expect that the Garlic project will contribute to this area over time.…”

Section: Garlic Query Processingmentioning

confidence: 99%

Towards heterogeneous multimedia information systems: the Garlic approach

Carey

Haas

Schwarz

et al.

Proceedings RIDE-DOM'95. Fifth International Workshop on Research Issues in Data Engineering-Distributed Object Management

201

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We provide an overview of the Garlic project, a new project at the IBM Almaden Research Center. The goal of this project is to develop a system and associated tools for the management of large quantities of heterogeneous multimedia information. Garlic permits traditional and multimedia data to be stored in a variety of existing data repositories, including databases, files, text managers, image managers, video servers and so on; the data is seen through a unified schema expressed in an object-oriented data model and can be queried and manipulated using an object-oriented dialect of SQL, perhaps through an advanced querybrowser tool that we are also developing. The Garlic architecture is designed to be extensible to new kinds of data repositories, and access efficiency is addressed via a "middleware" query processor that uses database query optimization techniques to exploit the native associative search capabilities of the underlying data repositories.

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Query Processing in R*

Cited by 102 publications

References 21 publications

PMJoin: Optimizing Distributed Multi-way Stream Joins by Stream Partitioning

PMJoin: Optimizing Distributed Multi-way Stream Joins by Stream Partitioning

Evaluation of Join Strategies for Distributed Mediation

Towards heterogeneous multimedia information systems: the Garlic approach

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