Exploiting Correlation and Parallelism of Materialized-View Recommendation for Distributed Data Warehouses

Jiang, Haifeng; Gao, Dengfeng; Li, Wen-Syan

doi:10.1109/icde.2007.367873

Cited by 13 publications

(10 citation statements)

References 5 publications

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“…That is, the source updates are committed without any concern of how and when the view manager will incorporate them into the view. This causes problems which we called maintenance anomalies: View maintenance, view synchronization, and view adaptation [7]. View maintenance maintains the materialized view extent under source data updates.…”

Section: Materialized View Maintenancementioning

confidence: 99%

Optimal Materialized View Management in Distributed Environment Using Random Walk Approach

Bagale

Joshi

2016

J. Adv. Coll. Engin. Mgt.

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Materialized View selection and maintenance is a critical problem in many applications. In large databases particularly in distributed database, query response time plays an important role as timely access to information and it is the basic requirement of successful business application. The materialization of all views is not possible because of the space constraint and maintenance cost constraint. Materialized views selection is one of the crucial decisions in designing a data warehouse for optimal efficiency. Selecting a suitable set of views that minimizes the total cost associated with the materialized views is the key component in distributed database environment. Several solutions have been proposed in the literature to solve this problem. However, most studies do not encompass search time, storage constrains and maintenance cost. In this research work two algorithms are depicted; first for materialized view selection and maintenance in distributed environment where database is distributed, Second algorithm is for node selection in distributed environment.

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Section: Materialized View Maintenancementioning

confidence: 99%

Optimal Materialized View Management in Distributed Environment Using Random Walk Approach

Bagale

Joshi

2016

J. Adv. Coll. Engin. Mgt.

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“…This seems to be the first paper to present an algorithm for this type of constraint. The problem of choosing which views to materialize has also been researched in federated databases: [16] analyzes which views to materialize in front-end databases, and [13] analyzes which views to materialize in back-end databases. A self-tuning algorithm for data placement in distributed databases is proposed by [15].…”

Section: Related Workmentioning

confidence: 99%

“…However, these approaches depend on monotonic cost models, while considering updates and distributed data results in non-monotonic models [4,11]. All existing approaches to derive MVs in distributed scenarios we are aware of make prohibitive restrictions in order to obtain monotonic cost models or reduce the complexity, e.g., no update costs [4,10,12,25] or only one querying node [10,11,12,13,16].…”

Section: Introductionmentioning

confidence: 99%

Towards materialized view selection for distributed databases

Chaves¹,

Buchmann

Hueske³

et al. 2009

Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology

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Materialized views (MV) can significantly improve the query performance of relational databases. In this paper, we consider MVs to optimize complex scenarios where many heterogeneous nodes with different resource constraints (e.g., CPU, IO and network bandwidth) query and update numerous tables on different nodes. Such problems are typical for large enterprises, e.g., global retailers storing thousands of relations on hundreds of nodes at different subsidiaries.Choosing which views to materialize in a distributed, complex scenario is NP-hard. Furthermore, the solution space is huge, and the large number of input factors results in non-monotonic cost models. This prohibits the straightforward use of brute-force algorithms, greedy approaches or proposals from organic computing. For the same reason, all solutions for choosing MVs we are aware of do not consider either distributed settings or update costs.In this paper we describe an algorithmic framework which restricts the sets of considered MVs so that a genetic algorithm can be applied. In order to let the genetic algorithm converge quickly, we generate initial populations based on knowledge on database tuning, and devise a selection function which restricts the solution space by taking the similarity of MV configurations into account. We evaluate our approach both with artificial settings and a real-world RFID scenario from retail. For a small setting consisting of 24 tables distributed over 9 nodes, an exhaustive search needs 10 hours processing time. Our approach derives a comparable set of MVs within 30 seconds. Our approach scales well: Within 15 minutes it chooses a set of MVs for a realworld scenario consisting of 1,000 relations, 400 hosts, and a workload of 3,000 queries and updates.

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“…A hybrid approach between the warehouse and the federation approach is suggested in [19] as a way to obtain a more flexible and applicable architecture. The idea is to aggregate selected data from the component data warehouses as materialized views and cache them at a federation server to improve query performance; a set of materialized query tables are recommended for the benefits of load distribution and easy maintenance of aggregated data.…”

Section: Warehousing Approachesmentioning

confidence: 99%

Collaborative Business Intelligence

Rizzi

2012

Business Intelligence

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Exploiting Correlation and Parallelism of Materialized-View Recommendation for Distributed Data Warehouses

Cited by 13 publications

References 5 publications

Optimal Materialized View Management in Distributed Environment Using Random Walk Approach

Optimal Materialized View Management in Distributed Environment Using Random Walk Approach

Towards materialized view selection for distributed databases

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