Materialized Sample Views for Database Approximation

Joshi, Shantanu; Jermaine, Christopher

doi:10.1109/icde.2006.90

Cited by 12 publications

(15 citation statements)

References 15 publications

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“…Sampling has also been studied from the perspective of maintaining samples [42]. In [26], Joshi and Jermaine studied indexed materialized views that are amenable to random sampling. While similar in spirit (queries on the view are approximate), the goal of this work was to optimize query processing and not to address the cost of incremental maintenance.…”

Section: Mini-batch Experimentsmentioning

confidence: 99%

Stale view cleaning

et al. 2015

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Materialized views (MVs), stored pre-computed results, are widely used to facilitate fast queries on large datasets. When new records arrive at a high rate, it is infeasible to continuously update (maintain) MVs and a common solution is to defer maintenance by batching updates together. Between batches the MVs become increasingly stale with incorrect, missing, and superfluous rows leading to increasingly inaccurate query results. We propose Stale View Cleaning (SVC) which addresses this problem from a data cleaning perspective. In SVC, we efficiently clean a sample of rows from a stale MV, and use the clean sample to estimate aggregate query results. While approximate, the estimated query results reflect the most recent data. As sampling can be sensitive to long-tailed distributions, we further explore an outlier indexing technique to give increased accuracy when the data distributions are skewed. SVC complements existing deferred maintenance approaches by giving accurate and bounded query answers between maintenance. We evaluate our method on a generated dataset from the TPC-D benchmark and a real video distribution application. Experiments confirm our theoretical results: (1) cleaning an MV sample is more efficient than full view maintenance, (2) the estimated results are more accurate than using the stale MV, and (3) SVC is applicable for a wide variety of MVs.

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Section: Mini-batch Experimentsmentioning

confidence: 99%

Stale view cleaning

et al. 2015

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“…The k-ary multi-dimensional index tree (k-MDI tree) proposed in Rudra et al, (2012) extends the ACE Tree index (Joshi and Jermaine, 2008) for multiple dimensions. The height of the k-MDI tree is limited to the number of key attributes.…”

Section: Multidimensional Indexingmentioning

confidence: 99%

“…They demonstrated the effectiveness of this structure for single and two attribute database queries, but did not deal with multi-attribute aggregate queries. For extending the ACE Tree to k key attributes, Joshi and Jermaine (2008) proposed binary splitting of one attribute range after another at consecutive levels of the binary tree starting from the root; from level k+1, the process is repeated with each attribute in the same sequence as before. This process could lead to an index tree of very large height for a data warehouse even if only a relatively small number of attributes are considered.…”

Section: Introductionmentioning

confidence: 99%

“…To alleviate this problem, several recent schemes for approximate query processing have been proposed (Aouich and Lemire 2007); (Heule et al, 2013); (Jermaine 2007); (Jermaine 2003); (Jermaine et al, 2004); (Jin et al, 2006); (Joshi and Jermaine, 2008); (Li et al, 2008); (Spiegel and Polyzotis, 2009). Joshi and Jermaine (2008) introduced the ACE Tree, which is a binary tree index structure for efficiently drawing samples for processing database queries. They demonstrated the effectiveness of this structure for single and two attribute database queries, but did not deal with multi-attribute aggregate queries.…”

Section: Introductionmentioning

confidence: 99%

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Selecting Adequate Samples for Approximate Decision Support Queries

Rudra¹,

Gopalan²,

Achuthan

2013

Proceedings of the 15th International Conference on Enterprise Information Systems

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Abstract:For highly selective queries, a simple random sample of records drawn from a large data warehouse may not contain sufficient number of records that satisfy the query conditions. Efficient sampling schemes for such queries require innovative techniques that can access records that are relevant to each specific query. In drawing the sample, it is advantageous to know what would be an adequate sample size for a given query. This paper proposes methods for picking adequate samples that ensure approximate query results with a desired level of accuracy. A special index based on a structure known as the k-MDI Tree is used to draw samples. An unbiased estimator named inverse simple random sampling without replacement is adapted to estimate adequate sample sizes for queries. The methods are evaluated experimentally on a large real life data set. The results of evaluation show that adequate sample sizes can be determined such that errors in outputs of most queries are within the acceptable limit of 5%.

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“…The primary technical contribution is given in [23] in terms of index structure called the Appendability, Combinability, and Exponentially (ACE) Tree, which can be used for efficiently implementing a materialized sample view. Such a view, stored as an ACE Tree, has the following characteristics:…”

Section: Related Workmentioning

confidence: 99%

Selection Of Materialized View Using Query Optimization In Database Management : An Efficient Methodology

Karde¹,

Thakare²

2010

IJDMS

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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. A data warehouse uses multiple materialized views to efficiently process a given set of queries. Quick response time and accuracy are important factors in the success of any database. The materialization of all views is not possible because of the space constraint and maintenance cost constraint. Selection of Materialized views is one of the most important 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 and is the key component in data warehousing. Materialized views are found to be very useful for fast query processing. This paper gives the results of proposed tree based materialized view selection algorithm for query processing. In distributed environment where database is distributed over the nodes on which queryshould get executed and also plays an important role. This paper also proposes node selection algorithm for fast materialized view selection in distributed environment. And finally it is found that the proposed methodology performs better for query processing as compared to other materialized view selection strategies.

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Materialized Sample Views for Database Approximation

Cited by 12 publications

References 15 publications

Stale view cleaning

Stale view cleaning

Selecting Adequate Samples for Approximate Decision Support Queries

Selection Of Materialized View Using Query Optimization In Database Management : An Efficient Methodology

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