An automatic clustering technique for query plan recommendation

Azhir, Elham; Navimipour, Nima Jafari; Hosseinzadeh, Mehdi; Sharifi, Arash; Darwesh, Aso Mohammad

doi:10.1016/j.ins.2020.09.037

Cited by 17 publications

(7 citation statements)

References 33 publications

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“…Also, the number of clusters is determined based on the proposed methods in Refs. 46,47. The process of selection of the K‐means cluster‐heads is as follows: 65%: low energy, 25%: average energy, and 10%: high energy and 25%: branch‐head, 25%: branch‐head, and 50%: branch‐head.…”

Section: The Proposed Methodsmentioning

confidence: 99%

A hybrid method for reduction of size and number of hues in the color images used in a carpet map

Cui

Shi

2021

Color Research & Application

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The carpet industry is no longer a small business done in the villages on a small scale; instead, it has carved an altogether different space, identity, and appreciation for itself in the cosmopolitan world. As computers are is becoming more and more ubiquitous, most industries, including the carpet industry, use computers for quality improvement, accuracy enhancement, speed development, and cost reduction purposes. Unlike traditional carpet maps, many modern maps include images of human faces for hand‐woven carpet tableau. These digital images comprise millions of colors and thousands of pixels, making it practically impossible to construct and weave the carpet in the same dimensions. Many weavers currently use manual and experience‐based methods for reducing the size and number of hues for making a hand‐woven carpet tableau map. Therefore, the outcomes are not the optimal results and can be improved. Also, many color reduction methods do not focus on the hand‐woven carpet tableau map. To overcome these problems and gaps, this research focuses on proposing a new automatic method for reducing the size of color images without compromising facial nuances, lessening the number of colors used while protecting the important areas of the images, and transforming those images into carpet tableau maps. The proposed approach inputs the original color image. It continuously detecting the face and specifying important areas, and finally, outputs carpet tableau map that is proportional to the given dimensions and color count. To evaluate the proposed method, MATLAB, as a powerful simulation tool, was employed. Final results are compared to the existing approaches in terms of face detection, size reduction, and color quantization. The obtained results have shown that the approach improves speed by 39% in face detection and increases the precision of size reduction and color quantization phases. The results have also confirmed that when images of human faces are reduced by a proposed method to form an appropriate image for tableau maps, they are nearly always perceived as more attractive than the reduced faces via traditional methods.

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Section: The Proposed Methodsmentioning

confidence: 99%

A hybrid method for reduction of size and number of hues in the color images used in a carpet map

Cui

Shi

2021

Color Research & Application

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“…Here we describe work related to query optimization using access plan recommendation mechanisms. Azhir et al (2021) proposed an automatic hybrid query plan recommendation method based on incremental DBSCAN and NSGA-II. Dunn and Davies-Bouldin indices were used to evaluate the goodness of clusters.…”

Section: Related Workmentioning

confidence: 99%

“…The JSQL parser (http://jsqlparser.sourceforge.net/) parses the SQL statements and provides the ability to manipulate them. First, a custom JSQL parser library and various standardization rules are employed to improve clustering quality (Azhir et al, 2021). The rewriting module parses the queries' text to eliminate string literals, constants, temporary names of tables and columns, syntactic sugar, and database namespaces (Kul et al, 2018).…”

Section: Parallel Access Plan Recommendationmentioning

confidence: 99%

A technique for parallel query optimization using MapReduce framework and a semantic-based clustering method

Azhir

Navimipour

Hosseinzadeh

et al. 2021

PeerJ Computer Science

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Query optimization is the process of identifying the best Query Execution Plan (QEP). The query optimizer produces a close to optimal QEP for the given queries based on the minimum resource usage. The problem is that for a given query, there are plenty of different equivalent execution plans, each with a corresponding execution cost. To produce an effective query plan thus requires examining a large number of alternative plans. Access plan recommendation is an alternative technique to database query optimization, which reuses the previously-generated QEPs to execute new queries. In this technique, the query optimizer uses clustering methods to identify groups of similar queries. However, clustering such large datasets is challenging for traditional clustering algorithms due to huge processing time. Numerous cloud-based platforms have been introduced that offer low-cost solutions for the processing of distributed queries such as Hadoop, Hive, Pig, etc. This paper has applied and tested a model for clustering variant sizes of large query datasets parallelly using MapReduce. The results demonstrate the effectiveness of the parallel implementation of query workloads clustering to achieve good scalability.

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“…In the presented process, the query optimizer makes use of the likeness of query statements to execute new queries. However, clustering large query sets becomes a problem for traditional clustering algorithms due to the high processing time [7,8]. Various query plan prediction techniques are introduced [5,6] to identify the similarity between the queries using traditional clustering algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…It can be observed that the presented approaches reduce the expenses related to optimizing. Azhir, et al [7] also presented a novel query plan recommendation technique depending on DBSCAN and NSGA-II algorithms for enhancing prediction accuracy. The outcomes related to the suggested method are compared to traditional K-means and DBSCAN.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Query Plan Recommendation with Apache Hadoop and Apache Spark

et al. 2022

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Access plan recommendation is a query optimization approach that executes new queries using prior created query execution plans (QEPs). The query optimizer divides the query space into clusters in the mentioned method. However, traditional clustering algorithms take a significant amount of execution time for clustering such large datasets. The MapReduce distributed computing model provides efficient solutions for storing and processing vast quantities of data. Apache Spark and Apache Hadoop frameworks are used in the present investigation to cluster different sizes of query datasets in the MapReduce-based access plan recommendation method. The performance evaluation is performed based on execution time. The results of the experiments demonstrated the effectiveness of parallel query clustering in achieving high scalability. Furthermore, Apache Spark achieved better performance than Apache Hadoop, reaching an average speedup of 2x.

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An automatic clustering technique for query plan recommendation

Cited by 17 publications

References 33 publications

A hybrid method for reduction of size and number of hues in the color images used in a carpet map

A hybrid method for reduction of size and number of hues in the color images used in a carpet map

A technique for parallel query optimization using MapReduce framework and a semantic-based clustering method

Performance Evaluation of Query Plan Recommendation with Apache Hadoop and Apache Spark

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