A parallel query processing system based on graph-based database partitioning

Nam, Yoon-Min; Han, Donghyoung; Kim, Min-Soo

doi:10.1016/j.ins.2018.12.031

Cited by 8 publications

(4 citation statements)

References 31 publications

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“…In this section, the experimental settings of the proposed Random Forest Bagging X-means SQL Query Clustering (RFBXSQLQC) technique are performed using Java language. To conduct a fair comparison, three existing methods, namely, cluster-based decision tree technique [1], pattern detection method [2], and graph-based database partitioning method called GPT [3], are taken into account along with the proposed RFBXSQLQC technique since similar numbers of queries are utilized with a simulation of 10 runs. The performance of the proposed RFBXSQLQC technique is analyzed with the aid of the IIT Bombay dataset.…”

Section: Experimental Settingsmentioning

confidence: 99%

“…"N AD " to the sample input queries "N." It is measured in terms of percentage (%). Table 1 given below shows the antipattern detection accuracy using RFBXSQLQC technique, cluster-based decision tree technique [1], pattern detection method [2], and graph-based database partitioning method called GPT [3].…”

Section: Performance Analysis Of Antipattern Detection Accuracymentioning

confidence: 99%

“…Here, new knowledge standards were discovered based on computational intelligence techniques and source code analysis. Also, knowledge discovery in databases (KDD) and certain principles of lexical, syntactic, and semantic analysis were utilized to result in a minimum error [3].…”

Section: Introductionmentioning

confidence: 99%

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Random Forest Bagging and X‐Means Clustered Antipattern Detection from SQL Query Log for Accessing Secure Mobile Data

Dhanaraj

Ramakrishnan

Poongodi

et al. 2021

Wireless Communications and Mobile Computing

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In the current ongoing crisis, people mostly rely on mobile phones for all the activities, but query analysis and mobile data security are major issues. Several research works have been made on efficient detection of antipatterns for minimizing the complexity of query analysis. However, more focus needs to be given to the accuracy aspect. In addition, for grouping similar antipatterns, a clustering process was performed to eradicate the design errors. To address the above-said issues and further enhance the antipattern detection accuracy with minimum time and false positive rate, in this work, Random Forest Bagging X-means SQL Query Clustering (RFBXSQLQC) technique is proposed. Different patterns or queries are initially gathered from the input SQL query log, and bootstrap samples are created. Then, for each pattern, various weak clusters are constructed via X-means clustering and are utilized as the weak learner (clusters). During this process, the input patterns are categorized into different clusters. Using the Bayesian information criterion, the similarity measure is employed to evaluate the similarity between the patterns and cluster weight. Based on the similarity value, patterns are assigned to either relevant or irrelevant groups. The weak learner results are aggregated to form strong clusters, and, with the aid of voting, a majority vote is considered for designing strong clusters with minimum time. Experiments are conducted to evaluate the performance of the RFBXSQLQC technique using the IIT Bombay dataset using the metrics like antipattern detection accuracy, time complexity, false-positive rate, and computational overhead with respect to the differing number of queries. The results revealed that the RFBXSQLQC technique outperforms the existing algorithms by 19% with pattern detection accuracy, 34% minimized time complexity, 64% false-positive rate, and 31% in terms of computational overhead.

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Section: Experimental Settingsmentioning

confidence: 99%

Section: Performance Analysis Of Antipattern Detection Accuracymentioning

confidence: 99%

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Random Forest Bagging and X‐Means Clustered Antipattern Detection from SQL Query Log for Accessing Secure Mobile Data

Dhanaraj

Ramakrishnan

Poongodi

et al. 2021

Wireless Communications and Mobile Computing

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“…ey modelled the multiquery optimization cost using a multiple-choice knapsack problem. Furthermore, a general database partitioning method, called GPT, has been proposed for complex analytical queries to improve the query's performance and reduce data redundancy [49]. e GPT method determines an undirected multigraph as a partitioning scheme by considering the trade-off between data redundancy and the number of opportunities for joins processing without shuffling.…”

Section: Join Optimization Different Mapreduce Join Strategiesmentioning

confidence: 99%

Exploiting Sharing Join Opportunities in Big Data Multiquery Optimization with Flink

et al. 2020

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Multiway join queries incur high-cost I/Os operations over large-scale data. Exploiting sharing join opportunities among multiple multiway joins could be beneficial to reduce query execution time and shuffled intermediate data. Although multiway join optimization has been carried out in MapReduce, different design principles (i.e., in-memory Big Data platforms, Flink) are not considered. To bridge the gap of not considering the optimization of Big Data platforms, an end-to-end multiway join over Flink, which is called Join-MOTH system (J-MOTH), is proposed to exploit sharing data granularity, sharing join granularity, and sharing implicit sorts within multiple join queries. For sharing data, our previous work, Multiquery Optimization using Tuple Size and Histogram (MOTH) system, has been introduced to consider the granularity of sharing data opportunities among multiple queries. For sharing sort, our previous work, Sort-Based Optimizer for Big Data Multiquery (SOOM), has been introduced to consider the implicit sorts among join queries. For sharing join, additional modules have been tailored to the J-MOTH optimizer to optimize sharing work by exploiting shared pipelined multiway join among multiple multiway join queries. The experimental evaluation has demonstrated that the J-MOTH system outperforms the naive and the state-of-the-art techniques by 44% for query execution time using TPC-H queries. Also, the proposed J-MOTH system introduces maximal intermediate data size reduction by 30% in average over Hadoop-like infrastructures.

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Comparative Analysis of Decision Tree Algorithms for Data Warehouse Fragmentation

Mazahua

Rodríguez-Mazahua

López-Chau

et al. 2021

Studies in Computational Intelligence

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A parallel query processing system based on graph-based database partitioning

Cited by 8 publications

References 31 publications

Random Forest Bagging and X‐Means Clustered Antipattern Detection from SQL Query Log for Accessing Secure Mobile Data

Random Forest Bagging and X‐Means Clustered Antipattern Detection from SQL Query Log for Accessing Secure Mobile Data

Exploiting Sharing Join Opportunities in Big Data Multiquery Optimization with Flink

Comparative Analysis of Decision Tree Algorithms for Data Warehouse Fragmentation

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