Evolutionary game theory approach to materialized view selection in data warehouses

Sohrabi, Mohammad Karim; Azgomi, Hossein

doi:10.1016/j.knosys.2018.09.012

Cited by 33 publications

(27 citation statements)

References 31 publications

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“…This is evidence from the sample calculations. With ‗ ' numbers of different features considered for experimentation at different time intervals, feature selection rate using TR-GIBMRC method was found to be ‗ ', ‗ ' using Evolutionary game theorybased [1] and ‗ ' using Integrated artifacts [2] respectively. This is because of the application of Tobit Regressive Feature Selection model.…”

Section: Figure 5 Performance Results Of Feature Selection Rate Usingmentioning

confidence: 99%

“…In other words, the Tobit Regressive Feature Selection being a statistical model infers the correlation between non-negative dependent variable and an independent variable for relevant feature selection. In this way, the feature selection rate using TR-GIBMRC method is reduced by 27% when compared to [1] and 53% when compared to [2] respectively. Impact of classification accuracy Classification accuracy is one of the most important metrics for measuring the predictive analysis.…”

Section: Figure 5 Performance Results Of Feature Selection Rate Usingmentioning

confidence: 99%

“…order_id) considered for experimentation, ‗ ' numbers of features were correctly classified using TR-GIBMRC, ‗ ' numbers of features were correctly classified using Evolutionary game theory-based method [1] and ‗ ' numbers of features were correctly classified using Integrated artifacts [2]. With this, the classification accuracy using TR-GIBMRC was found to be ‗ ', classification accuracy using Evolutionary game theory-based method [1] was found to be ‗92.7%' and classification accuracy using Integrated artifacts [2] was found to be ‗ ' respectively. From this it is evident that the classification accuracy was improved using the TR-GIBMRC method.…”

Section: Figure 6 Performance Results Of Classification Accuracy Usinmentioning

confidence: 99%

“…training dataset), experiments were conducted in the range of 5000 to 50000 features. The sample calculations for feature selection rate using the proposed TR-GIBMRC and existing evolutionary game theory-based method [1] and integrated artifacts [2] are given below.…”

Section: (11)mentioning

confidence: 99%

“…For Veracity evaluation, data quality model was essential one for categorizing dirty data stored in data warehouse and describing the metrics for counting errors in every class. An evolutionary game theory-based method was introduced in [2] for materialized view selection in data warehouse with multiple view processing plan structure to find problem search space. A population of players were generated where each player were considered as solution to the problem.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Tobit Regressive Based Gaussian Independence Bayes Map Reduce Classifier on Data Warehouse for Predictive Analytics

Sivakkolundu¹,

Kavitha²

2019

IJITEE

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Data warehouse comprises of data collected from different probable heterogeneous resources at different time intervals with the objective of responding to user analytic queries. Big data is a field that helps in analysing and extracting information from large datasets. The unfolding Big Data incorporation inflicts multiple confronts, compromising the feasible business research practice. Heterogeneous resources, high dimensionality and massive volumes that confront Big Data prototype may prevent the effectual data and system integration processes. In this work, we plan to develop a Tobit Regressive based Gaussian Independence Bayes Map Reduce Classifier (TR-GIBMRC) method for categorizing the collected and stored data which helps the users in making decision with minimum time consumption. The TR-GIBMRC method consists of two processes. They are, Tobit Regressive Feature Selection and Gaussian Independence Bayes Map Reduce Classification. Tobit Regressive Feature Selection process is used to select relevant features from collected and stored data. Tobit statistical model, used to describe the relationship between non-negative dependent variable and an independent variable for selecting relevant features. Next, Gaussian Independence Bayes Map Reduce Classifier is used to classify the selected relevant features for decision making with lesser time consumption. Gaussian Independence Bayes Map Reduce Classifier, a probabilistic classifier segments the data by class by measuring the mean and variance of data in each class. The data point gets allocated to the class with minimal variance. This in turn helps to perform efficient data classification for accurate decision making. Experimental evaluation is carried out on the factors such as feature selection rate, classification accuracy, classification time and error rate with respect to number of features and number of data points.

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Section: Figure 5 Performance Results Of Feature Selection Rate Usingmentioning

confidence: 99%

Section: Figure 5 Performance Results Of Feature Selection Rate Usingmentioning

confidence: 99%

Section: Figure 6 Performance Results Of Classification Accuracy Usinmentioning

confidence: 99%

Section: (11)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Tobit Regressive Based Gaussian Independence Bayes Map Reduce Classifier on Data Warehouse for Predictive Analytics

Sivakkolundu¹,

Kavitha²

2019

IJITEE

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Multi‐objective materialized view selection using flamingo search optimization algorithm

Srinivasarao

Satish

2022

Softw Pract Exp

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A materialized view selection in data warehouse management is important to speed up query processing. The data presented in data warehouses is generally stored as a set of materialized views. The major challenge is determining which views to materialize and satisfy the response time with reduced cost functions. This paper proposed an effective multi objective cost model based flamingo search algorithm for materialized view selection in data warehouse design. The multiple view processing plan structure of the data warehouse describes the search space of problem in order to select the optimal materialized views. The proposed model evaluates a multi-objective optimization problem based on the cost functions resulting from materialization. The multiple objective functions of the proposed model are maintenance costs, current query processing costs, response cost and previous query processing costs. This model selects the top-k views for materialization by satisfying the mentioned multi-objective functions. The experimental results are simulated using the TPC-H dataset. The efficacy of proposed model is measured by comparing the obtained results of the proposed model with various existing approaches.

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View Selection for Graph Pattern Matching

Wang

Liu

Chen

et al. 2020

Lecture Notes in Computer Science

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Evolutionary game theory approach to materialized view selection in data warehouses

Cited by 33 publications

References 31 publications

Tobit Regressive Based Gaussian Independence Bayes Map Reduce Classifier on Data Warehouse for Predictive Analytics

Tobit Regressive Based Gaussian Independence Bayes Map Reduce Classifier on Data Warehouse for Predictive Analytics

Multi‐objective materialized view selection using flamingo search optimization algorithm

View Selection for Graph Pattern Matching

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