Literature Review of Attribute Level and Structure Level Data Linkage Techniques

Gollapalli, Mohammed

doi:10.5121/ijdkp.2015.5501

Cited by 12 publications

(5 citation statements)

References 71 publications

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“…R-squared measurement was previously mentioned in section 4.3. 8 and Table 9 we can conclude that the two proposed models outperformed the benchmark study as it gave 0.74 for Linear Regression which shows a significant increase in accuracy compared to 0.55 in the benchmark study and 0.82 for Random Forest regression which shows a reasonable increase in accuracy compared to 0.80 in benchmark study in terms of R-squared metric [38][39][40].…”

Section: Experimental Comparisonmentioning

confidence: 78%

Intelligent Modelling Techniques for Predicting Used Cars Prices in Saudi Arabia

Gollapalli¹,

Alqahtani²,

Alhamed³

et al. 2023

MMEP

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The production of cars has been decreasing in most countries since the COVID-19 pandemic from 2020 to 2021. Due to this, the used car market has grown to be a booming industry by itself. Recent advances in online portals and platforms have made it possible to get more information about the factors that determine used car values. Hence, car price prediction has become a high-interest field of research. This paper aims to investigate the power of machine learning to build a model that will be able to predict the approximate price of a used car by utilizing the "Saudi Arabia Used Cars" Dataset which is collected from the Syarah platform and available on the Kaggle platform. The model assists both customer and seller to estimate the approximate price of a used car in the market. Three different Machine learning techniques were utilized which are Linear Regression, Random Forest, and XGBoost which score an MSE of 0.15, 0.10, and 0.19 respectively. The Random Forest Regressor algorithm outperformed other algorithms where it achieves the best result on the three evaluated metrics RMSE, MSE, and R-squared.

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Section: Experimental Comparisonmentioning

confidence: 78%

Intelligent Modelling Techniques for Predicting Used Cars Prices in Saudi Arabia

Gollapalli¹,

Alqahtani²,

Alhamed³

et al. 2023

MMEP

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“…A comparative evaluation of many contemporary work scheduling algorithms was offered by Anushree and Xavier [3]. The comparative study was proposed based on performance indicators, algorithm benefits, and algorithm drawbacks [30,31]. As a result of this research, they discovered that no single strategy can attain all of the essential parameters.…”

Section: Task Scheduling Evaluating Algorithmsmentioning

confidence: 99%

Modeling Algorithms for Task Scheduling in Cloud Computing Using CloudSim

Gollapalli¹,

Al-Amoudi²,

Aldossary³

et al. 2022

MMEP

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As the number of cloud users are spontaneously growing globally, there is an urgent need to constantly provide quality services to consumers. Consequently, task scheduling plays an essential role in improving the performance of the cloud computing environment. Most of the published research in this field share common goals, which can be summarized in maximizing resource utilization, reducing cost, and increasing performance. This research provides the foundation knowledge on the latest works done to enhance and optimize the existing task scheduling algorithm in cloud computing by considering various parameters. Furthermore, in this study, we have applied comparative study to analyze the performance of three task scheduling algorithms namely Max-Min, First Come First Serve (FCFS), and Round Robin (RR) in cloud computing environments based on the performance metric of the Virtual Machines (VM) resources' cost, average time and makespan to find the best performing algorithm in the cloud environment. The experimental evaluations were conducted using CloudSim simulation tool. The results show that Max-Min achieved better performance based on makespan and average waiting time than other algorithms in Space and Time-shared policies.

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“…Some of the most noteworthy works are developed by the type of attribute matching, also known as field matching, and structure level matching, used when the records need to be matched to more than one record. Attributes like linguistic similarity, rule expression, ranking, string distance, term frequency, range pattern, numeric distance, weight pattern, blocking, fuzzy matrix and path sequence are the most frequently used study approaches for record linkage by field matching (20)(21)(22).…”

Section: Previous Workmentioning

confidence: 99%

“…Different data linkage approaches have been developed by researchers ( 5 , 8 , 18 – 20 ) including but not limited to SQL Matching Strategies, exact matching strategies, and approximate matching strategies ( 21 ).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Structure level matching implements different techniques for group comparison like the iterative pattern, tree pattern, sequence pattern, neighborhood pattern, relational hierarchy, clustering, graphical statistics, taxonomy, and data constraints ( 23 – 25 ). These techniques require many combinations of attributes to perform a data matching, that can require a powerful record linkage tool and can be time-consuming ( 21 , 23 ).…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Record Linkage of Crashes with Injuries and Medical Cost in Puerto Rico

Santiago

Jordán

Valdés

2020

Transportation Research Record: Journal of the Transportation R

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Cost considerations are critical in the analysis and prevention of traffic crashes. Integration of cost data into crash datasets facilitates the crash-cost analyses with all their related attributes. It is, however, a challenging task because of the lack of availability of unique identifiers across the databases and because of privacy and confidentiality regulations. This study performed a record linkage comparison between the deterministic and probabilistic approaches using attributes matching techniques with numerical distance and weight patterns under the Fellegi–Sunter approach. As a result, the deterministic algorithm developed using the exact match of the 14-digit police accident record number had an overall matching performance of 52.38% of real matched records, while the probabilistic algorithm had an overall matching performance of 70.41% with a quality measurement of the sensitivity of 99.99%. The deterministic approach was thus outperformed by the probabilistic approach by approximately 20% of records matched. The probabilistic matching with numerical variables seems to be a good matching strategy supported by quality variables. On record matching, a multivariable regression model was developed to model medical costs and identify factors that increase the costs of treating injured claimants in Puerto Rico.

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Literature Review of Attribute Level and Structure Level Data Linkage Techniques

Abstract: ABSTRACT

Cited by 12 publications

References 71 publications

Intelligent Modelling Techniques for Predicting Used Cars Prices in Saudi Arabia

Intelligent Modelling Techniques for Predicting Used Cars Prices in Saudi Arabia

Modeling Algorithms for Task Scheduling in Cloud Computing Using CloudSim

Record Linkage of Crashes with Injuries and Medical Cost in Puerto Rico

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