Big Data Analytics in Association Rule Mining: A Systematic Literature Review

Shahin, Mahtab; Peious, Sijo Arakkal; Sharma, Rahul; Kaushik, Minakshi; Yahia, Sadok Ben; Shah, Syed Attique; Draheim, Dirk

doi:10.1145/3474944.3474951

Cited by 22 publications

(6 citation statements)

References 22 publications

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“…This can benefit patients, physicians, healthcare organizations, pharmaceutical companies, policymakers, and other stakeholders. Big Data applications can include individual and population health surveillance, predicting health issues, calculating medical complications and risks associated with a patient, analyzing suitable treatments, and evaluating the effectiveness of current treatment strategies [149]. Big Data can inform patients about their current and future health states, empowering them to make better-informed decisions.…”

Section: The Big Data Advantage In Healthcare -Use Casesmentioning

confidence: 99%

Harnessing Big Data Analytics for Healthcare: A Comprehensive Review of Frameworks, Implications, Applications, and Impacts

Ahmed,

Xi,

Hou

et al. 2023

IEEE Access

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Big Data Analytics (BDA) has garnered significant attention in both academia and industries, particularly in sectors such as healthcare, owing to the exponential growth of data and advancements in technology. The integration of data from diverse sources and the utilization of advanced analytical techniques has the potential to revolutionize healthcare by improving diagnostic accuracy, enabling personalized medicine, and enhancing patient outcomes. In this paper, we aim to provide a comprehensive literature review on the application of big data analytics in healthcare, focusing on its ecosystem, applications, and data sources. To achieve this, an extensive analysis of scientific studies published between 2013 and 2023 was conducted and overall 180 scientific studies were thoroughly evaluated, establishing a strong foundation for future research and identifying collaboration opportunities in the healthcare domain. The study delves into various application areas of BDA in healthcare, highlights successful implementations, and explores their potential to enhance healthcare outcomes while reducing costs. Additionally, it outlines the challenges and limitations associated with BDA in healthcare, discusses modelling tools and techniques, showcases deployed solutions, and presents the advantages of BDA through various real-world use cases. Furthermore, this study identifies and discusses key open research challenges in the field of big data analytics in healthcare, aiming to push the boundaries and contribute to enhanced healthcare outcomes and decisionmaking processes.

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Section: The Big Data Advantage In Healthcare -Use Casesmentioning

confidence: 99%

Harnessing Big Data Analytics for Healthcare: A Comprehensive Review of Frameworks, Implications, Applications, and Impacts

Ahmed,

Xi,

Hou

et al. 2023

IEEE Access

Self Cite

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“…Anomaly detection has been the subject of extensive research in recent years, with a wide range of approaches proposed to address this challenging problem. Numerous studies and articles have been published on the subject, especially for high-dimensional datasets, indicating the importance and significance of Big Data in various domains [13]. Moreover, this topic continues to gain more interest as organizations recognize the value of identifying unusual patterns or events in their data, which could significantly impact their business operations [14].…”

Section: Related Workmentioning

confidence: 99%

Quality Anomaly Detection Using Predictive Techniques: An Extensive Big Data Quality Framework for Reliable Data Analysis

Widad,

Saida,

Gahi

2023

IEEE Access

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The increasing reliance on Big Data analytics has highlighted the critical role of data quality in ensuring accurate and reliable results. Consequently, organizations aiming to leverage the power of Big Data recognize the crucial role of data quality as an integral component. One notable type of data quality anomaly observed in big datasets is the presence of outlier values. Detecting and addressing these outliers have become a subject of interest across diverse domains, leading to the development of numerous anomaly detection approaches. Although anomaly detection has witnessed a proliferation of practices in recent years, a significant gap remains in addressing anomalies related to the other aspects of data quality. Indeed, while most approaches focus on identifying anomalies that deviate from the expected patterns, they do not consider irregularities in data quality, such as missing, incorrect, or inconsistent data. Moreover, most of approaches are domain-correlated and lack the capability to detect anomalies in a generic manner. Thus, we aim through this paper to address this gap in the field and provide a holistic and effective solution for Big Data quality anomaly detection. To achieve this, we suggest a novel approach that allows a comprehensive detection of Big Data quality anomalies related to six quality dimensions: Accuracy, Consistency, Completeness, Conformity, Uniqueness, and Readability. Moreover, the framework allows for sophisticated detection of generic data quality anomalies through the implementation of an intelligent anomaly detection model without any correlation to a specific field. Furthermore, we introduce and measure a new metric called "Quality Anomaly Score," which refers to the degree of anomalousness of the quality anomalies of each quality dimension and the entire dataset. Through the implementation and evaluation of our framework, the suggested framework has achieved an accuracy score of up to 99.91% and an F-score of 98.07%.

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“…After patterns are found, further association rules were discovered and applied by using the Apriori algorithm and finally some useful results were generated. Association rule mining is one of the techniques in Data Mining methods that explores the relationships between different attributes of data [18]. Apriori is a basic algorithm that finds frequent item-sets based on candidate generation.…”

Section: Data Cleaning and Pre-processingmentioning

confidence: 99%

Discovering associated factors behind road accidents using association rule mining: A case study from Gujarat, Pakistan

Tariq¹,

Mehmood²,

Mahfooz³

2022

World J. Adv. Res. Rev.

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Traffic and road safety is an international issue that is among the major concerns of the respective governing bodies around the world. Regulations and restrictions surrounding this issue can be legislated and applied only after identifying and understanding the numerous factors and conditions that increase the likelihood of traffic accidents. In this research work, we study and analyze the road accidents data and figure out the major factors that contribute to these incidents. The data was collected from Gujrat rescue office and outstanding results are achieved by applying association rules mining using Apriori algorithm. Our research and analysis found that some factors are greater contributors than others and need careful enforced legislation. Other researchers have also used the Apriori algorithm to analyze traffic accidents data from their regions and like our experience this algorithm has produced accurate and effective results. We also try to provide additional insights through visualization that may serve as guidelines to regulate better traffic control systems

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Big Data Analytics in Association Rule Mining: A Systematic Literature Review

Cited by 22 publications

References 22 publications

Harnessing Big Data Analytics for Healthcare: A Comprehensive Review of Frameworks, Implications, Applications, and Impacts

Harnessing Big Data Analytics for Healthcare: A Comprehensive Review of Frameworks, Implications, Applications, and Impacts

Quality Anomaly Detection Using Predictive Techniques: An Extensive Big Data Quality Framework for Reliable Data Analysis

Discovering associated factors behind road accidents using association rule mining: A case study from Gujarat, Pakistan

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