While big data benefits are numerous, the use of big data requires, however, addressing new challenges related to data processing, data security, and especially degradation of data quality. Despite the increased importance of data quality for big data, data quality measurement is actually limited to few metrics. Indeed, while more than 50 data quality dimensions have been defined in the literature, the number of measured dimensions is limited to 11 dimensions. Therefore, this paper aims to extend the measured dimensions by defining four new data quality metrics: Integrity, Accessibility, Ease of manipulation, and Security. Thus, we propose a comprehensive Big Data Quality Assessment Framework based on 12 metrics: Completeness, Timeliness, Volatility, Uniqueness, Conformity, Consistency, Ease of manipulation, Relevancy, Readability, Security, Accessibility, and Integrity. In addition, to ensure accurate data quality assessment, we apply data weights at three data unit levels: data fields, quality metrics, and quality aspects. Furthermore, we define and measure five quality aspects to provide a macro-view of data quality. Finally, an experiment is performed to implement the defined measures. The results show that the suggested methodology allows a more exhaustive and accurate big data quality assessment, with a more extensive methodology defining a weighted quality score based on 12 metrics and achieving a best quality model score of 9/10.

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%.

Big data has emerged as a fundamental component in various domains, enabling organizations to extract valuable insights and make informed decisions. However, ensuring data quality is crucial for effectively using big data. Thus, big data quality has been gaining more attention in recent years by researchers and practitioners due to its significant impact on decision-making processes. However, existing studies addressing data quality anomalies often have a limited scope, concentrating on specific aspects such as outliers or inconsistencies. Moreover, many approaches are context-specific, lacking a generic solution applicable across different domains. To the best of our knowledge, no existing framework currently automatically addresses quality anomalies comprehensively and generically, considering all aspects of data quality. To fill the gaps in the field, we propose a sophisticated framework that automatically corrects big data quality anomalies using an intelligent predictive model. The proposed framework comprehensively addresses the main aspects of data quality by considering six key quality dimensions: Accuracy, Completeness, Conformity, Uniqueness, Consistency, and Readability. Moreover, the framework is not correlated to a specific field and is designed to be applicable across various areas, offering a generic approach to address data quality anomalies. The proposed framework was implemented on two datasets and has achieved an accuracy of 98.22%. Moreover, the results have shown that the framework has allowed the data quality to be boosted to a great score, reaching 99%, with an improvement rate of up to 14.76% of the quality score.

The effective management of electronic health records (EHRs) is vital in healthcare. However, traditional systems often need help handling data inconsistently, providing limited access, and coordinating poorly across facilities. This study aims to tackle these issues using blockchain technology to improve EHR systems' data security, privacy, and interoperability. By thoroughly analyzing blockchain's applications in healthcare, we propose an innovative solution that leverages blockchain's decentralized and immutable nature, combined with advanced encryption techniques such as the Advanced Encryption Standard and Zero Knowledge Proof Protocol, to fortify EHR systems. Our research demonstrates that blockchain can effectively overcome significant EHR challenges, including fragmented data and interoperability problems, by facilitating secure and transparent data exchange, leading to enhanced coordination, care quality, and cost-efficiency across healthcare facilities. This study offers practical guidelines for implementing blockchain technology in healthcare, emphasizing a balanced approach to interoperability, privacy, and security. It represents a significant advancement over traditional EHR systems, boosting security and affording patients greater control over their health records. Doi: 10.28991/HIJ-2023-04-03-012 Full Text: PDF

Twitter is a microblogging service where users can send and read short messages of 140 characters called “tweets”. Many healthcare-related unstructured and free-text tweets are shared on Twitter, which is becoming a popular domain for medical research. Sentiment analysis is one of the data mining types that provides an estimate of the direction of personality sentiment analysis in natural language processing. By analyzing text, computational linguistics is used to infer and analyze mental knowledge of the web, social media, and related references. The data reviewed actually quantifies the attitudes or feelings of the global society towards specific goods, people, or thoughts and exposes the contextual duality of the knowledge. Sentiment analysis is used in various sectors such as health care. There is an incredible amount of healthcare information available online, such as social media, and websites focused on rating medical problems, that is not accessed in a methodical way. Sentiment analysis has many benefits, such as using medical information to achieve the best possible patient outcome and improve the quality of health care. This review paper focuses on the presented sentiment analysis methods that are used in the medical field.

Increased digital banking operations have brought about a surge in suspicious activities, necessitating heightened real-time fraud detection systems. Conversely, traditional static approaches encounter challenges in maintaining privacy while adapting to new fraudulent trends. In this paper, we provide a unique approach to tackling those challenges by integrating VAE-QLSTM with Federated Learning (FL) in a semi-decentralized architecture, maintaining privacy alongside adapting to emerging malicious behaviors. The suggested architecture builds on the adeptness of VAE-QLSTM to capture meaningful representations of transactions, serving in abnormality detection. On the other hand, QLSTM combines quantum computational capability with temporal sequence modeling, seeking to give a rapid and scalable method for real-time malignancy detection. The designed approach was set up through TensorFlow Federated on two real-world datasets—notably IEEE-CIS and European cardholders—outperforming current strategies in terms of accuracy and sensitivity, achieving 94.5% and 91.3%, respectively. This proves the potential of merging VAE-QLSTM with FL to address fraud detection difficulties, ensuring privacy and scalability in advanced banking networks.