Meanwhile, virtual data integration has become an alternative topic that is increasingly attracting the attention of researchers in the current era of big data. Various data integration methodologies have been developed and used in various domains, processing heterogeneous data using various approaches. ...
Gathering information from multiple internal and external sources, converting it into a common format, and then putting it all together in one place—like a data warehouse—is known as data integration. Because of this centralization, teams from many departments can access, examine, and use the d...
25. What are some of the data validation methodologies used in data analysis? Many types of data validation techniques are used today. Some of them are as follows: Field-level validation: Validation is done across each of the fields to ensure that there are no errors in the data entered by...
However, the deployment of AI and Large Language Models in government sectors brings with it the imperative of adhering to stringent standards of data privacy, ethics, and quality. In the context of government operations, where data sensitivity and accuracy are paramount, it’s essential to ensure...
These new challenges are focused mainly on problems such as data processing, data storage, data representation, and how data can be used for pattern mining, analysing user behaviours, and visualizing and tracking data, among others. In this paper, we present a revision of the new methodologies ...
Artificial intelligence is based on the analysis of large datasets and requires a continuous supply of high-quality data. However, using data for AI is not without challenges. This paper comprehensively reviews and critically examines the challenges of using data for AI, including data quality, ...
Data integration The most widely used data integration technique is extract, transform and load. ETL pulls data from source systems, converts it into a consistent format and then loads the integrated data into a data warehouse or other target system. However, data integration platforms now also ...
here a framework is used which has been traditionally applied for enterprise and business model quality, with the data quality characteristics structured relative to semiotic levels, which makes it easier to compare aspects in order to find opportunities and challenges for data integration. A case stu...
Each of these challenges can affect the performance of the model and lead to inaccurate or unreliable results. This study aims to conduct a comprehensive review of the literature on online learning methodologies and the associated challenges, especially for online learning, with full feedback. In ...
analysis: Cluster impurity was evaluated as the fraction of objects that were inconsistent with the label of the cluster. It was calculated using each data type alone and by integrating them. Errors decreased with the integration approach in particular when the semi-supervised methodologies were used...