# Big Data Analysis and Visualization: Challenges and Solutions
## 2 Challenges and Solutions for Big Data Analysis and Visualization
### Big Data Analysis
In other words, the GNNs may be unable to explore the global graph structure effectively. To address this problem, [PERSON] et al. [4] presented a graph neural network model--in particular, _graph dilated networks with rejection mechanism_ (_GraphDRej_). The dilated graph convolution kernel captures the high-level graph structure, and the rejection mechanism addresses the over-smoothing problem. The experimental results show that GraphDRej led to a higher accuracy compared to several existing GNN-based methods. Skyline queries are popular in many big data analysis and visualization applications. The skyline query conducts computations using a domination test. It tests if a data point has a value that is better than the other values in at least one dimension, and is not worse than others in the remaining dimensions. Such a skyline query can be used in constructing efficient queries based on data from various fields. However, with an increase in the number of dimensions or the volume of data, the overall performance of naive skyline queries may degrade due to the higher comparison cost among data. Although attempts have been made to solve this problem using index structures, these index structures are heavily influenced by dimensionality and data volume. [PERSON] et al. [5] presented a _hashindex-based skyline query processing_ (_HI-Sky_) to overcome the aforementioned shortcomings. When computing the skyline, HI-Sky manages data through the hash index and improves its performance by reducing unnecessary dominance tests and eliminating unnecessary data comparisons. The theoretical and experimental results show that HI-Sky improves skyline query performance when compared with prevalent methods. It is natural for humans to use hand gestures to interact with computers. For example, people usually use gestures to express meanings and their thoughts in daily conversations, as well as for numerous applications in various fields. As technology advances, hand gesture recognition is becoming popular in human-computer interactions (HCI). [PERSON] et al. [6] presented a system--which uses an RGB-D camera (i.e., a red-green-blue color depth-sensing camera) and a three-dimensional convolution neural network (3 DCNN)--for real-time hand gesture recognition and identification. It extracts fingertip locations and recognizes gestures in real time. To demonstrate the accuracy and robustness of the system interface, experiments were conducted to evaluate hand gesture recognition across various gestures. The results show that the system led to a high accuracy in hand gesture recognition, which is promising for gesture-based interfaces to interact between humans and computers by hand in the future. In the current era of Industry 4.0 (aka Fourth Industrial Revolution), many companies are focusing on securing artificial intelligence (AI) technology by enhancing their competitiveness through machine learning (which is the core technology of AI) and allowing computers to acquire high-quality data through self-learning. To enhance their competitiveness, many companies are securing high-quality big data. As such, the volume of digital information has rapidly increased throughout the world. The presentation of the value quality index of each data attribute has become meaningful because users may be interested in evaluating data quality from their viewpoint to determine whether the data are suitable for their use. Hence, [PERSON] et al. [7] presented a study on data profiling (for both structured and unstructured data), with a focus on both the _attribute value quality index_ (_AVQI_) and the _structured data value quality index_ (_SDVQI_).