Quick analysis and visualization of high-dimensional data
When multiple devices and sensors deliver large amounts of data to be merged and analyzed, it is recommended to first select suitable data structures and a suitable file format or define a new one.
In order to find correlations in the data or to recognize patterns, it can be useful to explore the data visually. With different colors and sizes of the data points, three-dimensional images that change over time, and additional tricks, six or more dimensions can be displayed on the screen.
Dimensionality Reduction
However, such visualizations are not suitable for all data sets and are can also be hard to understand. Parameterization or dimensionality reduction of the data can help to simplify the data without loss of significant information. The simplified data set can then be explored again.
Clustering and Classification
Clustering algorithms can be used to automatically group the data into different classes. If training data is available, machine learning algorithms can also be used.
Contact
For consulting on Big Data projects and development of individual solutions, 256.systems can be contacted.