It is not possible to conduct scientific research on how to analyze big data using traditional methods, because it involves too many problems. Traditional methods of how to analyze big data are either too time-consuming or too inflexible to suit today’s fast-changing business world. The most important aspect of big data is to deal with the problems arising out of the huge amount of data that is accumulated every day. Traditional approaches to how to analyze big data generally make use of specific procedures based on traditional statistical analysis, such as the Meta-stats approach. There are many problems in establishing a connection between descriptive variables and the real world.
Example 1: How SAM Data Analysis Works presented two empirical data sets to illustrate how to apply the SAM method to real data from the WVS website (WVS, data source). The first empirical data set was obtained from the WVS website using the citation analysis tool from SAM. This example is also useful to demonstrate how to analyze big data using traditional statistical methods. For this project, we used the Generalized Estimator (GE), a statistical method widely used in the social sciences.
In this project, the geographical information systems (GIS) data was integrated with the rest of the data sources to provide regional and national level summary statistics. We used the Geographic Information System (GIS) as a database to support how to analyze big data analysis. GIS consists of three major components: data sources, information processing and display systems. Due to its geographical location, geographical data from GIS cannot be easily accessed by traditional statistical analysis.
In this project, we used the Google Maps application as one of the data sources. Google Maps had an interface that allows users to browse through various types of data sets and map locations. To analyze this data, we used a linear model in R. We used the natural logarithm of the spatial frequency spectrum as a means to measure the frequency spectrum of data points. The resulting function is a vectorial function which represents the data points. This function can be used as a function of time or it can be used as a function of geographic distance.
Using logarithms, we found that the relationship between geographic location and frequency of data points is non-zero, as expected. We then applied logistic regression to this data set to estimate the rate of occurrence of a point. This technique is used when there are more than one data source for a point. Using this technique, we found that the spatial frequency of data points tends to be positive correlated with distance. It is also likely that high frequencies are associated with small points that are closest to the origin.
Frequencies and spatial patterns may be used as a means of exploratory data analysis. Frequencies can represent time since the sample has been in existence. Spatial patterns, on the other hand, reflect relationships among samples over time. A frequent data set shows a high level of temporal aggregation. Some data analysis techniques can even treat these two concepts as independent, allowing them to be compared over time. Frequencies may be linearly correlated with distance, while spatial representations tend to be non-linearly correlated.
When using analytical techniques for frequency distributions and geometric visualization, we observed that there tends to be a high correspondence in the magnitude of the largest data sets. When the largest data set is plotted as a function of time, there tends to be a U shaped curve. Analysis of time series of data sets can reveal the trend of larger data points over a period of time. These trends can also be used as a way of describing relationships among samples over time, a common practice in statistics.
In summary, we have presented some of the basic methods on how to analyze big data. We looked at how to exploit the large volume of data, how to create or access big data analysis tools, how to combine data forms for easier visual scrutiny, how to visualize data sets for easier analysis, how to visualize frequency curves and spatial patterns, and how to investigate relationships among samples over a period of time. This basic presentation has presented a simple conceptual model on how to analyze big data. We also introduced the three main analytic techniques: frequency and spatial transformations, hierarchical supervised learning, and greedy finite difference models. We hope that this article has helped you learn how to analyze big data sets using the analytic techniques that you are already familiar with.