DATA UNCERTAINTY AND DATA QUALITY PROCESSING WITH APACHE SPARK
International Journal of Emerging Trends in Engineering Research · 2020
Spatiotemporal analysis refers to the processing of spatiotemporal data and discovering knowledge, patterns from it.A practical example is identifying different city's histories and earthquakes.This data is collecting from the source where it is being referred with countries their regions, population, speaking habits from time to time, evolutions from time to time in the cities and also the developments happened constitutes space and time, and this data are often highly noisy and sparse consequently extract useful knowledge from such noisy and sparse data is a challenging task.Another challenging problem is integrating multiplemodel data from three different sectors like countries, states, cities and their socio-economic development.There are different factors involved in ST data such as location, time, and text, these heterogeneous factors are highly coupled to reflect people's activities in a collective way, yet they have totally different modes, sizes, and allocations.How to effectively integrate those different data types for knowledge acquisition remains another challenge with the reference data structure is still unsolved.Furthermore, still, its time-consuming process to store a huge volume of spatiotemporal data which are rapidly accumulated and processing queries on the vast amount of spatiotemporal data is highly difficult in terms of space or time complexity.This paper discusses about the different sources around us to navigate and work along with the spatio data and explore more into the data set.The methods used in the analysis are EDA (Exploratory Data Analysis), Data deficiency, Data uncertainty so on.The architecture is been used in order to make a perfect their representation of the models.Lastly a model is also proposed as to work with the dataset and work more on the model with a proper consistent method.