COVID Interactive Query Answering Using a Data Cube

Decision makers require accurate summarized information to support their opinions. Usually, this data owes missing and erroneous values to be detected and cleaned using a time-consuming process. Diverse alternatives and tools have been proposed and developed to reduce the time used to prepare the data, such as data warehouse, and data cubes obtained from an integrated multidimensional dataset. Recently, big data tools have represented an alternative to improve the performance of data preparation. This paper compares traditional data preparation using relational databases, data cubes, and the usage of big data. The results show data volume and time reduction with data cube and big data approaches. One important factor is the knowledge of the required answered queries, which allows the identification of the data of interest. COVID-19 data is employed to obtain data volumes, time measures, and comparisons.