Data warehousing is a huge collection of data sources meant for handling strategic decisions with historical and current data. The recent trend in information technology market is big data analytics. Huge amounts of data available with the companies but the proper information as per the requirements is still a challenging issue. The current article is dealing with the implementation of iceberg queries, slowly changing dimensions and uncertain data processing. The idea behind the integration of these aspects is processing subsets of the data from the huge amounts of the data. In case of iceberg querying the main theme is aggregate query processing such as count, sum, average, maximum and minimum kind of calculations. In most of the cases the analysis of the data and comparison of the performance aspects between aggregations only, so usage of iceberg querying will improve the processing speed of the data warehousing. The second component we are considering to process data warehousing is slowly changing dimensions. The dimension tables are the basic things of the data warehousing construction, usually dimensions will change slowly not frequently. If we are able to track the changes done to the dimensions such as maintenance of historical and current data, with the tracking of data we can get the subset of data which got modified or which we need to process, which will greatly reduce the number of records to process. The third component we are considering is uncertain data processing. The data which is not having any structure and no information about the format is known as uncertain data, now a days the data population of the data like reviews, likes or shares in social media, MS response all are recorded in uncertain format. The processing of uncertain data with softest computing will give the identification of missing data, parameterization aspects are possible.

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Slowly Changing Dimensions Ice Berg Queries Uncertain Data Bitmap Indexing Soft Set Computing