Online aggregation with tight error bounds in dynamic environments

OLAP is a category of database technology that allows analysts to gain insight into the aggregation of data by enabling them to gain access to a variety of different views of the information contained in a database. It is very important to provide analysts with guaranteed error bounds for approximate results to aggregation queries in enterprise applications such as decision support systems. We propose a general method of providing tight error bounds for approximate results to OLAP range-sum queries. We perform an extensive experiment on diverse data sets and examine the effectiveness of the proposed method for various data cube dimensions and query sizes.