Approximate answers to OLAP queries on streaming data warehouses

Abstract

We study streaming data for a data warehouse, which combines different sources. We consider the relative answers to OLAP queries on a schema, as distributions with the L1 distance and approximate the answers without storing the entire data warehouse. We first study how to sample each source and combine the samples to approximate any OLAP query. We then consider a streaming context, where a data warehouse is built by streams of different sources. We first show a lower bound on the size of the memory necessary to approximate queries and then consider a statistical hypothesis where some attributes determine fixed distributions of the measure. We use the sampling methods to learn the statistical model and approximate OLAP queries. In this case, we approximate OLAP queries with a finite memory. We apply the method to a dataset which simulates the data of sensors, which provide weather parameters over time and locations from different sources.