This research is concerned with issues such as which data to aggregate, how to store the aggregate data, and when to pre-aggregate the derived data. These issues are also considered as part of the view materialization problem in data warehouses. The basic tradeoffs that need to be considered for when to aggregate are time and space. Time is required to create reports from either input data or aggregate data, create and store the aggregate data in advance, and update both types of data. Frequency of known and anticipated queries and updates need to be considered here.
Space is also important, in fact critical, to store any pre-calculated aggregates.
It is very difficult, if not impossible, to anticipate all the aggregate queries that end users may want to use. The most flexible systems allow the user to iteratively formulate queries based on previous results. This kind of analysis of pre-aggregation is the specific focus of this research. We will first evaluate the currently proposed strategies for the selection of which views to materialize, including recent work on the data cube and its variations. Evaluation criteria such as data volume due to aggregate functions, user workloads, computing platform, update costs, and storage limits will be used to compare strategies.