Expressing complex OLAP queries involving nested expressions using normal group-by, aggregation, and joins can be extremely difficult. We propose a technique that translates nested query expressions into an algebra extended with a complex OLAP operator. The GMDJ is an operator with a simple and easy to optimize implementation that is particularly useful for OLAP computations because the size of intermediate results is bound by the size of the base-value argument relation. We show that all SQL subqueries can be expressed in the algebra using GMDJs. This not only makes it easy to integrate subqueries into any query engine that supports GMDJs, but also gives access to a broad range of OLAP optimization strategies for evaluating subqueries. We discuss the coalescing of GMDJs and the completion of tuples, two GMDJ optimizations that are particularly relevant to subquery processing. Our experimental results demonstrate the validity and efficiency of our approach for computing subquery expressions.

The success of Internet applications has led to an explosive growth in the demand for bandwidth from ISPs. Managing an IP network includes complex data analysis that can often be expressed as OLAP queries. Current day OLAP tools assume the availability of the detailed data in a centralized warehouse. However, the inherently distributed nature of the data collection (e.g., flow-level traffic statistics are gathered at network routers) and the huge amount of data extracted at each collection point (of the order of several gigabytes per day for large IP networks) makes such an approach highly impractical. The natural solution to this problem is to maintain a distributed data warehouse, consisting of multiple local data warehouses (sites) adjacent to the collection points, together with a coordinator. In order for such a solution to make sense, we need a technology for distributed processing of complex OLAP queries. We have developed the Skalla system for this task. We conducted an experimental study of the Skalla evaluation scheme using TPC(R) data.

OLAP queries (i.e. group-by or cube-by queries with aggregation) have proven to be valuable for data analysis and exploration. Many decision support applications need very complex OLAP queries, requiring a fine degree of control over both the group definition and the aggregates that are computed. For example, suppose that the user has access to a data cube whose measure attribute is Sum(Sales). Then the user might wish to compute the sum of sales in New York and the sum of sales in California for those data cube entries in which Sum(Sales)>$1,000,000. This type of complex OLAP query is often difficult to express and difficult to optimize using standard relational operators (including standard aggregation operators). In this paper, we propose the MD-join operator for complex OLAP queries. The MD-join provides a clean separation between group definition and aggregate computation, allowing great flexibility in the expression of OLAP queries. In addition, the MD-join has a simple and easily optimizable implementation, while the equivalent relational algebra expression is often complex and difficult to optimize. We present several algebraic transformations that allow relational algebra queries that include MD-joins to be optimized.