  In optimizing queries, solutions based on AND/OR DAG can generate all
possible join orderings and select placements before searching for optimal
query execution strategy. But as the number of joins and selection conditions
increase, the space and time complexity to generate optimal query plan
increases exponentially. In this paper, we use join graph for a relational
database schema to either pre-compute all possible join orderings that can be
executed and store it as a join DAG or, extract joins in the queries to
incrementally build a history join DAG as and when the queries are executed.
The select conditions in the queries are appropriately placed in the retrieved
join DAG (or, history join DAG) to generate optimal query execution strategy.
We experimentally evaluate our query optimization technique on TPC-D/H query
sets to show their effectiveness over AND/OR DAG query optimization strategy.
Finally, we illustrate how our technique can be used for efficient multiple
query optimization and selection of materialized views in data warehousing
environments.
