We define a framework for static optimization of sliding window conjunctive queries over infinite streams. When computational resources are sufficient, we propose that the goal of optimization should be to find an execution plan that minimizes resource usage within the available resource constraints. When resources are insufficient, on the other hand, we propose that the goal should be to find an execution plan that sheds some of the input load (by randomly dropping tuples) to keep resource

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... e within bounds while maximizing the output rate. An intuitive approach to load shedding suggests starting with the plan that would be optimal if resources were sufficient and adding "drop boxes" to this plan. We find this to be often times suboptimal -in many instances the optimal partial answer plan results from adding drop boxes to plans that are not optimal in the unlimited resource case. In view of this, we use our framework to investigate an approach to optimization that unifies the placement of drop boxes and the choice of the query plan from which to drop tuples. The effectiveness of our optimizer is experimentally validated and the results show the promise of this approach. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee.