Ahmet Cosar, Ee-Peng Lim, Jaideep SrivastavaDepartmentof Computer ScienceUniversity of MinnesotaMinneapolis, MN 55455AbstractIn certain database applications such as deductivedatabases, batch query processing, and recursive queryprocessing etc., a single query can be transformed into aset ofclosely related database queries. Great benefits canbe obtained by executing a group of related queries all to-gether in a single unijied multi-plan instead of executingeach query separately. In order to achieve this, MultipleQuery Optimization (MQO) identifies common task(s)(e.g. common subezpressions, joins, etc.) among a setof query plans and creates a single unified plan (multi-plan) which can be executed to obtain the required out-puts forall queries at once. In this paper, anew heuris-tic function (f=), dynamic query ordering heuristics,and Depth-First Branch-and-Bound (DFBB) are de-jined and experimentally evaluated, and compared withexisting methods which use A* and static query order-ing. Our experiments show that all three of f., DFBB,and dynamic query ordering help to improve the perfor-mance of our h4Q0 algorithm.1 IntroductionThe objective of multiple query optimization (MQO)is to exploit the benefits of sharing common tasks inthe access plans for a group of queries. In certaindatabase applications, e.g. deductive query processing,batch query processing and recursive query processing,often a group of queries are submitted together to theDBMS for execution. The traditional approach of pro-cessing queries one at a time will be inefficient espe-cially when there is a high number of queries sharingPermission to copy without fee all or part of this materisi isgmnted provided that the copies m. not mad. or distributed fordirect commercial advantage, tha ACM copyright notica and thatitla of tha publication and ita data appaar, and
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