TY - JOUR
T1 - Algorithms for distributional and adversarial pipelined filter ordering problems
JF - ACM Trans. Algorithms
Y1 - 2009
A1 - Condon,Anne
A1 - Deshpande, Amol
A1 - Hellerstein,Lisa
A1 - Wu,Ning
KW - flow algorithms
KW - Pipelined filter ordering
KW - Query optimization
KW - selection ordering
AB - Pipelined filter ordering is a central problem in database query optimization. The problem is to determine the optimal order in which to apply a given set of commutative filters (predicates) to a set of elements (the tuples of a relation), so as to find, as efficiently as possible, the tuples that satisfy all of the filters. Optimization of pipelined filter ordering has recently received renewed attention in the context of environments such as the Web, continuous high-speed data streams, and sensor networks. Pipelined filter ordering problems are also studied in areas such as fault detection and machine learning under names such as learning with attribute costs, minimum-sum set cover, and satisficing search. We present algorithms for two natural extensions of the classical pipelined filter ordering problem: (1) a distributional-type problem where the filters run in parallel and the goal is to maximize throughput, and (2) an adversarial-type problem where the goal is to minimize the expected value of multiplicative regret. We present two related algorithms for solving (1), both running in time O(n2), which improve on the O(n3 log n) algorithm of Kodialam. We use techniques from our algorithms for (1) to obtain an algorithm for (2).
VL - 5
SN - 1549-6325
UR - http://doi.acm.org/10.1145/1497290.1497300
CP - 2
M3 - 10.1145/1497290.1497300
ER -