By Sbihi A.
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Extra resources for A best first search exact algorithm for the Multiple-choice Multidimensional Knapsack Problem
Briefly, the eddy invokes a reoptimizer every K tuples, where K is a system parameter called the batching factor. 2). The resulting plan is encoded into a routing table, and the next K tuples are routed according to that plan. This delineation between Planning and Actuation results in negligible routing overhead for reasonable batching factors (K = 100) . 2. Routing Policy based on A-Greedy: As observed by Babu et al.
The eddy can adapt to changing data or operator characteristics by simply changing the order in which the tuples are routed through these operators. Note that the operators themselves must be chosen in advance (this was somewhat relaxed by a latter approach called SteMs that we discuss in Section 6). These operator choices dictate, to a large degree, the plans among which the eddy can adapt. 1 New Operators 31 ities and costs). On the other hand, blocking operators like sort-merge operators are not very suitable since they do not produce output before consuming the input relations in their entirety.
One option is to use the set of base relations that a tuple contains and the operators it has already been routed through, collectively called tuple signature, as the lineage. However, for efficient storage and lookups, compact representations of this information are typically used instead. For instance, the original eddies proposal advocated attaching two bitsets to each tuple, called done and ready, that respectively encode the information about the operators that the tuple has already been through, and the operators that the tuple can be validly routed to next .