  Ports, warehouses and courier services have to decide online how an arriving
task is to be served in order that cost is minimized (or profit maximized).
These operators have a wealth of historical data on task assignments; can these
data be mined for knowledge or rules that can help the decision-making?
  MOO is a novel application of data mining to online optimization. The idea is
to mine (logged) expert decisions or the offline optimum for rules that can be
used for online decisions. It requires little knowledge about the task
distribution and cost structure, and is applicable to a wide range of problems.
  This paper presents a feasibility study of the methodology for the well-known
k-server problem. Experiments with synthetic data show that optimization can be
recast as classification of the optimum decisions; the resulting heuristic can
achieve the optimum for strong request patterns, consistently outperforms other
heuristics for weak patterns, and is robust despite changes in cost model.
