  In this paper, we design the first streaming algorithms for the problem of
multitasking scheduling on parallel machines with shared processing. In one
pass, our streaming approximation schemes can provide an approximate value of
the optimal makespan. If the jobs can be read in two passes, the algorithm can
find the schedule with the approximate value. This work not only provides an
algorithmic big data solution for the studied problem, but also gives an
insight into the design of streaming algorithms for other problems in the area
of scheduling.
